New Zealand As a Model for Vector Borne Disease Emergence

Master´s Thesis Ecosystems, Governance and Globalisation Master´s programme 2007/09

New Zealand as a model for vector borne disease emergence

Effects of social and environmental factors on dengue

Malin Nordwall New Zealand as a model for vector borne disease emergence: effects of social and environmental factors on dengue

Malin Nordwall Supervisors: Dr. Elisabet Lindgren & Dr. Mary McIntyre

Prologue

Until recently, New Zealand’s geographic isolation has allowed a very unusual fauna to evolve in the near absence of native land mammals (Crump et al. 2001). About 80 million years ago New Zealand was separated from the super-continent Gondwana and the links that had earlier enabled mass migrations (Stevens 1988).

The long isolation allowed for an extensive in situ evolution resulting in an almost 100% endemism at a species level (Daugherty et al. 1993), 80% of the plants (van Bunnik et al. 2007) and 35% of its bird species can only be found in New Zealand (Daugherty et al. 1993). The absence of mammalian predators has allowed traits such as flightlessness, no defensive behaviours, gigantism and low productive rates to appear (Atkinson and Cameron 1993).

The first colonising Polynesians, ancestors of Maori, arrived about 1000 years ago (McCulloch 1988, Crump et al. 2001), this started off the first human induced extinction wave on New Zealand, exterminating the Haast’s eagle and the moas amongst others. The second wave was set off 200 years ago, when the first European settlers came (Diamond 1990).

The extensive human disturbance on the country’s ecosystems has resulted in indigenous forest being reduced from 82% to 21% of the land surface area with large tracts of remaining mainly at higher altitude, natural freshwater wetlands have been reduced by 90%, more than 50% of the flora consists of non-native species (Department of Conservation 2000) and of 31 species of land mammals only two insectivorous bat species are native (King 1990).

The native fauna of New Zealand never included animal hosts for human pathogen, but the introduction of exotic mammals gave an opportunity for zoonotic disease to establish. Emerging zoonoses has been successfully controlled and excluded from New Zealand by a strict quarantine system. However, New Zealand has been argued to have deferred impacts from infectious diseases that are emerging in other parts of the world (Crump et al. 2001).

It is New Zealand’s isolation that makes it such an interesting place, not only to study the flora and fauna, but also for studying the emergence of infectious diseases.

Acknowledgements

I would like to thank my supervisors Dr. Elisabet Lindgren and Dr. Mary McIntyre for their support and guidance. I would also like to express my gratitude to Mary for making it possible for me to carry out this research together with Wellington School of Medicine and Health Science, University of Otago, helping me orientating myself in New Zealand and making me feel welcome.

Last but not least, I would thank Dr. Simon Hales for his help and expertise on the subject and Dr. James Stanley for guiding me through a statistical nightmare.

Abstract

The geographic distribution of dengue fever has increased worldwide in recent years and is at present the most widespread vector borne viral disease in the world (Halstead 2002). Because of its rapid spread and increasing seriousness of its complications it is considered to be the most troubling vector borne disease (Wilcox and Colwell 2005, Phillips 2008). Dengue fever is the one vector borne disease that poses the greatest threat to New Zealand. Imported cases are being reported in ever increasing numbers and all the components for a mosquito borne disease cycle is already present (Ministry of Health 1997). Furthermore, New Zealand’s geographic isolation makes it a unique location for studying the emergence of vector borne diseases, such as dengue.

The objective of this thesis was - by using case studies on dengue fever on a global scale and the potential emergence of the disease in isolated New Zealand as examples – to explore the interlinkages between global changes (climate change and rapid urbanisation), globalisation (rapid travel and trade), and their local impacts on vulnerability and health (i.e. changes in local climate, travel and trade patterns and demographic changes that affect emergence and transmission of disease). This was done by 1) reviewing the ecological and environmental conditions necessary for dengue transmission; 2) examining key social and environmental factors contributing to the recent global increase in dengue fever and dengue hemorrhagic fever (DHF) and 3) drawing projections to 2070 in order to build future scenarios for epidemic dengue risks in New Zealand. Regression analysis were used to analyse 16 years of area specific dengue rates from 232 geographical areas in relation to key social and environmental factors proposed to contribute to dengue emergence. The results were tested on the mainland of New Zealand in order to build future scenarios for epidemic dengue risks in New Zealand for 2070.

The outcome from the regression analysis proved to have a good ability to predict dengue rates based on national characteristics and it predicted a nearly fourfold increase in risk of epidemic for New Zealand’s North Island based on climate projections for 2070. The projected increase in population density however, had much less of an effect on the perceived risk than the projected climate change despite an estimated increase of 33% in population density.

This is the first study that makes an attempt to measure the relative importance of different social and environmental variables proposed to contribute in the recent global increase in dengue.

List of Acronyms

DHF Dengue Hemorrhagic Fever EID Emerging Infectious Diseases EIP Extrinsic Incubation Period GDP Gross Domestic Product IPCC Intergovernmental Panel on Climate Change IPO Interdecadal Pacific Oscillation MA Millennium Ecosystem Assessment NTD Neglected Tropical Diseases SOI Southern Oscillation Index WHO World Health Organization YoY Year on Year

Table of Contents

Table of Contents ...... 7 1. Introduction ...... 9 1.1 Case studies, Global assessment and New Zealand focus: Problem statement ...... 11 1.1.1 Risk of dengue establishment in New Zealand ...... 12 1.2 Objectives of the case studies ...... 12 2. Theoretical framework ...... 13 2.1 Mechanisms for dengue emergence ...... 16 2.1.1 Climate change ...... 17 2.1.2 Population growth and urbanisation ...... 18 2.1.3 Travel and trade ...... 19 2.1.4 Socio-economic factors ...... 20 2.1.5 Land use change ...... 21 3. Dengue overview ...... 23 3.1 The dengue transmission cycle ...... 24 3.2 Dengue vector ecology ...... 25 3.2.1 Aedes albopictus – the Asian tiger mosquito ...... 25 3.2.2 Aedes aegypti – the primary dengue vector ...... 26 3.2.3 Aedes polynesiensis – the Polynesian dengue vector ...... 27 3.2.4 Aedes notoscriptus – an urban dengue vector in New Zealand ...... 27 3.3 Dengue flavivirus ...... 28 4. Potential introduction of dengue fever into New Zealand: risk assessment background ..... 29 4.2 Ecological features ...... 30 4.2 Socio-economic features ...... 31 4.3 Dengue cases and intercepted dengue vectors in New Zealand ...... 34 4.4 Biosecurity - surveillance and exclusion programmes ...... 36 4.5 Previous risk assessments for epidemic dengue in New Zealand ...... 38 4.5.1 The Hotspots system - spatial analysis of dengue fever risk to New Zealand ...... 39 5. Case study ...... 42 5.1 Case study part I: The global context ...... 42 5.1.1 Literature ...... 42 5.1.2 Data ...... 43 5.1.3 The regression analysis ...... 46 7

5.2 Case study part II: The New Zealand context ...... 46 5.2.1 Literature and data ...... 46 5.2.2 Projections ...... 46 5.2.3 Scenarios ...... 47 6. Results ...... 48 6.1Results part I: The global context ...... 48 6.2 Results part II: The New Zealand context ...... 49 6.2.1Vapour pressure projections...... 49 6.2.2Scenario I: Increase in vapour pressure ...... 49 6.2.3Population density projections ...... 50 6.2.4 Scenario II: Increase in population density ...... 51 7. Discussion ...... 51 7.1 Recommendations and lessons to be learned ...... 56 7.2 Limitation of the study ...... 58 8. Conclusions ...... 59 References ...... 61 Appendix ...... 69

1. Introduction

Humans have over the last 50 years changed the earth’s ecosystems faster and more extensively than ever before. The intensification of agriculture has allowed for the global population to double in just 40 years, with half of us living in urban areas (Nelson 2005). A third of the earth’s land area is used for agriculture and built-up areas and as humans divert more of the earth’s resources to support themselves, the planet is becoming increasingly homogenised (Walker and Salt 2006:3). Globalisation is making once distant people closer and more connected as the amount of people and goods that are being transported around the world increases (Walker and Salt 2006). Our activities are affecting the provision of vital ecosystem services and are affecting the local and global climate (IPCC 2007).

The World Health Organization (WHO 1986) stated in the “Ottawa Charter for Health Promotion” that “a stable ecosystem and sustainable resources” are a prerequisite for health. More recently the Millennium Ecosystem Assessment defined the “regulation of human infectious disease agents” as an ecosystem service (Patz and Confalonieri 2005). This acknowledges the relationship between human health and ecological sustainability, signifying that human health is dependent on both the present and future management of ecosystem and their services.

Within disease emergence this study will focus on the spread of dengue and the mechanisms for dengue emergence. These mechanisms include:

• changes in the availability of vector breeding sites due to habitat alteration and fragmentation; • genetic changes in vectors (e.g., pesticide resistance) and pathogens (e.g., antibiotic resistance) (Patz and Confalonieri 2005); • changes in geography and seasonality of the vectors due to climate changes (McMichael 2006); • movement of organisms by travel and trade (Sutherst 2004).

These mechanisms often act in combination resulting in synergistic or non-linear effects on disease transmission. Thus not only do humans have impacts on the environment but human- induced environmental change may significantly affect human well-being (Patz and Confalonieri 2005). To fully appreciate the nature of dengue it is essential to understand the 9 biology of the pathogen and vector as well as the environmental and social factors affecting them (Patz and Confalonieri 2005). The theory of biocomplexity has been applied to get a holistic view over these environmental and social factors affecting dengue at different levels of organisation (Wilcox and Colwell 2005).

The objective of this thesis was - by using case studies on dengue fever on a global scale and the potential emergence of the disease in isolated New Zealand as examples – to explore the interlinkages between global changes (climate change and rapid urbanisation), globalisation (rapid travel and trade), and their local impacts on vulnerability and health (i.e., changes in local climate, travel and trade patterns, and demographic changes that affect emergence and transmission of disease). The case study parts of this study will analyse the key social and environmental factors that have been proposed to contribute to the recent global increase in dengue fever and dengue hemorrhagic fever (DHF). This is done by 1) reviewing the ecological and environmental conditions necessary for dengue transmission; 2) examining key social and environmental factors contributing to the recent global increase in dengue fever and dengue hemorrhagic fever (DHF) and 3) drawing projections to 2070 in order to build future scenarios for epidemic dengue risks in New Zealand. Dengue rates over 16 years from 232 geographical areas with endemic dengue will be analyzed in relation to key social and environmental factors proposed to contribute to dengue emergence. The goal is to increase understanding of the key determinants for dengue establishment and the creation of a framework for more reliable risk assessment.

The results will then be tested on the mainland of New Zealand, which has all the components of a mosquito-borne disease cycle present but does not yet have local transmission of the dengue virus. Based on the regression results analysis the features which, according to the analysis, may influence local transmission of the dengue virus will be examined. Projections to year 2070 will then be performed in order to build future scenarios for epidemic dengue risks in New Zealand. These scenarios can then be used as an envisioning tool for considering implications for governance and policy development. The country’s geographic isolation and its recent human settlement, make New Zealand a unique model for studying the emergence of vector borne diseases.

1.1 Case studies, Global assessment and New Zealand focus: Problem statement Dengue fever is an emerging disease that has in recent decades been increasing in prevalence worldwide and is today the most widespread arthropod borne viral disease in the world (Halstead 2002). This is due to disease-specific ecological and epidemiologic factors (i.e. humans being reservoirs and the urban adaptation of the vectors) and the increasing travel and trade world-wide that allow the pathogen (through infected humans and vectors) and the vector and its eggs and larvae to become established in new areas if local conditions are favourable (see below). Dengue is considered to be the most troubling vector borne disease globally because the rapid spread and the seriousness of its complications (DHF) (Wilcox and Colwell 2005, Phillips 2008). Approximately 2.5 billion of the world’s population is at risk and an estimated 50 to 100 million dengue infections occur each year (Gubler 1998). There are 500 000 cases of DHF annually and 22 000 deaths, mostly among children. In effect, dengue is the main cause of morbidity and mortality in children of Southeast Asia (McBride and Bielefeldt-Ohmann 2000, Gubler 2002). Dengue is now endemic in over 100 countries in comparison to only 9 countries before 19701. The rapid spread of dengue is due to the geographic expansion of the dengue virus and vectors, mainly Aedes aegypti and Aedes albopictus (Rodhain and Rosen 1998).

Dengue, as other emerging infectious diseases (EID), has not only a significant impact on public health but also on the global economy. For example, the yearly cost of DHF in Thailand was estimated to be, depending on epidemic activity, somewhere between 31.5 and 51.5 million US dollar. This cost adds to the social impact of dengue since almost half of these costs are paid by the patients (Gubler 2002).

Key determinants for dengue emergence are proposed to be different environmental and socio-economic factors (Binder et al. 1999, Morens et al. 2004). To date there has only been one study analysing linkages between such factors by Jones et al. (2008), and this indicated a significant correlation between all three sets of factors in the origin of EIDs, but no correlation of vector borne disease events with the ecological or environmental variables.

1 World Health Organization (WHO) 2008. http://www.who.int/mediacentre/factsheets/fs117/en/, 2008-07-28.

1.1.1 Risk of dengue establishment in New Zealand No indigenous cases of dengue have been identified within New Zealand even though the potential dengue vector Aedes notoscriptus is the second most abundant mosquito species in the North Island (Derraik and Slaney 2007). Dengue fever is thought to be the one mosquito borne disease that poses the greatest threat to New Zealand (Ministry of Health 1997) and imported cases are being reported in increasing numbers. In 2007 New Zealand had 114 cases of dengue, their highest number recorded to date2. The increase in infected travellers coming to New Zealand, combined with warmer and wetter climate predicted for the future3 enhances the risk of dengue establishment. New Zealanders are also more susceptible to dengue since they are non-immune to the disease and also inexperienced with mosquito-borne diseases (Ministry of Health 1997).

According to Derraik (2004) there have been 171 interceptions (to prevent something from proceeding or arriving) of exotic mosquitoes in New Zealand between 1929 and 2004. The main mode of invasion for the known dengue vectors Aedes aegypti, Aedes albopictus and Aedes polynesiensis has been ships transporting used tyres, used cars and used machinery shipped from Japan to Auckland (Derraik 2004). Derraik (2004) believes that the Asian tiger mosquito, A. albopictus, poses the greatest threat to New Zealand’s biosecurity. The Asian tiger mosquito is considered by the World Conservation Union (IUCN) to be the one of the world’s worst invasive alien species4 and has been intercepted 12 times in New Zealand during 1929-2004 (Derraik 2004). Laird et al. (1994) believe that cold adapted strains of the vector A. albopictus coming from Japan could establish in the country. New Zealand is under a serious threat of arboviral infection outbreaks which consecutively threatens New Zealand’s public health and economy (Derraik 2004).

1.2 Objectives of the case studies 1. To review the ecological and environmental conditions necessary for dengue transmission.

2 New Zealand Public Health Observatory (NZPHO) 2008. http://www.nzpho.org.nz/NotifiableDisease.aspx, 2008-08-10.

3 National Institute of Water & Atmospheric Research (NIWA) 2008. http://www.niwa.cri.nz/news/mr/2008/2008-05-27, 2008-08-15.

4The Global Invasive Species Database 2009. http://www.issg.org/database/species/search.asp?st=100ss&fr=1&str=&lang=EN, 2009-01-08. 12

2. To examine key social and environmental factors contributing to the recent global increase in dengue fever and dengue hemorrhagic fever. A better understanding of the key determinants for dengue/dengue hemorrhagic fever establishment would allow for a more reliable risk assessment for dengue in New Zealand. 3. To draw projections to 2070 in order to build future scenarios for epidemic dengue risks in New Zealand. These scenarios can then be used as an envisioning tool for considering implications for governance and policy development.

2. Theoretical framework

The following chapter aim to provide an explanation of how theorised social and environmental factors can result in dengue emergence. It will look at dengue emergence in a global context.

The world is becoming increasingly smaller and homogenised. Globalisation is bringing the people of the world closer and more connected (Walker and Salt 2006). The human population is projected to reach 10 billion by 2050 (Norris 2004), and today half of the human population live in urban areas (UNEP 2007). The liberalisation of international trade and more economical mass transport together with increasing wealth in developing countries is accelerating the increase in amount of material and people that are being transported around the world. There are about 700 million tourist arrivals per year and there were more than 14.5 million refugees at the end of 2100. International trade have increased fourfold from 1980 to 2000 (Sutherst 2004). The combination of population growth, resource depletion and more homogenous and human-adapted biota’s restrict options to deal with unexpected crises (Walker and Salt 2006).

Our activities are affecting the supply of ecosystem services, such as the local and global climate. Over the last 100 years (1906-2005) the global average surface temperature has increased by 0.74 [0.56 to 0.92] °C according to the Intergovernmental Panel on Climate Change (IPCC). The linear warming trend over the 50 years from 1956 to 2005 (0.13 [0.10 to 0.16] °C per decade) is nearly twice that for the 100 years from 1906 to 2005. There is today strong scientific consensus world-wide that human activities immensely contribute to the increased concentration of green-house gases in the atmosphere (IPCC 2007). For the next two decades a warming of about 0.2 °C per decade is projected. By 2090-2099 the global average surface temperature is projected to increase by 1.1 to 6.4°C resulting in an increase in the global average precipitation and water vapour concentration (IPCC 2007). 13

We are also affecting the provision of e.g. food and fresh water, frequency and magnitude of droughts and floods and disease control, all services that humans are vitally dependent upon. Changes in the supply of ecosystem services and goods have the potential to affect human well-being not only by affecting health directly, but also by influencing the economic growth rate and the occurrence and persistence of poverty (MA 2003). The perspective developed by the Millennium Ecosystem Assessment recognises “that a dynamic interaction exists between people and ecosystems, with the changing human condition serving to both directly and indirectly drive change in ecosystems and with changes in ecosystems causing changes in human well-being” (MA 2003:26). The full understanding of the dynamics of dengue emergence is at present one of the most complex scientific problems threatening human well- being. Knowledge gaps exist for dengue as for most other EIDs, and these gaps are partly due to the naive view of pathogens being disconnected from a social-ecological context and the presumption that pathogens have a linear response to changes in the environment surrounding them (Wilcox and Colwell 2005). When in reality, dengue emergence has been recognised as a cross-scale process between systems with feedback loops across space and time. Dengue vectors and transmission rates are largely affected by environmental factors, as well as demographic and social changes which will produce unpredictable nonlinear responses (Waltner-Toews 2001, Wilcox and Gubler 2005). Research for a sustainable health strategy must therefore include this new awareness of multiple scales and perspectives and the high degree of uncertainty coming from non-linear responses (Waltner-Toews 2001, Eisenberg et al. 2007).

The theory of biocomplexity allows for a more realistic view as it offers a holistic perspective over dengue emergence. It includes all the different processes that are proposed to affect, directly or indirectly, the spread and distribution of dengue and also the associated uncertainty and surprises (Waltner-Toews et al. 2004, Wilcox and Colwell 2005). It gives a theoretical framework (Figure 1) that illustrates dengue emergence by connecting evolutionary- and emergence processes operating on a molecular level to that of human driven environmental and social changes (Wilcox and Colwell 2005).

Environmental change, such as population growth, land use change and especially travel and trade play important roles in dengue emergence. Population growth will lead to crowding and expanding habitats for Aedes mosquitoes (Norris 2004, Sutherst 2004, Patz and Confalonieri 2005), while land use changes have the potential to result in loss of important ecosystems

14 services, such as climate regulation and disease control (Hales et al. 2002, Sutherst 2004, Patz and Confalonieri 2005, McMichael 2006) and increased travel and trade can transport the pathogen and its vectors to new areas (Daszak et al. 2000, Patz et al. 2004). These environmental changes on a landscape level will also produce changes in their communities and consequentially in their pathogen-, vector- and host (human) populations (Wilcox and Colwell 2005). Changes in host pathogen dynamics will assist novelty, increased epidemic activity caused by several serotypes will allow for genetic exchange among pathogens, and can potentially result in the emergence of new genotypes with better virulence and/or epidemic potential (Gubler 2002). Socio-economic factors and climate change and their interactions with environmental change, can further contribute to dengue emergence (Wilcox and Colwell 2005).

Socio- Environmental Climate economic change factors

Land use Trade and Population change travel growth • Increased • Weakened public • De/reforestati • Increased temperature, • Increased health on movements of precipitation crowding infrastructure Irrigation humans, and vapour Uncontrolled • • • Limited disease vectors and pressure urbanisation • Decreased control capacity in pathogens • Prolonged • Expanding • Poverty pathogen • Travellers seasons habitats for regulation contract • Latitudinal and • Poor housing Aedes dengue more altitudinal shifts • Unsafe water mosquitoes • Changes in Landscape level local climate easily in habitat distribution • Civil unrest and • New strains of • Increased floods war the dengue and droughts virus to new areas

• Assuming pathogens Ecological-Evolutionary Dynamics • Change in are disconnected from Opportunistic habitat expansions/Ecological release vector a social-ecological abundance and context and have a distribution Vector domestication Epidemics caused by several linear response to virus serotypes environmental change • Favouring command and control programs Natural community level

• Poor vector and Host-Pathogen Dynamics • Changes in vector- pathogen monitoring Emergences Processes pathogen life cycles and surveillance and abundance • Changes in frequency • Lack of linkage with • Increased epidemic activity and genetic exchange and types of host- Population level clinical surveillance • Breaching of pathogen and vector persistence pathogen contact

Dengue Emergence

Human population

Figure 1. Theoretical framework illustrating the mechanism of dengue emergence and how the human poulation have an impact on the environment that will ultimately have a significant affect on human well-being. Source: built on Wilcox and Colwell’s blueprint of disease emergence in Wilcox, B.A. and R.R. Colwell. 2005. Emerging and reemerging infectious diseases: biocomplexity as an interdisciplinary paradigm. EcoHealth 2: 244–257.

The processes behind dengue emergence and how humans have an impact on these processes and sometimes are fully responsible for them will be discussed in depth below.

2.1 Mechanisms for dengue emergence There are a number of factors contributing to the rapid increase in dengue and DHF over the last 40 years. Not all of them are well understood, but it is clear that climate, urbanisation,

16 population growth, crowding, international travel and increased transportation had a significant impact on the geography and increased incidence of dengue and DHF (Khan et al. 2001, Gubler 2002). Synergistic or non-linear effects on disease transmission often appear as a result of these mechanisms acting in combination (Patz and Confalonieri 2005).

2.1.1 Climate change Temperature and rainfall influence the dengue transmission cycle and thereby determine the distribution of dengue. Temperature functions as a regulatory force and rainfall provides breeding ground for mosquitoes (Githeko et al. 2000). Dengue Flavivirus do not tend to survive below 12-13 C° and reach their maximum potential at 40 C°. In many parts of the world Aedes mosquitoes have adapted to live indoors. However, Aedes aegypti is found mainly in regions where the mean temperature of the coldest month stays above 10°C5. The cold-adapted strain of Aedes albopictus that is now prevalent in Japan, North and Latin America, and Europe can survive in areas with monthly mean temperatures as low as -2°C 6. The vector’s survival will decline when the temperature exceeds 40 C° (Martens et al. 1997).

It will be in the extremes of the temperature range that climate change will have the greatest impact on dengue transmission (Githeko et al. 2000). Warming in the upper range can stop transmission while warming in the lower end has very significant and non-linear result on the extrinsic incubation period (EIP), which is the time the pathogen needs to become infective within the vector (Watts et al. 1987). The expected water temperature rise would make it possible for mosquito larvae to mature much faster, which would increase the abundance of offspring produced during the transmission period and thereby increase the density of vectors. The adult female Aedes mosquito would also feed more frequently and digest faster in a warmer climate and consequently increase the transmission intensity (Martens et al. 1997, Githeko et al. 2000).

Global warming will lead to changing precipitation patterns (IPCC 2007) which will have an influence on vector breeding sites. In the aquatic stages in the mosquito life cycle can rainfall, if moderate, act in favour for the mosquito larvae (Githeko et al. 2000). More rainfall can

5 World Health Organization 2009. http://gamapserver.who.int/mapLibrary/Files/Maps/Global_DengueTransmission_ITHRiskMap.png , 2009-07- 24 6 European Centre for Disease Prevention and Control 2009. http://ecdc.europa.eu/en/files/pdf/Publications/0905_TER_Development_of_Aedes_albopictus_risk_maps.pdf , 2009-07-24 17 result in a prolonged season for adult mosquitoes due to increased relative humidity. An analysis of temperature, rainfall and humidity in relation to dengue incidences in the Sukhothai province in Thailand showed that dengue commonly occurs when temperature, rainfall and humidity are higher than average (Nakhapakorn and Tripathi 2005).

The annual number of dengue cases in the South Pacific between 1970 and 1995 has been linked to the Southern Oscillation Index (SOI), which refers to periodic shifts (oscillation) in the pattern of atmospheric pressure across the western and eastern sides of the Pacific Ocean south of the equator. High positive values of the SOI (indicating La Niña conditions) in the South Pacific are known for much wetter and warmer conditions than what is usual – perfect environment for breeding mosquitoes (Hales et al. 2003). Hales et al. (1996) studied the relationship between monthly reports of dengue cases in 14 Pacific Island countries and the El Nino Southern Oscillation (ENSO). Positive correlations could be found in ten countries, five of the islands showed a positive correlation between the local temperature and/or precipitation and SOI.

In another study Hales et al. (2002) showed that present geographical limits of dengue fever transmission could be modelled with an 89% accuracy based on monthly average vapour pressure (a measure of humidity, which is indirectly a measure of air temperature and precipitation) between 1961 and 1990. From the model it was estimated that the baseline population at risk in 1990 were 1.5 billion people (~30%). Their results were further applied to future climate change situations in order to make dengue fever risk projections for 2055 and 2085. If the projected increase in population was realised and vapour pressure would stay at baseline values they estimated that 3.2 billion people (34%) would be at risk in 2055 and another 3.5 billion (35%) by 2085. However, using the same projection for population growth but instead using the predicted vapour pressure changes by the HADCM2 global circulation model; 4.1 billion people (44%) would be at risk in 2055 and 5.2 billion (52%) in 2085.

2.1.2 Population growth and urbanisation Population growth will lead to intensification in urbanisation, crowding, poverty, pollution and human migration, which can affect the distribution and incidence of dengue (Norris 2004, Sutherst 2004, Patz and Confalonieri 2005). Crowded human populations that live in close association with mosquitoes often have a dramatic increase in epidemics of vector borne diseases along with a dramatic increase in mosquitoes (UNECE 2004). A study by Jones et al

(2008) found that population density was an important predictor of EID events, which support the belief that anthropogenic and demographic changes are key drivers in disease emergence. Gubler and Meltzer (1999) showed historically that the increase in dengue and DHF cases was highly correlated to human population growth. They proposed that this was due to a lack of surveillance, control programs, prevention, public health resources for research and a general complacency about vector-borne diseases.

Urbanisation plays a major role in the global emergence of dengue and DHF (Gubler 2002, UNECE 2004). Uncontrolled urbanisation often results in poverty and concentration of people without the essential infrastructure that is needed for safe distribution and storage of water and drainage of waste water (Sutherst 2004, Patz and Confalonieri 2005). Containers used to collect rainwater are ideal breeding sites for mosquitoes as are bottle caps, flower vases and swimming pools. Just about any container that can hold water is a potential breeding site (Norris 2004, Patz and Confalonieri 2005). Urban areas with insufficient water supply systems have been shown to support dengue transmission (Patz and Confalonieri 2005). A study by Nakhapakorn and Tripathi (2005) looked at the spatial statistical relationship between dengue affected areas and different classes of land cover, such as built-up areas, agricultural areas, forested areas and water bodies. They found that built-up areas to have the highest influence on the incidence of dengue.

2.1.3 Travel and trade The recent rise in dengue cases is proposed to be partly caused by tourism and trade (Staples 2007). These types of movement of humans, products and animals have the potential to spread both pathogens and their vectors all over the world (Daszak et al. 2000, Patz et al. 2004, Sutherst 2004, Patz and Confalonieri 2005), and the transposition of these has been termed “pathogen pollution” (Patz et al. 2004). Travellers can more easily contract an infectious disease, such as dengue, since it is rare in their native country and thus have no immunity to the disease (Patz et al. 2004). Travellers also act as reservoirs for dengue, transporting the pathogen from a tropical country to the rest of the world (Patz et al. 2004) and potentially increase the incidence of DHF by carrying new strains to new areas (DHF may only occur if a person that previously has been infected with the dengue virus is re-infected with another of the four types of the virus strains, see Chapter 3) (Staples 2007).

2.1.4 Socio-economic factors “Dengue is a disease of poverty. In the places where it’s really rife, typically urban shantytowns, people have got very poor services. Waste is piling up in the street. There’s no running water, so people have to collect water in vessels, which then breed mosquitoes. The people have got terrible housing, so they’re not able to protect themselves from getting bitten. And they’re living in very close proximity. It’s the perfect recipe for a huge epidemic.” (Hales in Phillips 2008).

Dengue is 1 of 15 diseases on the WHOs list of Neglected Tropical Diseases (NTD’s)7. NTD’s are the results of poverty, the worst affected are the poorest populations often living in urban slums, remote rural areas or in conflict zones8.

In a study done during a dengue epidemic in the city of São José do Rio Preto (Brazil), Costa and Natal (1998) found an association between social and economic variables, such as education level and income, and the incidence of dengue infections. Low income neighbour hoods showed to have a higher level of dengue infection than the rest of the city. Another study by Brunkard et al. (2007) in Brownsville, Texas found from evidence of past dengue infections that people with low income, poor street drainage and no air conditioning were the most likely to have been infected.

However, when looking at Singapore it is evident that a higher living standard (i.e. richer societies) does not necessarily lower the incidence of dengue. A study conducted in Singapore by Ooi et al. (2006) showed that despite intense vector control programs in the 1970’s, resulting in an 15 year period of low dengue incidence, dengue was able to resurged several fold in the 1990’s. This indicates that the dengue situation may have worsened due to the vector control program. Ooi et al. (2006) suggest that factors such as: lowered herd immunity and increased virus transmission outside the home contributed to the resurge. Lowered herd immunity gives a better change for dengue to spread regardless of low vector density. Dengue vectors are highly domesticated, they may live, breed and feed indoors and has a limited flight range. This is why women and children in Asia are the most affected since they spend the most time indoors. But, after Singapore resurge in dengue, dengue and DHF was lower in

7 World Health Organization 2009. http://www.who.int/neglected_diseases/diseases/en/, 2009-05-04 8 World Health Organization 2009. http://www.who.int/neglected_diseases/en/, 2009-05-04 20 children than in adults. This is suggesting that considerable virus transmission occurs outside (Ooi et al. 2006).

Civil unrest and war has been proposed as a potential driver of disease emergence (Patz et al. 2004). The conditions refugees comes from, where malnutrition, lack of clean water and sanitation are common, are ideal for the transmission of many vector-borne diseases such as dengue (Patz et al. 2004, Sutherst 2004, Patz and Confalonieri 2005). Improvised health systems combined with the mass movements of refugees can contribute to an increase in dengue (Githeko et al. 2000).

2.1.5 Land use change A growing population will consequentially lead to intensification in agriculture; irrigation and deforestation all of which can affect the distribution and incidence of vector borne diseases (Norris 2004, Sutherst 2004, Patz and Confalonieri 2005). Theses human driven modifications of the environment can to some extent intensify disease emergence, such as allowing pathogens to spread into new areas and ecological niches (Patz et al. 2004, Patz and Confalonieri 2005).

Dengue virus differs from most arboviruses in regards to that it has completely adapted to humans and has lost the need for an animal reservoir. Humans are normally a dead-end host and do not contribute to the natural transmission cycle (Mackenzie et al. 2004), but for dengue humans are the principal reservoir (Wilcox and Colwell 2005) and its vectors are highly adapted to the urban environment (Weinstein et al. 1997, McBride and Bielefeldt-Ohmann 2000). So there is only so much land use change can affect the distribution and emergence of dengue. But nevertheless, changes in land use practises have the potential to disrupt ecosystems and influence human health by affecting services such as: clean water, provision of essential resources and climate regulation (McMichael 2006). All important services that can, directly and indirectly, affect the spread and incidence of dengue (Hales et al. 2002, Sutherst 2004, Patz and Confalonieri 2005).

A survey conducted by Chang et al. (1997) concluded that the mosquito fauna shifted in terms of species composition, occurrence and relative density in an area undergoing forest clearing for oil palm development in north Sarawak. Data was collected from the site during three annual phases; forest clearing, burning/cultivation, and maintenance. The analysis showed that after forest clearing, four species of Anopheles decreased considerably. While populations 21 of A. albopictus appeared to have increased between the cultivation stage and the maintenance stage and were now the most abundant species. However, this increase in A. albopictus was most likely due to the domestic breeding habitats, such as artificial water containers, that came with the plantation workers. Another study by Nakhapakorn and Tripathi (2005) over the spatial statistical relationship between different classes of land cover and a dengue affected areas in northern Thailand showed that forest in itself had no influence on dengue, instead, agricultural areas had a moderate influence on the incidence of dengue.

Agriculture does not only place people in vector habitats and thereby increases the risk of dengue; it is also associated with irrigation. Irrigation creates mosquito habitats by increasing the surface area of locally available water; it also brings water into areas where surface water is very limited (Norris 2004). Reduced land cover from agriculture can also result in a decrease in evaporative cooling during the day (Sutherst 2004). Plants transpire water vapour through photosynthesis which evaporates and thereby affects the moisture and heath fluxes to the atmosphere. Removing vegetation can alter water and energy fluxes and thereby increase the surface temperature up to 2°C which can be enough to change the vectorial capacity of existing vectors (Defries et al. 2002, Norris 2004, Sutherst 2004). Even if the changes in surface temperature might be small, they can still affect the developmental time of mosquitoes and thereby increase mosquito density and shorten the EIP of the pathogen which leads to improved transmission (Norris 2004).

Whether or not these proposed factors have an impact on the distribution of dengue is still rather controversial. According to Gubler “...there are no real hard data to show that [climate change is] having an effect” and that “a lot of public health officials and a lot of policy makers use global warming as a cop-out, an excuse for not controlling a disease that is very preventable” (Gubler in Phillips 2008). In the 2008 annual meeting of the American Institute of Biological Sciences, Gubler urged policy makers not to focus on climate change but on the key known drivers of dengue emergence: urbanisation, population growth and transportation (Phillips 2008). But, just as there is no data showing that climate change is affecting the distribution of dengue, Hales opposes, there is no proof reading of other factors, such as urbanisation, population growth and transportation. There are also no published studies so far that has tried to measure the relative importance of urbanisation, population growth and transportation in comparison to climate trends (Phillips 2008).

The overall aim of this thesis is to – in different steps – evaluate how the combination of factors reflecting global changes, globalisation and interconnectiveness, and their local impacts may contribute to an increased risk of the introduction and establishment of the main dengue vectors and the pathogen in New Zealand. The potential for a less competent native mosquito species to become dengue vector will also be discussed in the light of changes in climate, demographic and land use changes, and the patterns of travel and trade to this otherwise isolated country.

3. Dengue overview

This chapter will provide the reader with background information about dengue. It also reviews the environmental conditions necessary for dengue vector development (Objective 1).

Dengue used to be a mild illness and during the 18th and 19th centuries the disease only circulated in the tropics. The dengue virus and its vectors spread via ships with mosquitoes breeding in open water contained in various ways on board and thus surviving long voyages. Dengue epidemics were occasional with gaps of 10 to 40 years due to the slow journey (Gubler 2002). Since then our mode of transportation has change significantly and also the transmission dynamics of dengue. The increased movement of people and goods has enabled the dengue virus and its vectors to spread to new parts of the world, leading to nearly half of the world’s population at risk (Figure 2) (Phillips 2008).

Figure 2. Areas at risk 2008. Source: World Health Organization, 2008. http://gamapserver.who.int/mapLibrary/Files/Maps/Global_DengueTransmission_ITHRiskMap.png, 2009-07-25.

There are four serotypes of the dengue virus all with the potential to cause headache, fever and rash (McBride and Bielefeldt-Ohmann 2000). Infection with one serotype confers lifetime immunity against that serotype but not the others and there is evidence that a prior infection enhance the risk of developing DHF when infected with another serotype (Phillips 2008). Symptoms of DHF are haemorrhage (bleeding) due to a failure in the blood-clotting mechanism, resulting in internal bleeding which can lead to shock and fatality9.

3.1 The dengue transmission cycle The four serotypes of the dengue viruses all have humans as the primary vertebrate host and Aedes mosquitoes as main vectors (Rodhain and Rosen 1998). The virus has no direct pathogenic effect on the mosquitoes. When a mosquito acquires the dengue virus after probing, it will first infect the epithelial cells of the mosquito’s midgut to escape to the haemocele in order to infect the salivary gland. The dengue virus will then be secreted with the saliva when the mosquito is feeding. The mosquito’s genital tract can also get infected and the virus is able to penetrate fully developed egg at oviposition, a phenomenon called transovarial transmission (McBride and Bielefeldt-Ohmann 2000).

The transmission cycle of dengue virus by Aedes aegypti (Figure 3) starts with a viraemic person. The viremia begins just before the onset of symptoms, and last for about 5 days. Symptoms can last from 3 to 10 days. A. aegypti acquires the virus while probing and have the capability to be infected with two different serotypes of the virus at the same time without affecting the yield of either virus (McBride and Bielefeldt-Ohmann 2000). The virus then replicates within the mosquito during the EIP, normally 8-12 days. The infected mosquito then transmits the virus to another person who will start showing symptoms after 4-7 days following the mosquito bite. This period of 4-7 days is called the intrinsic incubation period (McBride and Bielefeldt-Ohmann 2000).

9 Concise Medical Dictionary, Oxford Reference Online 2007. http://www04.sub.su.se:2300/views/ENTRY.html?subview=Main&entry=t60.e2534, 2008-10-24.

Mosquito feeds / Mosquito refeeds /

aqcuires virus transmits virus

Extrinsic Intrinsic

incubation incubation period period

Viremia Viremia

Human 1 Human 2 0 5 8 12 16 20 24 28 Days Illness Illness

Figure 3. Transmission of dengue virus by Aedes aegypti. Source: Centers for Disease Control and Prevention, 2009. http://www.cdc.gov/ncidod/dvbid/dengue/slideset/set1/i/slide04.htm, 2009-04-08.

Mosquitoes are easily interrupted when feeding resulting in repeated probing of one or several hosts. The dengue virus is therefore dependent on a high viral concentration in the host to certify transmission in mosquitoes (McBride and Bielefeldt-Ohmann 2000).

3.2 Dengue vector ecology Mosquitoes from the genus Aedes act as the primary vectors for dengue, especially Aedes albopictus, Aedes aegypti, Aedes polynesiensis and to some extent Aedes notoscriptus (Rodhain and Rosen 1998).

3.2.1 Aedes albopictus – the Asian tiger mosquito The Asian tiger mosquito, Aedes albopictus, attacks more hosts than any other mosquito species in the world (Moore 2005), yet it is only the second most important dengue vector (de Wet et al. 2005). Nevertheless, it is an important maintenance vector of the dengue virus playing an essential role in the trans-ovarial transmission (de Wet et al. 2005).

A. albopictus originated from South East Asia and has been able to spread all over the world (Ministry of Health 1997), it is especially capable of colonising new areas very rapidly after it has been introduced (de Wet et al. 2005). Originally the mosquito was found in natural forest but has adapted to breed in artificial containers. The temperate strains of A. albopictus is cold 25 tolerant and able to diapauses (de Wet et al. 2005). The shortened photoperiod during late summer and early autumn stimulates the female mosquito to produce eggs that enter facultative diapause (i.e. preset suspension of development – usually enables avoidance of cold or dry conditions) (ECDC 2009). The eggs are desiccation resistant (de Wet et al. 2005) and by resisting hatching stimuli they can stay viable for months in a state of reduced morphogenesis (Mori et al. 1981).

Factors affecting A. albopictus distribution has been studied in Japan by Kobayashi et al. (2002). Larval surveillance was carried out in 26 different rural and urban areas in Japan between 1998 and 2000. A climatological analysis concluded that the current distribution of A. albopictus followed conditions with a mean annual temperature higher than 11°C and a mean temperature of the coldest month higher than −2°C.

The vector competence has been found to vary between different Vector competence is the strains of A. albopictus, in a comparative study by Gubler and vectors ability to obtain, maintain and transmit microbial Rosen (1976) of 13 geographic strains it was concluded that there agents. Besides vector competence, there are several is a significant variation in susceptibility for the four dengue external factors that influence the efficiency of disease serotypes (Gubler and Rosen 1976).There was a 100-fold transmission. These external difference in the oral infectious dose (the dose at which an factors belong to a broader category: vectorial capacity. organism can reproduce in the host and produce a measurable Source: ECDC 2007 effect according to Johnson (2003) between the least and most susceptible strain. There was also a direct relationship between infection rate and the amount of ingested virus for all of the mosquito strains (Gubler and Rosen 1976).

3.2.2 Aedes aegypti – the primary dengue vector Aedes aegypti is the primary dengue vector and trade and travel has allowed it to spread all over the tropics and subtropics, and is not known to survive temperatures lower than 10°C (Ministry of Health 1997). It has, however, been found in more temperate climates such as southern Europe. A. aegypti is a container breeder just like A. albopictus laying desiccation resistant eggs, but does not diapause (de Wet et al. 2005).

A. aegypti breeds in old discarded bottles, tires, plastic packaging, and water storage containers, basically anything that can collect water. Initially A. aegypti bred in small water bodies e.g. rock pools and tree holes, but at present the mosquito prefers to live indoors rather than outdoors and is highly adapted to the urban environment (Phillips 2008). 26

Although A. aegypti is thought to have a low susceptibility to dengue virus infection, it is still the main vector since it is an urban mosquito with very domesticated habits (McBride and Bielefeldt-Ohmann 2000). The fact that Aedes aegypti prefers to feed on humans rather than on other animals’ means that the viral transmission gets less diluted by blood from uninfected hosts (Phillips 2008). The vector competence of A. aegypti was tested for 24 different collections in Mexico and USA. The mosquitoes were fed orally with dengue 2 virus and the vector competence for the population ranged from 24% to 83% (Bennett et al. 2002).

3.2.3 Aedes polynesiensis – the Polynesian dengue vector The mosquito is widely distributed in New Zealand’s neighbouring countries in the Pacific and is a major nuisance biter, biting from dawn to dusk. A. polynesiensis uses both natural and artificial containers to breed, such as rock pools, tree holes, bottles and roof gutters, and can tolerate slightly brackish water. Their habitat is often associated with peri-domestic environments and human settlements (de Wet et al. 2005). Optimal temperature for A. polynesiensis has been measured to 10.8 °C > 32°C (Ingram 1954).

3.2.4 Aedes notoscriptus – an urban dengue vector in New Zealand The Australian mosquito Aedes notoscriptus is a potential vector for dengue. It is wide spread on the Northern Island of New Zealand and is entrenched in urban and peri-urban areas (Figure 4). This species was formerly found only at seaports north of Gisborne but has spread southwards in recent years and is now found throughout the Wellington region (Weinstein et al. 1997, McIntyre M. pers. comm. 2008). The lack of competition from native species in New Zealand has greatly facilitated the establishment of A. notoscriptus (Laird 1990). New Zealand only has 12 native mosquito species and 4 exotic, one of which is A. notoscriptus (Derraik and Slaney 2007).

Figure 4. Distribution of Aedes notoscriptus in New Zealand. Source: Landcare Research, 2009. http://www.landcareresearch.co.nz/research/biocons/invertebrates/mosquitoes/Mosquitoes%20of%20New%20Zealand/html/ notoscriptus.htm, 2009-04-21.

The mosquito originally bred in natural containers such as tree holes, rock pools and marshes but has been able to shift to artificial containers. The eggs of A. notoscriptus are very well adapted to this kind of habitat and can endure drought for several months (Weinstein et al. 1997).

A. notoscriptus vector competence is thought to be poor for most arboviruses (e.g. Ross River virus, yellow fever, Murray Valley encephalitis) but laboratory studies with New Zealand’s A. notoscriptus concluded that the mosquito is a capable experimental vector of dengue (Weinstein et al. 1997) and there is some evidence of it being an important dengue vector in urban environments in other countries (Derraik and Slaney 2007).

3.3 Dengue flavivirus The dengue flavivirus is an arboviral virus which transmission is entirely dependent on Aedes mosquitoes, and it is here the essential biological factor that is contributing to this virus success can be found (Wilcox and Colwell 2005). The dengue virus normally does not survive below 12-13 C° and the maximum transmission potential is found at 40 C° (Figure 5) (Martens et al. 1997).

Figure 5. Epidemic potential for Flavivirus. Source: Martens, J.M., T.H. Jetten and D.A. Focks. 1997. Sensitivity of malaria, schistosomiasis and dengue to global warming. Climatic Change 35: 145–156.

An important risk factor is the appearance of new genotypes of the dengue virus with better virulence and/or epidemic potential. This is the case when increased epidemic activity is caused by several virus serotypes, resulting in increased rate of genetic change in the virus and new genotypes or a virus strain emerges. This changes the dengue transmission dynamics and enhances the possibility of a second dengue infection (Gubler 2002). New virus strains have been identified in an increasing rate over the last 20 years (Gubler 2002) and found to roughly correlate with the human population growth (Aguirre et al 2002).

4. Potential introduction of dengue fever into New Zealand: risk assessment background

The following chapter introduces the location of the second part of my case study, New Zealand. It will especially look at the different social and environmental factors presented in the theoretical framework. It will also bring up the current situation of imported cases of dengue, information about their biosecurity and previous risk assessments. This information will be important to draw projections and scenarios for epidemic dengue in New Zealand (Objective 3).

New Zealand is the largest and the southernmost group of islands of Polynesia and unlike the other island groups New Zealand has a cool temperate environment and a very distinctive indigenous biota. The country consists of the North and South Island together with several smaller islands located in the southwest Pacific Ocean on the boundary of the Indo-Australian and Pacific tectonic plates.

4.2 Ecological features The earth movement that comes with New Zealand’s location has shaped the country to a mountainous landscape with geothermal and volcanic activity in the North Island. Half of New Zealand’s land cover is made up of natural land cover such as lakes, rivers, ice, native forest and native vegetation (Figure 6). Pasture makes up 39% of the land cover and 7.3% of the land area is covered in exotic forest. In 5 years, between 1997 and 2002, exotic forest cover increased by 8% and human settlements increased by 3% (van Bunnik et al. 2007).

Figure 6. Land use in New Zealand, 2004. Source: Ministry for the Environment. 2007. Environment New Zealand. Ministry for the Environment, Wellington.

Many of the urban areas are situated on floodplains resulting in flooding being the most common cause of civil defence emergencies. Flooding has always been a natural phenomenon due to the fact that New Zealand is a narrow country with very steep, fast flowing rivers. However, the intensive deforestation that came with human settlement changed the river systems and led to more severe and recurrent flooding. Deforestation has also caused 30 intensive hill-country erosion that damaged human infrastructure and waterways (van Bunnik et al. 2007). The country lost 51 000 hectares/year from 1990-2000 and 17 000 hectares/year from 2000-2005, that means that they had a deforestation rate of 0.6% and 0.2% respectively10.

New Zealand’s location and its mountainous landscape produce a highly variable climate with a larger climatic contrast from east to west than from north to south (Leathwick et al. 2002). The country also has variations in the decadal climate, which are related to the Interdecadal Pacific Oscillation (IPO). New Zealand shifted to the negative phase of the IPO (La Niña) around 1999; this condition means higher temperatures and reduced rainfall in the southwest but an increase in the northeast. These conditions are expected to last until 2030-2040 (Ministry for the Environment 2008). Temperature range over the year is small and the mean annual temperature (the average of all the monthly average temperatures) is 10.1°C ranging from -6.9 to 16.2°C with the mean minimum temperature (0.86°C) in July (Leathwick et al. 2002). Temperatures are anticipated to increase by 1°C by 2040 and by 2°C by 2090 (Ministry for the Environment 2008). In most urban areas rainfall ranges from 600 millimetres to 1.5 metres per year. New Zealand receives about 180 rain days in a year on the west coast with much drier weather on the east coast. Vapour pressure lies around 12.4 hectopascals (hPa) and mean relative humidity lies between 65-85%. Humid spells are rather short-lived, but there has been an increase in humid spells in the south in recent years11.

4.2 Socio-economic features At the end of 2007 New Zealand’s population was estimated to be 4,230,700 people in total with a majority of the population (3,265,700) living on the North Island 12. Between 2001 and 2006 the population increased with 8.4%13 with the biggest increase in the cities of the North Island. New Zealand is one of the most urbanised countries in the world; 86% of the population live in urban areas leaving the rest of the country with a low overall population

10 Food and Agriculture Organization of the United Nations 2009. http://www.fao.org/forestry/sofo/en/, 2009-02-17.

11 New Zealand’s National Metrological Service 2008. http://www.metservice.co.nz/default/index.php?alias=climateofnz, 2008-09-29.

12 Statistics New Zealand 2008. http://www.stats.govt.nz/tables/population-indicators.htm, 2008-09-30.

13 Statistics New Zealand 2008. http://www.stats.govt.nz/NR/rdonlyres/4980B3EA-6F91-4A09-BB06- F7C1C2EA3403/0/quickstatsaboutnzspopanddwellingsrevised.pdf, 2008-09-30.

31 density, 15.8 person/km2 for the whole country and 28.7 person/km2 for the North Island. Land is an important part of the economy; making up 17% of the country’s gross domestic product (GDP) by supporting forestry, agriculture, viticulture, horticulture and tourism.

New Zealand’s isolation means that they are dependent on air and sea transport for imports and exports, this dependence on a high level of international movement of goods and people greatly increase the risk of accidental importation of unwanted organisms and pathogens. While the New Zealand Customs and Biosecurity surveillance is among the most stringent in the world there are underlying biological reasons why the New Zealand environment is also among the most vulnerable to invasion by biological agents, with significant potential costs in the health field as well as international export markets (Ministry for the Environment 2007). The overseas imports were $39.9 billion for the year ended June 2007. This is an increase of 52% since the $20.9 billion recorded in 199714 (Figure 7).

Figure 7. Value of cargo unloaded in New Zealand and Auckland. Source for data: Statistics New Zealand. 2008. http://www.stats.govt.nz/products-and-services/info-releases/oseas-cargo-info-releases.htm, 2008-11-10.

New Zealand imports most of its goods from Australia; in 2002 it represented 22% of the total imported goods (Figure 8). Imports with the largest value from Australia in 2002 were 1) mechanical machinery and equipment and 2) vehicles, parts and accessories. The EU was the second largest source and USA the third; accounting for 19% and 16% respectively of the

14 Statistics New Zealand 2008. http://www.stats.govt.nz/products-and-services/info-releases/oseas-cargo-info- releases.htm, 2008-11-10.

32 imports in 2002. Imports from Japan have had an annual increased of 9% between 1999 and 2002. The majority of the imported goods from Japan (53%) are vehicles, parts and accessories. The rest is mostly used cars and machinery15.

Figure 8. Origin of New Zealand’s imports (billion $). Source: Statistics New Zealand. 2009. http://www.stats.govt.nz/products-and-services/Articles/Australia-Trade-Nov02.htm, 2009-04-18.

The countries isolation makes it very reliant on air transport for travellers, only a few arrive by sea. In 2005 8.69 million passengers travelled to or from New Zealand, which is an 83% increase in 10 years. This rise is most likely due to international travel becoming more accessible and affordable (Ministry for the Environment 2007).

Between 1999 and 2008 short-term departures grew from 1.182 million to 1.977 million, where almost half of the trips were to Australia (Figure 9). During this period travels to countries in Oceania (not including Australia) also increased, going from 130,600 to 249,300 with the most departures to Cook Islands, Tonga, Fiji, Vanuatu, Samoa, Norfolk Island, French Polynesia and New Caledonia (Statistics New Zealand 2008).

15 Statistics New Zealand. 2009. http://www.stats.govt.nz/products-and-services/Articles/Australia-Trade- Nov02.htm, 2009-04-18.

Figure 9. Short-term New Zealand traveller departures to Pacific Island countires. Source for data: Statistics New Zealand. 2008. Short-term New Zealand Traveller Departures to Pacific Island Countries. Wellington, New Zealand.

Trips to these Pacific Island countries are seasonal; the winter months are the most popular time. Travels to Tonga and Samoa are more common in December, reflecting New Zealand’s ethnic composition where travellers go to visit relatives during Christmas. In 2008, 39 percent of the departures to Tonga and Samoa were to visit friends and/or relatives. The number of people visiting friends and/or relatives in the Cook Islands (6400) is similar to that of Tonga (6900) (Statistics New Zealand 2008).

4.3 Dengue cases and intercepted dengue vectors in New Zealand So far there have been no cases of epidemic dengue in New Zealand, which is very fortuitous considering there are no conditions to make New Zealand unfavourable for the transmission of arboviral diseases (Weinstein et al. 1997). Crump et al. (2001) argues that due to New Zealand’s isolation it has postponed impacts from infectious diseases emerging in other parts of the world.

The notified dengue cases in New Zealand (Figure 10) between 1997 and 2005 reflects the dengue situation in the Pacific Island countries (Figure 11). The 26 notified dengue cases in New Zealand in 1998 corresponds to the epidemics in Fiji, New Caledonia and Tonga that year. The 93 cases in 2001 and 70 cases in 2002 coincided with the 2001 epidemic in French Polynesia and the 2002 epidemic in the Cook Islands.

Figure 10. Notified dengue cases in New Zealand by ethnicity. Source for data: New Zealand Public Health Observatory 2008. http://www.nzpho.org.nz/NotifiableDisease.aspx, 2008-10-11.

Figure 11. Dengue cases in Pacific Island countries and Australia. Source for data: World Health Organization 2008. http://www.who.int/globalatlas/default.asp, 2008-09-12. The World Bank 2008. http://go.worldbank.org/U0FSM7AQ40, 2008-09-05.

Of the 70 imported cases in 2002; 41 came from the Cook Islands (Figure 12). Of the 114 dengue cases in 2007, 75 of these came from the Cook Islands. Figure 10 shows that the majority of the imported cases are of Pacific Island ethnicity and the least of Maori.

Epidemics in the Pacific Island countries clearly affect the epidemiology of dengue in New

Zealand.

Figure 12. Origin of imported dengue cases in New Zealand. Source for data: Public Health Surveillance 2008. http://www.surv.esr.cri.nz/surveillance/NZPHSR.php, 2008-09-11.

There have been 171 interceptions of non-native mosquitoes (12 of these being Aedes albopictus) between 1929 and 2004 in New Zealand. Of these 152 interceptions had a described origin, where 66% came from the South Pacific, 28% originated from Australia and 15% from Japan which has been the head source of exotic mosquitoes since the 1990s. 62% of the interceptions occurred on aircrafts, however by looking only at the last 15 years of the study period the main vessel will be ships, accounting for 82% of the interceptions. For Aedes aegypti, Aedes albopictus and Aedes polynesiensis the main mode of invasion have been ships from Japan to Auckland, transporting used tyres, used cars and used machinery (Derraik 2004). A study by Laird et al. (1994) conducted in Auckland from November 1992 to January 1993 discovered that 5 ships from Japan had brought in used tyres infested with live A. albopictus, both adults, pupae and larvae.

4.4 Biosecurity - surveillance and exclusion programmes “Biosecurity is the exclusion, eradication or effective management of risks posed by pests and diseases to the economy, environment and human health” (MAF Biosecurity New Zealand 2003).

New Zealand’s native fauna, lacking terrestrial mammals, never included suitable reservoir hosts for human pathogens; this was recognized early by the government and one of the world’s most strict quarantine systems were soon put in place (Crump et al. 2001). Rigorous biosecurity is essential to protect New Zealand’s economic interests – and especially its dependence on travel and trade, this is reflected in the passing of the Biosecurity act in 1993. The law was to support efficient protection of New Zealand’s biological systems from exotic pests and diseases (MAF Biosecurity New Zealand 2003). The increases in passengers and goods arriving in to the country further increases the chances of exotic pests to appear, leaving the biological systems in more danger than ever before (MAF Biosecurity New Zealand 2003).

However, there is a chronic difficulty of gradually applied scant resources and changing political priorities (M. McIntyre pers. comm. 2008) - which have been insufficient to ensure continuing protection in keeping with the arrivals of people and goods. With more movement and as the borders diffuses further, New Zealand’s biosecurity system needs to advance quickly and become more extensive (MAF Biosecurity New Zealand 2003).

In a report from the Ministry of Health (1997) the Public Health Group stressed that the existing surveillance, border control and ready reaction systems needs to be improved in order for them to give an early, economic and successful response to the appearance of new exotic mosquitoes. To make sure active monitoring the report recommends:

• standardized guidelines to be published; • clear responsibilities for service providers; • risk-based protocols be designed for each region by the appropriate authority; • reviews and records on quality; • professional training and education for persons responsible for monitoring.

Designated officers carrying out routine surveillance of international airports and seaports are suggested to be responsible for the eradicating mosquitoes and their habitats. Vessels for mosquito invasion, such as water filled used tyres, should be checked at the border in order to exterminate exotic mosquitoes before they become established (Ministry of Health 1997).

At present, dengue, along with all other arboviral diseases, is recorded by the Institute of Environmental Science and Research (ESR) on their notifiable disease database EpiSurv. A 37 second source of surveillance is the viral analysis results of serum specimens which are screened for IgM and IgG antibodies to the dengue virus. A low level of IgG reactivity is a sign of a previous, although remote, dengue virus infection. A high level of IgG reactivity, but without any IgM reactivity indicates a recent reinfection with a different serotype of the dengue virus. An occurrence of both IgM and IgG antibodies, however, suggest a recent primary infection of dengue (Khan et al. 2001).

4.5 Previous risk assessments for epidemic dengue in New Zealand Kay (1997) covered the key factors that influence the risk of introducing vectors and their associated diseases to New Zealand. They are as follows:

• population growth and population density; • climate such as, temperature, rainfall and climate change; • country of origin and length of stay of international visitors; • international air services such as, arrivals and trade cargo; • shipping; e.g. number of cargo entries, port of loading and importation of used tyres.

Areas with major international airports or shipping are at higher risk of a potential introduction of exotic mosquitoes. Auckland is the area that is the most at risk since it has 1) the largest population; 2) the second highest population growth; 3) the highest temperature; 4) a large Pacific Island community (Kay 1997); 5) 71 percent of all air passengers in 200016; 6) unloaded more than half of all import cargo in 200717 and 7) 50 % of imported used tyres (Kay 1997).

Wellington, Christchurch, Tauranga, Napier and Gisborne are at medium risk. However, strong winds in Wellington and low annual average temperatures in Christchurch will lower the risk in these two cities. The risk in cities in the South Island is difficult to determine due to low temperature will make it less likely for exotic mosquitoes to become established (Ministry of Health 1997).

16 Statistics New Zealand. 2009. http://www.stats.govt.nz/analytical-reports/tourism-migration-2000/tourism- and-migration-part1.htm, 2009-05-03.

17 Statistics New Zealand. 2009. http://www.stats.govt.nz/products-and-services/hot-off-the-press/overseas- cargo/overseas-cargo-statistics-year-end-jun-07-hotp.htm?page=para002Master, 2009-05-03.

The Ministry of Health (1997) estimated the cost of a dengue outbreak in New Zealand to in the order of 250 million New Zealand dollars. The cost would come from losses in productivity due to employee absence, losses in tourism and tourism related business and medical costs (Table 1).

Table 1. Cost associated with a dengue outbreak in New Zealand.

Medical Time off work Tourism and business

100 hospitalisations for 1 week 900 cases off for 1 week 5 percent of total foreign exchange earnings for one year lost

10 intensive care cases for 1 week 100 cases off for 2 weeks and further 2 weeks in hospital

9 cases off for 4 weeks

1 death

Source: Ministry of Health. 1997. Exclusion and control of exotic mosquitoes of public health significance: report to the Minister for Biosecurity. Public Health Group, Wellington.

The costs were calculated assuming that of 100,000 people exposed there would be 1000 clinical cases of classical dengue and 10 cases of DHF. They were imitative to actual outbreaks of dengue in other countries (e.g. Puerto Rico 1977, Australia 1993 and Malaysia 1996) and the Ministry of Health consider these assumptions to be very conservative.

A dengue outbreak could become a reality in Auckland and other larger cities if adequate numbers of vector mosquitoes and viraemic individuals are present. A lack of rapid diagnostic services and general practitioners without awareness of dengue symptoms can result in an outbreak to pass unnoticed for some time, allowing the dengue infection to spread widely (Ministry of Health 1997).

4.5.1 The Hotspots system - spatial analysis of dengue fever risk to New Zealand Projections done by de Wet el al. (2005) using the Hotspot system, that is explained in detail below, suggested that A. albopictus, if it was to be introduced under current climatic conditions, would be able to establish in areas of Northland, Auckland, the Waikato regions, the coastal areas of the Bay of Plenty and Hawke’s Bay (Figure13). Climate change would broaden this risk to include the northern part of the North Island as well as the coastal areas. The South Island is with present climatic conditions not under risk, but with climate change it might be possible for A. albopictus to occupy the northern and eastern coastal areas of the South Island. This scenario enhances the risk significantly since Christchurch would then be a 39 potential entry port. A. aegypti, however, is very unlikely to survive in New Zealand under current climatic conditions. A high estimate climate change scenario for 2050 suggests that A. aegypti might be able to establish in Northland.

Potential distribution of A. Climatic suitability and exclusion map for A. albopictus under current climatic albopictus for a climate change scenario for the conditions year 2050

Potential distribution of A. Climatic suitability and exclusion map aegypti under current climatic for A. aegyti for a climate change conditions scenario for the year 2050

Figure 13. Potential distribution of A. albopictus and A. aegypti under current and future climatic conditions. Source: de Wet, N., D. Slaney, W. Ye, R. Warrick, and S. Hales. 2005. Hotspots: Exotic mosquito risk profiles for New Zealand. International Global Change Institute, University of Waikato, Hamilton and Ecology and Health Research Centre, University of Otago, Wellington

The Hotspot based analysis for A. polynesiensis implied that New Zealand is over all of very low suitability under present climatic conditions (Figure 14). It is the coastal ares of the North Island, especially Auckland that could possibly provide tolerable conditons for the mosquito. A warmer climate would give more suitable habitat areas and increase the risk of a colonisation by A. polynesiensis.

Potential distribution of A. Climatic suitability and polynesiensis under current exclusion map for A. climatic conditions polynesiensis for a climate change scenario for the year 2050

Figure 14. Potential distribution of A. polynesiensis under current and future climatic conditions. Source: de Wet, N., D. Slaney, W. Ye, R. Warrick, and S. Hales. 2005. Hotspots: Exotic mosquito risk profiles for New Zealand. International Global Change Institute, University of Waikato, Hamilton and Ecology and Health Research Centre, University of Otago, Wellington

The Hotspots system support spatial analysis of dengue fever risk to New Zealand with a resolution of a 100 metre grid. This approach integrates numerous component models, such as a climate scenario generator for New Zealand; a climate based vector distribution model that describes areas of receptivity for A. aegypti and A. albopictus; and an epidemic potential model that describes temperature dependent viral transmission efficiency for A. aegypti18. The Hotspots system was developed by the International Global Change Institute (IGCI) of the University of Waikato, Hamilton, in collaboration with the Ecology and Health Research Centre at the Wellington School of Medicine and Health Sciences of the University of Otago, Wellington. Exotic mosquito risk profiles for New Zealand were created using the Hotspot system (Table 2).

Table 2. Dengue vector risk profiles for New Zealand

A. albopictus A. aegypti A. polynesiensis Risk of High, especially via ships High High at the ports of Auckland introduction and Christchurch

Risk of Yes, cold tolerant Not for long-term under Possibly in the northern coastal establishment current climatic conditions areas of the North Island

18Hotspots 2009. http://www.waikato.ac.nz/igci/hotspots/, 2009-03-13 41

Area at risk Northland, Auckland, parts of Auckland, Whangarei, Coastal areas of Northland and Waikato, Coromandel peninsula, Christchurch, Invercargill and Auckland Bay of Plenty, Gisborne and Hawkes to a lesser extent Tuaranga, Bay Napier, Wellington and Dunedin Future risks Might be able to establish in most of A warmer climate could Warmer climate may provide the North Island and some parts of support populations in more suitable conditions for the South Island as far south as Northland, Auckland and long-term establishment in the Christchurch Whangarei northern coastal areas

Previous 11 interceptions between 1998-2004 At the port of Auckland via Larvae and pupa has recently interceptions of which 10 came from Japan via shipping’s of used machinery been found at the port of ships Auckland in used machinery and used tires. Adult mosquitoes have been intercepted from the Pacific Island countries (air). Fishing boats and yachts is also a mode of entry for eggs Source: de Wet, N., D. Slaney, W. Ye, R. Warrick, and S. Hales. 2005. Hotspots: Exotic mosquito risk profiles for New Zealand. International Global Change Institute, University of Waikato, Hamilton and Ecology and Health Research Centre, University of Otago, Wellington

5. Case study

This chapter will both explain the methodology used for this study and the decision to divide it into two parts, a global context and a New Zealand context.

This study is presented in two parts. The burden of dengue is firstly examined in a global context, and then the New Zealand situation is explored. The intent is to develop an overview of dengue and its vectors (objective 1 p.11) and consider possible contributing factors to present patterns (objective 2 p. 11) and then to use this information to consider present and future dengue risks for New Zealand (objective 3 p. 11). The methodology used for both parts of the case study is presented below.

5.1 Case study part I: The global context This overview examines a set of 8 environmental and social indicators in 232 geographical areas of which 76 have epidemic dengue. It also deals with the proposed mechanisms for dengue emergence (chapter 2) and gives background information about dengue and its vectors (chapter 3).

5.1.1 Literature Relevant literature was located using Google Scholar and by focusing on some particular journals. Some of the theoretical framework literature also came from the Millennium Ecosystem Assessment. Public health journals and ecology and health journals, such as

EcoHealth, was frequently used. Literature on the dengue virus and its transmission was found in journals on microbiology such as, Clinical Microbiology Reviews and TRENDS in Microbiology.

5.1.2 Data The total annual number of dengue and DHF cases by country was gathered from 76 countries over the period 1990 to 2005. Nearly all data was obtained from the WHO’s DengueNet19 with additional data added from the Pan American Health Organization20 and WHO’s regional office for South-East Asia21.

Base on the findings of the literature review a total of 8 different social and environmental factors was gathered from 232 geographical areas. These were: population density (persons/km2), urbanisation as a percentage of total population, health expenditure per person, imports as a percentage of GDP, access to improved water source (defined by the WHO22 as household connections, boreholes, public standpipes and protected dug wells) as a percentage of population with access, visitors from dengue risk areas as a percentage of the total population, deforestation rate and a climate variable.

Urbanisation and crowding is proposed to be one of the main drivers of dengue emergence (Gubler 2002, Norris 2004, Sutherst 2004, Patz and Confalonieri 2005). Access to improved water sources can be seen as a measure of unsafe distribution and storage of water and drainage of waste water can provide breeding grounds for mosquitoes (Norris 2004, Sutherst 2004, Patz and Confalonieri 2005). Trade and travel are also major drivers of dengue emergence, with the potential to spread both pathogens and their vectors all over the world (Daszak et al. 2000, Patz et al. 2004, Sutherst 2004, Patz and Confalonieri 2005). Deforestation has the possibility to result in pathogens spreading into new areas and ecological niches and altering mosquito species composition (Norris 2004, Sutherst 2004, Patz and Confalonieri 2005). In combination with these environmental factors a weak public

19 World Health Organization 2008. http://www.who.int/globalatlas/default.asp, 2008-09-12.

20 Pan American Health Organization 2008. http://www.who.int/globalatlas/dataQuery/default.asp, 2008-09- 15.

21 World Health Organization 2008. http://www.searo.who.int/en/Section10/Section332_1101.htm, 2008-09- 15.

22 World Health Organization 2008. http://www.who.int/whosis/indicators/2007ImprovedAccessWaterSanitation/en/, 2008-09-05. 43 health infrastructure and poor disease surveillance can add to further dengue burden (Wilcox and Colwell 2005).

All data but the deforestation rate, the visitors data and the climate variable came from the World Bank’s World Development Indicators 200823. Deforestation rate data came from the Food and Agriculture Organization of the United Nations (FAO) 2009 report “State of the world’s forests”. Data on the region of origin for visitors came from United Nations Statistics Division database “UNdata”24. The regions of origin were: Western Asia, Southern Asia, East and Southeast Asia/Oceania, Africa, Americas and Europe and the data was only available from 2000 to 2005. The measure of estimated risk different visitors impose on New Zealand is estimated by multiplying the average number of visitor (for 2000- 2005 from each region) with the dengue rate for that specific region. The sum of the regional numbers was then divided by the destination country’s average 1995-2005 population. Knowing both the dengue risk and also the average arrivals from different regions we can calculate the estimated average number of dengue cases entering a country. By dividing the estimated average number of dengue cases entering with the population of the designated country we can see what percentage these imported dengue cases represent of the total designated country’s population. Also by dividing the imported dengue cases with the population of the designated country one can compare the impact these imported cases will have on different countries. This is important because an imported number of dengue cases will have a much more severe impact on a designated country with a population of 100 compared to one with a population of 10 000.

The climate variable used in the analysis is a measure of the Vapour pressure in population weighted risk of dengue fever transmission meteorology refers to the pressure exerted by water according to vapour pressure. This approach to measuring the molecules in a given risk of dengue was first used by Hales et al. (2002). The aim of volume of the atmosphere, which increases with Hales et al. (2002) study was to examine the effects of climate temperature. Source: Encyclopædia change on the transmission of vector borne diseases. They Britannica “showed that the current geographical limits of dengue fever transmission can be modelled with 89% accuracy on the basis of long-term average vapour pressure.” Data on 1) dengue

23 The World Bank 2008. http://go.worldbank.org/U0FSM7AQ40, 2008-09-05.

24 United Nations Statistics Division 2009. http://data.un.org/Default.aspx, 2009-06-23. 44 outbreaks during 1975-1996; 2) monthly averages of vapour pressure from 1961 to 1990 and 3) population based on the spatial pattern in 1990, and region-specific projections for 2055 and 2085 was entered into a geographic information system that had been converted into ASCII grid format. A logistic regression, fitted by the method of maximum likelihood, was used to model the presence (1) or absence (0) of dengue, on the basis of the vapour pressure data (Table3).

Table 3. The modeled presence or absence of dengue on the basis of monthly average vapour pressure between 1961-1990. Coefficient P 95% Confidence interval Vapour pressure 0.2703548 0.000 0.2658619 0.2748477 Constant -5.411313 0.000 -5.492468 -5.330159 Source: S. Hales

This model was then fitted with vapour pressure data for all the 232 geographical areas to estimate a baseline risk of dengue fever transmission between 0 and 1000 (the climate variable) for each of the grid cells. The relationship between vapour pressure and the climate variable is represented by the following equation:

R= (exp(X*β)+C / (1+exp(X* β)+C))*1000

Where R is the climate variable (risk of dengue), X is the vapour pressure in hPa, β is the vapour pressure coefficient and C is a constant (see Table 3). The equation is basically the modelled probability multiplied by 1000.

The findings are biologically credible since vapour pressure is only high where temperatures and precipitation are high, which are the conditions necessary for the survival of vector mosquitoes and a reduction in the EIP of the dengue virus. After several discussions with Dr. Hales it was agreed that for the purpose of this study it would be sufficient to use their findings as the only climate variable. The reasoning behind this is that their model included monthly average vapour pressure, precipitation, maximum, minimum and mean temperature as the main climate variables effecting dengue transmission, both singly and combined. From these variables, “annual average vapour pressure was the most important individual predictor of dengue fever distribution” (Hales et al. 2002) and that the other climate variables individually have a small and statistically insignificant impact on dengue transmission.

5.1.3 The regression analysis Using the statistical software program, STATA 10, a Poisson regression was fitted to model the relationship between dengue rate, on the basis of 1995-2005 reports for 232 different geographical areas, and different social and environmental predictors. Area specific dengue incidence rates for 1990-2005 period was calculated using the number of reported dengue cases as a numerator and person-years at risk (pyar) per 100 000 of the population as a denominator. The output from the regression analysis is a dengue rate ratio for each of the 8 variables. The dengue rate ratio indicates the extent to which specific variables are estimated to affect the dengue rate holding that all other variables remains constant, thus making it possible to draw projections for New Zealand.

It is important to note that the result from the regression analysis does not by itself illustrate the relative importance of the different variables, i.e. it does not directly tell if one variable will affect dengue rate more than another. This is due to the fact that all of the 8 variables had different value ranges (e.g. the climate indicator ranged from 0-1000 while the urbanisation variable only ranged from 0-100) and non-comparable units of measurement. In this regard variables with a broader range of values will appear to have a considerably lower dengue rate ratio than variables with a smaller range of values.

5.2 Case study part II: The New Zealand context The second part of the study deals with New Zealand only, especially the North Island since it has been identified as the area most at risk for epidemic dengue.

5.2.1 Literature and data Information on ecological and socio-economic features came from the Ministry for the Environment New Zealand and Statistics New Zealand. Climate data was found on the New Zealand’s National Metrological Service website. Data on imported dengue cases came from the New Zealand Public Health Observatory. Information on the biosecurity and surveillance programs was extracted from the MAF Biosecurity New Zealand website and from the Ministry of Health.

5.2.2 Projections Projections for 2 of the 8 variables (the population density and climate variable) were made only for the North Island for year 2070. The decision to concentrate on the North Island was

46 based on the Ministry of Health (1997) report, Kay (1997) and the exotic mosquito risk profiles created by the Hotspots system, as described in section 4.5.1.

The population weighted risk of dengue fever transmission according to vapour pressure for the North Island of New Zealand for 2070 was calculated. This was done by using estimates for vapour pressure generated by the MIROC3.2 model for interdisciplinary research on climate25 and the previous equation (see section 5.1.2):

R= (exp(X*β)+C / (1+exp(X* β)+C))*1000

Official population density projections up to year 2061 for New Zealand and 2031for the North Island was used to create the 2070 population projection for the North Island. The population projection for New Zealand came in 9 series and the projection for the North Island in 3 series, both produced by Statistics New Zealand. According to Senior Demographer Kim Dunstan (2009) “Statistics New Zealand has run the 2006-base national population projections out to 2111, by keeping fertility, mortality and migration assumptions constant from 2061. Series 1, 5 and 9 give alternative populations of 4.52, 5.63 and 6.85 million in 2070, respectively”. Series 5 was used to generate the 2070 North Island projections, since at present it is “considered the most suitable for assessing future population changes”26. The population for 2070 was estimated by calculating the year on year (YoY) decrease in growth for both New Zealand and the North Island. The estimation was reviewed by Senior Demographer Kim Dunstan at Statistics New Zealand (2009) who concluded that “it is sensible to consider population growth rates and population shares” this way.

5.2.3 Scenarios Scenarios will be based on the Ministry of Health’s 1997 estimates for a dengue outbreak in New Zealand. This is due to the fact that New Zealand has no cases of epidemic dengue. The estimate for a dengue outbreak in 1997 conditions, suggested that approximately 100,000 people would be exposed. Of these 1000 cases would be of classical dengue and 10 cases of DHF. These estimates are considered to be very conservative.

25 IPCC 4th Assessment Report 2007. http://www.ipcc-data.org/ar4/model-NIES-MIROC3_2-HI.html, 2009- 04-30. 26 Statistics New Zealand 2009. http://wdmzpub01.stats.govt.nz/wds/TableViewer/summary.aspx, 2009-05-16 47

Using the estimates from the Ministry of Health and the dengue rate ratio (from the dengue regression analysis) future dengue rate for New Zealand can be calculated using an exponential model:

t Nt=N0*a

Where N is the dengue rate (number of dengue cases per year), t is the amount of units a certain variable has changed and a is the dengue rate ratio given by the dengue regression analysis.

6. Results

This section will start by introducing the results from the dengue regression analysis. It illustrates the impact that the different variables have on dengue rate (Objective 2). These results will then be used as a frame of reference to make projections and future scenarios for epidemic dengue in New Zealand (Objective 3).

6.1Results part I: The global context All eight variables considered independently (Table 4) show a significant correlation (p < 0.000) with the dengue rate ratio, as well as the overall analysis (p < 0.000).The findings can be used to extrapolate, e.g. population density has a dengue rate ratio of 1.002. In short terms, this means that if population density increases by 1 unit, dengue rate will increase by 0.2%, holding that all the other independent variables remains constant. Both health expenditure and improved water sources has a negative impact on dengue, e.g. health expenditure that has a dengue rate ratio of 0.999 will decrease dengue rate by 0.001% if health expenditure goes up 1 unit.

Table 4. Relationship between dengue rate and social and environmental predictors for all countries (Poisson regression).

Dengue rate ratio P 95% Confidence interval

Population density (person/km2) 1.002 0.000 1.002 1.002

Urbanisation (% of pop.) 1.030 0.000 1.030 1.030

Health expenditure per person (US$) 0.999 0.000 0.999 0.999

Imports (% of GDP) 1.008 0.000 1.008 1.008

Improved water sources (% of pop. with access) 0.987 0.000 0.987 0.987

Visitors from dengue areas (% of population) 1.070 0.000 1.070 1.070

Deforestation rate 1.317 0.000 1.316 1.319

Climate indicator 1.006 0.000 1.006 1.006 Chi2 < 0.0000 R2 = 0.6486 48

6.2 Results part II: The New Zealand context Two of the eight variables in the analysis (Table 4) were used to create scenarios in order to see to what extent they can increase the risk of dengue emergence for New Zealand’s North Island. All of the variables are of interest when it comes to dengue emergence in a global context, but when building scenarios for specific areas, some variables will be of more importance than others. For example, New Zeeland’s water supplies have a well-developed infrastructures (97% of the population have access to improved water sources27) and the access to improved water sources will therefore not be a concern for the future. Urban concerns are more likely to be with maintenance of household plumbing and the availability of artificial containers that collect water (rubbish, poorly maintained property and drains, old tyres), especially in more deprived areas such as South Auckland. However, the main reason behind using only two variables, namely the climate variable and population density was that projections for the other six variables could not be found.

6.2.1Vapour pressure projections With an estimated monthly average vapour pressure of 17.7hPa in 2070 for the North Island, the dengue risk equation (see section 5.1.2):

R= (exp(X*β)+C / (1+exp(X* β)+C))*1000 gives R= (exp(17.7*0.27)-5.4 / (1+exp(17.7*0.27)-5.4))*1000 =350. This is a significant change of the climate variable (the population weighted risk of dengue fever transmission according to vapour pressure during the study period was 113 which is equal to a vapour pressure of 12.4hPa) increasing it by 350-113=237 units.

6.2.2Scenario I: Increase in vapour pressure Scenarios will be based on the Ministry of Health’s 1997 assumption, that if a dengue outbreak would occur in 1997 in New Zealand there would be approximately 100,000 people exposed. Of these there would be a total of 1010 cases; 1000 clinical cases of classical dengue and 10 cases of DHF.

With an increase of 237 units for the climate variable in 2070 holding that all other variables remains constant, the exponential model (see section 5.2.3):

27 The World Bank 2008. http://go.worldbank.org/U0FSM7AQ40, 2008-09-05. 49

t Nt=N0*a

237 gives N237=1010*1.006 =4169 cases of epidemic dengue in New Zealand for 2070. This is an fourfold increase of the hypothetical number of dengue cases in 1997.

6.2.3Population density projections Figure 15 shows the projected population growth on the North Island with a population of 4,456,429 at 2070. The North Island population projection was based on official population projections made for New Zealand and the North Island summarised in table 5. The table also shows the year on year growth for both New Zealand and the North Island.

Figure 15. Population projection for the North Island. Source for data: Statistics New Zealand 2009. http://www.stats.govt.nz/products-and-services/table-builder/pop-projections.htm, 2009-05-16

Table 5. Population projection for New Zealand and the North Island. New Zealand YoY North Island YoY % of total Year population growth population growth population 2006 4184500 - 3185100 - 76.12% 2011 4393200 4.99% 3358000 5.43% 76.44% 2016 4588700 4.45% 3524200 4.95% 76.80% 2021 4770800 3.97% 3681500 4.46% 77.17% 2026 4939400 3.53% 3829200 4.01% 77.52% 2031 5089700 3.04% 3963400 3.50% 77.87% 2036 5217600 2.51% 4081509 2.98% 78.23% 2041 5325200 2.06% 4182731 2.48% 78.55% 2046 5413400 1.66% 4265967 1.99% 78.80% 2051 5480800 1.25% 4329530 1.49% 78.99% 2056 5531500 0.89% 4372825 1.00% 79.05% 2061 5571000 0.71% 4406059 0.76% 79.09% 2066 5602755 0.57% 4433817 0.63% 79.14%

2071 5630000 0.49% 4456429 0.51% 79.16% Data in shaded areas comes from Statistics New Zealand 2009 official data. http://www.stats.govt.nz/products-and- services/table-builder/pop-projections.htm, 2009-05-16. Unshaded areas are estimates based on the official data’s YoY growth. Kim Dunstan (Senior Demographer, 2009-07-21) at Statistics New Zealand has reviewed the data and in his opinion it is sensible to consider population growth rates and population shares as I have done.

The 2070 North Island population of 4,456,429 gives a population density of 39 persons/km2 (4,456,429 persons/113729 km2), which is a 13 unit increase since the 1990-2005 study period.

6.2.4 Scenario II: Increase in population density The population density scenarios will, just as the climate scenario, be based on the Ministry of Health’s 1997 hypothetical number of 1010 cases.

With an increase of 13 units for the population density variable in 2070 holding that all other variables remains constant, the exponential model (see section 5.2.3):

t Nt=N0*a

13 gives N13=1010*1.002 =1037 cases of epidemic dengue in New Zealand for 2070.

7. Discussion

This section will discuss to possible scenarios for New Zealand’s North Island. It will also touch on dengue management for the North Island and what can be learned from the case study site.

Based on areas of known dengue occurrence, the outcome from the dengue regression analysis is validated for predicting dengue rates based on national characteristics (R2 = 0.6486). The use of national characteristics is a particular advantage as data for the 8 variables used in this analysis are readily accessible in databases available on the internet. Furthermore, population projections tend to decrease in accuracy as the area concerned get smaller28. This study utilized the results from the dengue regression analysis for building scenarios for New Zealand’s North Island using two of the eight variables, namely the climate and population density variables.

28 Population Reference Bureau 2009. http://www.prb.org/Publications/PolicyBriefs/UnderstandingandUsingPopulationProjections.aspx, 2009-05-07 51

Of the eight variables in the regression analysis population density, urbanisation, imports, visitors from dengue areas, deforestation rate and the climate indicator had a positive effect on dengue rate. Population density and the climate indicator had lower dengue rate ratios as expected; since the two variables had much broader value ranges than urbanisation, imports and deforestation rate. Health expenditure and improved water sources had a negative effect on dengue rate as anticipated, since these would be expected respectively to improve public awareness and availability of healthcare, and to reduce the availability of breeding sites for mosquitoes.

Since New Zealand does not yet have cases of epidemic dengue, a hypothetical number of cases estimated by the Ministry of Health in 1997 was used for the North Island scenarios. This can then be used to illustrate how much climate change and population growth can enhance the risk of epidemic dengue to the North Island. The dengue rate ratio for the climate variable predicted a fourfold increase of dengue cases (from 1010 to 4169 cases) for a projected vapour pressure of 17.7hPa in 2070. The hypothetical number of 1010 cases is regarded as very modest and a fourfold increase by 2070 does not seem too unlikely considering the disease ability to rapidly increase in distribution and incidence over the last 40 years (Wilcox and Colwell 2005, Phillips 2008). A dengue outbreak of this size would cost New Zealand an estimated 1 billion New Zealand dollars, coming from losses in tourism, medical costs and productivity due to employee absence (based on the Ministry of Health 1997 figures).

A warmer climate will make the North Island much more suitable for dengue transmission, partly because the new conditions will make it easier for other dengue vectors to establish. According to de Wet et al. (2005) the North Island’s climate is already suitable for A. albopictus, the fact that it has not established there yet is good fortune for New Zealand. A study by Kobayashi et al. (2002) showed that A. albopictus distribution in Japan followed conditions with a mean annual temperature warmer than 11°C and a mean temperature of the coldest month warmer than −2°C. Cold-adapted strain of A. albopictus has also established in North and Latin America, and Europe where it can survive monthly mean temperatures as low as -2°C29. New Zealand has at present a mean annual temperature of 10.1°C and a mean temperature for the coldest month of 0.86°C (Leathwick et al. 2002), which is too cold for A.

29 European Centre for Disease Prevention and Control 2009. http://ecdc.europa.eu/en/files/pdf/Publications/0905_TER_Development_of_Aedes_albopictus_risk_maps.pdf , 2009-07-24 52 albopictus according to Kobayashi et al. (2002). However, the temperature is expected to increase by 2°C by 2090, indicating that most of New Zealand will be suitable for A. albopictus in the future. Furthermore, the La Niña conditions New Zealand is experiencing at the moment are expected to last for 20-30 more years (Ministry for the Environment 2008). La Niña conditions have been correlated to monthly reports of dengue cases in the South Pacific (Hales et al. 1996, Hales et al. 2003). This increases the risk of dengue establishment even further.

For A. polynesiensis a warmer climate would allow for establishment in the northern coastal areas, but a long-term establishment for A. aegypti does not seem likely (de Wet et al. 2005). Although, Martens et al. (1997) argue that Aedes mosquitoes live mainly indoors and therefore will ambient temperature have less of an effect on their survival. However, this is not the case in Europe where the potential dengue vector, A. albopictus, is now slowly expanding its distribution range after its introduction into the region more than of decade ago. The projected temperature increase will also benefit the dengue virus as the virus does not tend to survive below 12-13 C° (Martens et al. 1997). A possible scenario would be that the temperature limit for the virus will most likely hinder a year-round transmission of dengue in the North Island, and there will have to be an influx of new viruses for each transmission period.

A warmer climate will also increase vector capacity of the Aedes mosquitoes. Vector capacity for dengue vectors is a function of 1) vector density in relation to human hosts, 2) the human biting rate, 3) duration of the extrinsic incubation period for the dengue virus, and 4) daily survival rate for the vector (ECDC 2007). Warmer water temperatures allow mosquito larvae to mature much faster, which would eventually result in an increased vector density. Adult female Aedes mosquitoes feeds more frequently and digests much faster in warmer temperatures and thereby increase the human biting rate (Martens et al. 1997, Githeko et al. 2000). The EIP is also affected by a change in climate, warmer temperatures has a non-linear result on the EIP (Watts et al. 1987). Population density also has the potential to affect vector capacity since it will increase the number of available hosts and feeding opportunities (Gubler and Meltzer 1999, Jones et al. 2008).

Aedes notoscriptus, which is the second most abundant mosquito species in the North Island (Derraik and Slaney 2007), has a low vector competence for the dengue virus. Under current

53 conditions it is not likely that this species will be involved in dengue transmission (Weinstein et al. 1997). However, the warmer climate that is expected for the North Island (Ministry for the Environment 2008) together with a higher population density will increase A. notoscriptus vector capacity. Whether or not this can compensate for the low vector competence and allow for dengue transmission on the North Island without the establishment of other dengue vectors is difficult to say, but nevertheless it will increase the risk for epidemic dengue.

The projected increase in population density for the North Island had much less of an effect on the number of dengue cases than the projected increase in vapour pressure. Previous studies on population density and EIDs showed the population density was an important predictor of EID events (Jones et al. 2008), and past increase in dengue and DHF has been highly connected to population growth (Gubler and Meltzer 1999). The dengue rate ratio for population density predicted a very modest increase of 27 cases (from 1010 to 1037 cases) for 2070, despite a considerable 1/3 rise in population density (from 26 to 39 persons/km2). This results indicates that an increase in population density alone does not pose that big of a threat for the North Island. However, it is important to keep in mind the different variables can act in combination leading to synergistic or non-linear effects on disease transmission (Patz and Confalonieri 2005). A growing population growth will also consequentially lead to intensification in deforestation (Norris 2004, Sutherst 2004, Patz and Confalonieri 2005). The results from the dengue regression analysis proved that deforestation has a positive effect on dengue rate. Although, I would suggest that this result is not due to the deforestation act per se, but that deforestation works as an indirect measure of population growth.

Population growth will also lead to urbanisation, which can affect the distribution and incidence of dengue (Norris 2004, Sutherst 2004, Patz and Confalonieri 2005). The urbanisation variable in the analysis showed to have a positive effect on dengue rate. New Zealand is one of the world’s most urbanised countries in the world with 86% living in urban areas. The biggest population increase in New Zealand occurs in the cities of the North Island and the projected population growth will most likely take place here30. Although, it is uncontrolled urbanisation that poses the biggest threat since it often result in the concentration of people without the essential infrastructure that is needed for safe distribution and storage of water (Sutherst 2004, Patz and Confalonieri 2005). Urban areas with inadequate water supply

30 Statistics New Zealand 2008. http://www.stats.govt.nz/NR/rdonlyres/4980B3EA-6F91-4A09-BB06- F7C1C2EA3403/0/quickstatsaboutnzspopanddwellingsrevised.pdf, 2008-09-30.

54 systems have been proved to support dengue transmission; containers used to collect rainwater are an example of a perfect breeding site for mosquitoes (Norris 2004, Patz and Confalonieri 2005). This evidence further supports the result for the improved water sources variable in the dengue regression analysis. Of New Zealand’s population, 97%31 has access to improved water sources (e.g. household connections, boreholes, public standpipes and protected dug wells) and this number will most likely not decrease in the future. However, recent massive expansion of industrial scale dairy farming that is using irrigation to expand into previously unsuitable areas is greatly expanding rural habitat for Culex quinquefasciatus (a widespread vector of the West Nile virus, the Ross River virus and several encephalitis diseases) and to a lesser extent A. notoscriptus. Many New Zealand farmers also use old tyres (when no longer safe for transport) to hold down the plastic sheeting that is used to cover winter stores of animal feed (especially silage). These tyres has been shown to be perfect containers for mosquito breeding as they hold both water and heat and have sufficient cover to contain evaporation (McIntyre M. pers. comm. 2009).

Increased travel has the same potential as trade to spread pathogens and vectors all over the world (Daszak et al. 2000, Patz et al. 2004, Sutherst 2004, Patz and Confalonieri 2005, Staples 2007) and the visitor’s variable in the analysis proved to have a positive effect on dengue rate. Over the last 10 years New Zealand’s short-term departures increased by almost 60%, where nearly half of the trips are going to Australia and 13% to Pacific Islands, all countries with epidemic dengue (Statistics New Zealand 2008). The purpose of most of the trips to the Pacific Island countries is to visit family, reflecting New Zealand’s large Pacific Island community (Statistics New Zealand 2008). This results in that the majority of imported cases of dengue to New Zealand are of Pacific Island ethnicity32 travelling from one of the Pacific Island countries33.The number of imported cases per year also corresponds to outbreaks in the Pacific Island countries34. A large part of the Pacific Island community lives in Auckland, which will concentrate the risk to Auckland (Kay 1997). The rise in international departures follows the rise in imported dengue cases; still the dramatic rise in

31 The World Bank 2008. http://go.worldbank.org/U0FSM7AQ40, 2008-09-05. 32 New Zealand Public Health Observatory 2008. http://www.nzpho.org.nz/NotifiableDisease.aspx, 2008-10- 11. 33 Public Health Surveillance 2008. http://www.surv.esr.cri.nz/surveillance/NZPHSR.php, 2008-09-11.

34 World Health Organization 2008. http://www.who.int/globalatlas/default.asp, 2008-09-12. 55 dengue cases in 2001 and 2007 can not entirely be due to increased travel or outbreaks in the Pacific Island countries according to Kahn et al. (2001).

Imports proved to have a positive effect on dengue rate. This result is supported with the theory that trade has the potential to spread pathogens and vectors to new areas (Daszak et al. 2000, Patz et al. 2004, Sutherst 2004, Patz and Confalonieri 2005, Staples 2007). Projections were never attained for imports to the North Island in this study, although the volumes are likely to keep increasing. Imports to New Zealand and the North Island are however, very likely to keep increasing. Imported goods to New Zealand by sea increased by 52% between 1997 and 2007, and this trend should continue given the increase in demands from the expected population growth. New Zealand gets the majority of its imports from Australia35, which was also the origin of 28% of the intercepted non-native mosquitoes between 1929 and 2004 (Derraik 2004). Another major origin for both imports and exotic mosquitoes is Japan. Japan has been the main source of exotic mosquitoes since the 1990s (Derraik 2004). The bulk of the imported goods from Australia and Japan consist of machinery and used cars36, which is also the main mode of invasion for A. aegypti, A. albopictus and A. polynesiensis into the North Island (Derraik 2004).

7.1 Recommendations and lessons to be learned A better understanding of the complex linkages between environmental and social factors and dengue emergence is needed for effective policy development. There has been a very rapid spread of dengue in recent decades regardless of our increasing knowledge of the mechanisms behind dengue emergence. The previous failures to appreciate these complex processes between systems have hindered sustainable vector control solutions from being implemented. In vector-borne disease management both social and behavioural resources are increasingly more recognised as important tools (Spiegel et al. 2005).

One of the most pressing dengue management issues for New Zealand is to increase awareness about the disease. Gubler and Meltzer (1999) have proposed that recent increase in dengue and DHF was a result of poor surveillance, control programs and a general

35 Statistics New Zealand. 2009. http://www.stats.govt.nz/products-and-services/Articles/Australia-Trade- Nov02.htm, 2009-04-18.

36 Statistics New Zealand. 2009. http://www.stats.govt.nz/products-and-services/Articles/Australia-Trade- Nov02.htm, 2009-04-18.

56 complacency about the disease. A lack of knowledge of dengue symptoms among general practitioners and slow diagnostic services can allow a dengue outbreak to pass unnoticed. General education for the public about the symptoms and vector protection is an essential tool in disease prevention, especially for dengue that lack curative drugs or vaccines (Campbell- Lendrum et al. 2005). Vector protection is an easy and economic way to manage dengue and is done by changing settlement patterns or human behaviour. Things such as screening of windows and doors and improve household management of stored water can decrease human- vector contact (WHO 2004, Campbell-Lendrum et al. 2005). Screening windows and doors and making sure that stored water is inaccessible for mosquitoes might not be an urgent issue for New Zealand at the moment, but it is something to consider for new developments.

An issue that was brought up as early as 1997 by the Ministry of Health was to improve existing surveillance in order for an early, cost-effective and successful response to the appearance of new dengue vectors. New Zealand’s biosecurity surveillance system is under a lot of pressure as travel and trade increase. Just recently, in September 2008, MAF Biosecurity New Zealand announced in their “Surveillance Magazine” that they needed input on their Biosecurity Surveillance Strategy “from everyone who has an interest or role in biosecurity surveillance” (Clift in Surveillance Magazine 2008:3). They have recognised that New Zealand’s biosecurity surveillance can be improved by “working more collaboratively, effectively and efficiently with other government agencies, local government, industry groups, and other participants in the biosecurity surveillance system” (Clift in Surveillance Magazine 2008:3) and the development of a strategy for a cross-sector national surveillance system is now put in to action and is to be released on the 2nd of July 200937.

Both surveillance and the elimination of vector breeding sites will be most successful when they are developed in a participatory manner, starting at the neighbourhood level (Campbell- Lendrum et al. 2005, Wilcox and Colwell 2005). Using input from the community and cross- sectoral collaborations are a difficult task and there is much to be learned from New Zealand’s ongoing development of a cross-sector national surveillance strategy. MAF Biosecurity New Zealand also publishes the magazine “Biosecurity”, this magazines purpose is to raise public awareness and is written for a non-technical audience38. Studies of dengue control in the

37 MAF Biosecurity New Zealand 2009. http://www.biosecurity.govt.nz/surveillancestrategy, 2009-05-14

38 MAF Biosecurity New Zealand 2009. http://www.biosecurity.govt.nz/publications/biosecurity- magazine/index.htm, 2009-05-15 57

Caribbean have shown that even if knowledge of vector control in the community has increased, it is not being implemented. People believe that vector control is the government’s responsibility and consequently do not take action (Campbell-Lendrum et al. 2005). Early involvement of the community in surveillance and vector control is crucial.

A study by Wilcox and Colwell (2005) was sought out to identify the critical elements of successful control programs of dengue in the Americas and Asia. Adaptability, of vector control programs and public education the changes in vector behaviour and the capacity to learn from experience was found to be the key to successes. Development and sustainable implementation of adaptive vector control programs requires interdisciplinary cooperation and is hold back by the naive view that dengue is disconnected from a social-ecological context and will have a linear response to social and environmental change (Wilcox and Colwell 2005). Furthermore, it is clear from the biocomplexity framework that to solely manage dengue, with the ultimate goal to eliminate the disease, is ineffective if not futile. What its shows us is that it is better to adapt and search for opportunities in a very uncertain world (Waltner-Toews 2001).

7.2 Limitation of the study There are five limitations that need to be acknowledged and addressed regarding this study. The first limitation concerns the availability of data especially for the variables in the dengue regression analysis. Many countries do not keep detailed statistics e.g. the imports variable would have benefitted from only including imports from dengue risk areas. The data that was available was also on a fairly high level, the variables in the analysis are whole country/area surrogates for local factors which may affect both the vectors, virus and human exposures.

Data limitation was also the reason to why only two scenarios were being drawn for the North Island and why a hypothetical number of dengue cases estimated in 1997 was used. Official population projections have only been drawn up until 2061 for New Zealand and to 2031 for the North Island. This resulted in the decision to make my own population projections for the North Island which brings us to the second limitation namely that of accuracy of projections.

It is impossible to say how accurate the population projections are for the North Island , but one may mention that the official population projections for 2061 comes in 9 series, ranging

58 from 4.7 million to 6.5 million people39. This illustrates how uncertain population projections are and that human inability to predict the future plays a far greater limitation than that of my own future estimations. Furthermore my predictions are calculated based on the official data and all calculations are well documented. Nonetheless the inability to accurately forecast the future will always limit the accuracy of studies attempting to predict future outcomes.

The third limitation is the reliability and the quality of the different countries statistics obtained. Countries can for various reasons have inaccurate data ranging from limited funds to political agendas. There are of course no easy way to measure the accuracy of country statistics but it is reasonable to believe that discrepancies that may exist is not off such magnitude to significantly alter the results of this study.

The fourth limitation is that of scenarios based on the dengue rate ratio from the regression analysis. These scenarios are future forecasts in which variables have been examined in isolation to estimate the weighted risk any one variable has on dengue emergence and not to model reality. The sheer complexity of the mechanisms behind dengue emergence, such as the fact that it is a cross-scale process between systems including feedback loops within space and time and non-linear responses further weakens any attempt to predict future risks. Furthermore, my second and third limitations (the availability, reliability and quality of the data used for the analysis) will affect the accuracy of the regression analysis outcome.

The fifth limitation concerns personal limitations, especially limited statistical skills. To limit the effect of my personal statistical skills or lack thereof, I worked closely with James Stanley at Wellington School of Medicine and Health Science, University of Otago statistical expert who verified all data. My lack of skills will have more a bearing on the amount of results I could obtain in the set of time available for this study than the quality of results achieved.

8. Conclusions

The results of the case studies show that several factors associated with global changes and globalisation are of importance for dengue distribution world-wide. Factors such as the rapid travel and interconnectiveness in larger and larger urban environments contributes to the pathogen being introduced (by humans) into risk environments. Factors such as changes in

39 Statistics New Zealand 2009. http://www.stats.govt.nz/products-and-services/table-builder/pop- projections.htm, 2009-05-16

59 climatic conditions, land use changes and changes within the urban environment, and in trade conditions (contributing to the introduction of mosquitoes into a country/area) define vector prevalence and abundance. New Zealand being highly urbanised, with distinct differences in seasonal conditions and surrounded by dengue endemic regions will thus be affected by several of these risk factors, including climate changes, travel and trade from dengue infected areas or areas where the main vectors are prevalent, and continuous urbanisation.

In the case of the North Island scenarios, the projected increase in vapour pressure proved to have a significantly bigger effect on the risk of dengue than the projected increase in population density. The dengue rate ratio for the climate variable predicted a fourfold increase of a hypothetical number of dengue cases (from 1010 to 4169 cases) for the projected vapour pressure of 17.7hPa in 2070. While a projected 33% increase in population density merely gave an additional 27 cases.

An issue that needs attention is the possibility of A. notoscriptus being involved in dengue transmission in the future. So far laboratory studies in New Zealand showed it to be a capable experimental vector of dengue virus but probably not likely to be involved in dengue transmission on the North Island under current conditions (Weinstein et al. 1997). However, there is increasing evidence that A. notoscriptus is an important dengue vector in urban environments in other areas (Derraik and Slaney 2007). An increase in average temperatures in New Zealand might not do anything for A. notoscriptus vector competence, but it will increase its vector capacity. If this can compensate for the low vector competence or not needs to be examined.

New Zealand’s isolation and its effective biosecurity are the two main reasons why it has no mosquito borne diseases even though it only lacks the pathogen component for a complete mosquito borne disease cycle. However, globalisation and increase connectivity with the rest of the world decreases their isolation and increase pressure on their biosecurity system. Considering this it is only a matter of time before a more suitable vector gets established, and New Zealand will experience their first dengue epidemic. The magnitude will depend on New Zealand’s actions today and how well prepared they are for that day.

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Appendix

pyar 6837985 1.02E+09 28801969 28051785 81682682 38622333 26122063 15458400 10568208 468540245 437781370 147306500 661549655 980762000 502395025 164890233 118014024 469976638 157381948 193465189 104876801 -

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2005) ases (1990 ases C

mate 39 93 115 140 146 151 162 177 195 209 223 244 250 262 266 271 300 306 328 331 340 Cli variable

0.9 1.1 3.1 1.0 0.8 2.4 0.3 1.0 1.6 4.5 0.4 3.1 1.5 0.2 0.0 3.0 0.0 2.8 0.9 3.9 2.0 ------rate tation tation Defores

0.05 0.01 0.33 0.25 0.03 0.16 0.36 0.02 0.00 0.72 0.23 0.55 0.02 0.01 0.02 0.16 0.18 0.37 0.21 0.03 0.00 isitors V variable

% with

77 92 69 78 71 86 84 21 42 94 96 59 67 41 65 46 52 79 80 70 access)

sources ( Improved water

24 57 32 25 48 23 20 56 39 27 93 18 24 26 31 39 37 55 25 112 (% of GDP) Imports

Country specific data

7 7 4 66 11 87 80 13 13 18 21 42 54 123 107 260 212 US$) Health person (2000 (2000 person expenditure per

8 19 58 31 52 76 62 56 14 40 51 43 23 34 16 11 19 36 33 51 (% of population) Urbanisation

2 3 2 3 8 59 12 61 59 34 56 64 56 13 52 13 31 299 106 255 density Population (persons/km2)

4030 Area Area 30350 17200 24670 25680 (km2) 823290 446300 155360 566730 995450 569140 743390 386850 2381740 1759540 1214470 1101000 1030700 2376000 1266700

966150 427374 660513 1800123 1753237 5105168 9206656 2413896 1632629 7375877 9836372 6554800 29283765 27361336 41346853 61297625 63720095 31399689 10305640 29373540 12091574 Population

iland ndi Country Lesotho Algeria Namibia Morocco Libyan Arab Jamahiriya Tunisia South Africa Ethiopia Mauritania Botswana Egypt Swaz Sudan Niger Rwanda Kenya Zambia Zimbabwe Cape Verde Buru Reunion 70

1241577 6525222 17831477 80073157 42523396 58179769 18451481 20436435 57270416 1.875E+09 724286144 239652722 122840790 510370883 242369394 126586137 369436868 178843593 176486203 151223332 155790457 210811718

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

669 598 661 559 556 549 548 545 536 521 526 488 496 414 454 399 409 396 398 363 394 350

0.3 1.0 4.0 3.0 0.6 1.7 0.1 1.1 0.4 0.4 2.1 0.7 0.9 0.3 0.9 0.8 0.2 0.5 0.3 0.0 0.4 ------

.05 0.11 0.00 0.05 0.03 0 3.19 0.03 0.00 0.01 0.58 0.09 0.05 0.02 0.04 0.04 0.00 0.05 0.05 0.08 0.08 0.06 0.04

84 56 52 50 88 53 60 60 51 86 42 49 29 43 45 42 52 41 69 48 100

34 19 43 36 79 27 23 43 63 32 61 31 23 40 87 24 37 41 61 36 79

6 8 9 32 13 21 12 15 16 68 12 15 16 17 24 216 392 143

77 48 35 41 51 30 37 52 43 21 46 26 12 23 38 16 27 14 46 40 17

4 6 6 8 32 19 92 31 28 36 26 15 41 11 51 35 129 169 568 128 117 117

460 2030 54390 96320 10000 28050 94080 257670 465400 910770 245720 623000 885800 197100 581540 273600 192530 101000 2345410 1259200 1220190 1246700

77599 407826 1114467 5004572 7677549 3636236 2657712 1153218 1277277 7911634 9451458 9736904 3579401 14978295 45267884 31898180 23089804 15148087 11177725 11030388 13175732 117196622

Gabon Cameroon Togo Congo, Democratic Republic Nigeria Seychelles Guinea Central African Republic Mauritius Liberia Gambi United Rep. of Tanzania Uganda Madagascar Equatorial Guin Chad Mali Burkina Faso Angola Malawi Senegal Eritrea 71

2146462 72095100 20613100 10881823 48055055 37340211 76318702 51218663 95362016 78590393 69049696 8237437.9 96021.333 2762666.7 272514014 110649379 254536491 304447840 108019375 276878064 377846255 984471549 354998873

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

16 22 29 34 35 44 57 58 59 60 670 703 751 759 762 768 770 826 834 842 889

0.3 0.7 0.5 5.7 1.0 2.0 2.3 0.1 0.4 0.8 0.6 2.8 1.9 0.0 0.1 0.0 0.0 0.3 0.5 0.0 0.0 ------

0.03 0.09 0.05 0.02 0.02 0.00 0.00 0.12 0.00 0.01 0.00 0.00 0.00 0.80 0.13 0.79 0.00 0.01 0.09 0.00 0.00 0.11 0.01

40 81 57 58 90 22 71 64 79 63 70 66 80 92 21 58 90 93 80 100

39 29 43 37 38 33 46 56 28 55 67 47 54 64 61 30 20 49 70

0 7 8 0 0 11 11 24 16 50 15 24 25 17 38 10 31 62 102

28 51 35 29 28 32 43 42 81 37 57 57 36 66 21 28 38 62 60 53 46

9 1 9 22 63 46 11 50 84 29 61 15 25 27 43 56 38 71 140 277 462 114 184

960 413 374 1861 71620 28120 23180 28200 69490 786380 627340 318000 227540 110620 341500 191800 652090 139960 425400 120410 469930 1566500 1628550

1 7990 8319 600 134154 514840 680114 172667 4505944 128 6915586 6751211 3003441 2333763 4769919 3201166 5960126 4911900 4315606 17032126 15908531 1902 17304879 23615391 61529472 22187430

Mozambique Sao Tome and Principe Sierra Leone Guinea Bissau Comoros Somalia Ivory Coast Ghana Djibouti Benin Congo Saint Helena Mayotte Mongolia Kyrgyz Republic Armenia Afghanistan Tajikistan Uzbekistan Iran Korea, Dem. People's Rep. of Georgia Turkmenista n 72

9797129 9476957 6811225 33833004 71290337 10726150 57308171 94275800 45469560 13810565 35984325 41426837 1.031E+09 2.091E+09 125349861 250193474 268612096 368927159 341716094 733987295 101907045 248060011 331220816

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

81 82 87 91 63 70 81 106 110 187 213 281 323 410 418 585 699 338

0.1 0.2 1.8 2.0 1.3 1.4 0.2 0.0 0.5 0.8 4.7 0.7 0.0 0.3 0.0 3.1 0.0 1.3 0.0 - - - - -

0.28 0.09 2.41 0.16 0.00 0.94 1.51 2.22 0.04 4.74 0.04 1.78 3.74 2.04 0.00 1.14 0.13 0.84 0.00 0.12 17.29 22.94 341.72

82 97 92 96 89 71 84 87 71 82 78 100 100 100 100 100

23 50 46 34 75 33 51 55 72 41 32 19 64 45 37 65 45 30 141

0 33 55 30 25 50 12 221 543 174 548 747 407 500 722 240

63 52 98 51 68 77 78 68 85 88 91 94 33 78 25 71 56 12 100 100

7 6 84 95 85 49 50 73 54 34 32 119 465 350 170 887 272 161 6112 15096

0 630

690 28.2 9240 1042 82660 17820 88240 98730 1023 21640 11000 83600 769 183780 437370 770880 527970 309500 143000 2699700

7256 670384 612321 592310 863160 425702 7834366 2114563 4455646 3581761 5892238 2841848 2249020 6369190 2589177 64466859 15637092 2135 45874206 16788256 15503751 20701301 23057947 130715735

Turkey Azerbaijan Kuwait Syrian Arab Republic Iraq Jordan Korea Cyprus Lebanon Bahrain Israel Qatar Pakistan United Arab Emirates Yemen Timor East Oman Hong Kong Macao Kazakhstan Occupied Palestinian Territory Taiwan Nepal 73

771200 5032979 8857420 4140688 78861064 59911600 82140311 70797450 982453.33 4397594.2 2.016E+09 1.967E+10 1.558E+10 2.127E+09 3.192E+09 1.161E+09 1.204E+09 313632964 709890872 295584000 192032429 350678755 943268391

0 0 0 0 0 0 34 315 42022 4057 2590 10780 89512 19881 60470 52752 67486 61181 647115 246857 2 123211 969677 1034454

21 23 27 30 32 404 735 824 840 720 978 877 128 227 167 531 870 856 709 749 997 854

0.8 1.4 1.4 1.9 2.5 1.6 0.6 0.5 0.6 1.7 0.0 0.3 2.2 0.0 0.0 4.1 0.1 0.2 0.0 ------

0.67 0.88 0.54 0.03 0.01 0.29 0.05 0.12 0.26 0.50 0.75 6.34 1.85 0.44 4.67 1.77 0.00 1.34 1.65 0.00 29.82 21.46 12.81

99 74 93 77 78 81 63 72 74 87 29 98 44 64 93 96 100 100 100 100 100

9 2 45 22 28 13 18 46 43 28 48 47 91 35 52 68 51 36 29 31 189

0 40 31 16 12 20 21 10 33 31 19 438 348 147 214 778 2901 2847 1550 3277

79 27 23 39 56 16 28 31 70 65 34 23 27 16 59 20 23 92 61 75 34 100

3 60 10 12 67 68 67 21 17 15 34 346 132 327 286 110 243 231 863 115 131 1021 5589

1570 670 300 468 5270 1400 47000 64630 364600 130170 657550 298170 176520 328550 230800 325490 510890 100250 304590 304280 9327420 2000000 2973190 181

3769 44475 61403 48200 314561 553589 258793 274850 4928817 37 513 4424841 19602060 44368180 18474000 72552994 12002027 21917422 75230373 58954274 126015688 973574841 132960583 199491943 1229582029

Brunei Darussalam Japan China Saudi Arabia India Bangladesh Bhutan Myanmar Lanka Sri Indonesia Philippines Cambodia Malaysia Lao People's Democratic Republic Viet Nam Maldives Singapore Thailand Andorra Iceland Finland Norway Faeroe Islands 74

22775719 39362333 59281382 84523341 31715836 57100041 85746714 67547849 59930100 6749503.4 2.348E+09 1.307E+09 113636364 141235371 161014289 164526315 127687603 614390695 801555780 168692949 164133247 131513068 936513760

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

35 37 37 38 41 42 50 51 51 52 53 54 44 45 47 47 47 49 57 61 62 64 64

0.1 0.4 0.4 0.4 0.4 0.0 0.6 0.1 0.1 0.2 0.3 0.1 0.2 0.8 0.3 0.2 0.2 0.7 0.8 0.6 2.6 -

0.20 0.70 7.55 2.99 0.34 0.02 0.24 0.27 0.39 1.73 5.14 0.28 0.11 3.74 0.07 1.03 0.07 1.59 0.02 0.98 0.36 4.12 12.48

95 99 97 97 93 97 99 100 100 100 100 100 100 100 100 100 100 100 100

25 78 35 34 52 63 51 56 66 58 40 28 78 43 36 28 65 53 54 28 67 106

55 84 29 108 205 212 228 243 100 346 121 2294 2287 2500 2324 3695 3049 1788 1586

84 69 69 74 66 62 85 73 73 70 73 62 67 56 42 83 67 45 65 68 89 59

9 34 22 40 49 78 57 97 72 86 74 54 178 111 133 126 163 125 234 128 114 242

390 0 2590 42 40000 62050 25430 62680 48100 77270 82450 51200 42390 32870 68890 89870 410330 2074 304420 579350 348770 110630 241930 1638995

421844 1423482 7102273 8827211 2460146 1982240 3568753 5359170 7980475 3705086 5282709 4221741 8219567 3745631 10063393 10282895 38399418 50097236 10543309 81692478 10258328 58532110 146750268

urg

any Russia Estonia Switzerland Sweden Latvia Belarus Macedonia Lithuania Slovakia Czech Republic Austria Poland Bosnia Herzegovina Luxembo Ukraine Denmark Serbia and Montenegro Germ Republic of Moldova Hungary Bulgaria United Kingdom Ireland 75

667 610.7 61600 47072 518400 449600 898864 432480 896762 72544650 50479480 31855356 6130290.7 13450. 550933.33 100778.67 1135 1365114.7 250894493 357166229 171692300 163268651 936419532 643813950 9138 162619526

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 65 66 67 68 71 72 73 78 88 94 12 107 152

0.1 0.4 0.9 0.1 0.2 0.4 0.4 1.9 1.2 1.3 0.0 0.3 0.0 0.0 0.0 0.0 -

3.91 0.27 1.34 2.18 1.06 0.31 4.19 1.01 2.58 2.20 2.48 3.49 0.00 0.00 0.00 4.90 0.00 0.00 0.00 0.00 0.00 50.39 49.85

98 99 97 97 100 100 100 100

56 26 71 55 42 24 58 25 22 36 95

0 67 314 788 956 2019 1543 2317 1261 1029

97 55 40 67 53 92 16 92 75 60 75 50 76 81 100

0 0 94 83 81 99 81 26 463 338 115 106 194 111 215 468 720 124 736 1197 1911 16615

60 78 320 116 242 160 572 0.44 1.95 33880 30230 55920 27400 20140 91500 61020 237500 128900 550100 499440 294110 341700

841 2942 6299 27030 34433 32400 28100 56179 70976 85320 56048 383143 4534041 3154968 1990960 15680906 22322889 10730769 10204291 58526221 40238372 57116350 10163720

Netherlands Romania Greece Belgium Croatia Albania France Slovenia Spain Italy Portugal Malta Svalbard Gibraltar Holy See Liechtenstei n Monaco San Marino Guernsey Isle of Man Jersey Saint Pierre and Miquelon Greenland 76

704000 2985291 4654526 1156363 3768500 2398660 1598221 991431.8 40513733 225402.67 6503493.3 5951669.3 303722.67 158869.33 131738.67 1590933.3 4.382E+09 1.504E+09 481332396 176117062 131253061 134071040

0 0 0 0 0 3 7 64 96 520 414 118 779 511 342 4540 4204 28697 13464 17873 13642 274465

40 481 599 709 760 763 139 777 758 701 495 680 658 263 895 664 828

0.1 0.5 0.3 0.1 0.7 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 - - - - -

7.99 9.04 0.00 3.97 29.90 0.67 5.23 9.69 7.87 24.56 38.49 20.93 33.04 57.46 32.96 74.41 470.11 619.70 192.90 184.26 281.54 2 300.02

99 90 91 98 88 94 91 93 97 54 91 100 100

34 13 17 56 30 80 27 61 70 44 68 130

29 167 998 438 346 238 109 230 2106 4784

79 89 78 75 51 81 35 33 74 48 29 60 31 48 100 100

3 33 97 81 30 29 48 10 233 100 234 298 246 173 124 169 164 294 552 1239

810 50 430 153 800 102 102 260 440 610 340 180 10830 10010 27560 22 48380 109820 9093510 9158960 1943950

9929 8234 14088 18983 61964 44000 72273 99889 99433 406468 371979 186581 290908 235531 149916 2532108 8203316 8379440 30083275 11007316 94030474 273868500

slands Canada Turks and Caicos I Guadeloupe Martinique British Virgin Islands Netherland Antilles Anguilla Montserrat States United of America Bermuda Cayman Islands Cuba Jamaica Bahamas Haiti Antigua and Barbuda Mexico Belize Lucia Saint Dominican Republic Grenada Aruba 77

34112 297072 168288 1830644 4523469 691777.5 45015346 94461561 77724265 20502216 59850335 94011595 59235933 1718805.3 1149467.3 30426.667 1018.6667 23946.667 161029.33 225045.33 1367643.8 170795962

0 0 0 0 0 0 0 0 449 172 369 1693 5932 6821 63606 24937 20069 81631 103600 125544 209908 137572

62 25 448 634 625 865 481 646 313 656 784 5 803 626 739 762

0.2 1.3 1.6 1.5 0.3 1.4 3.1 0.4 1.4 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 ------

6.85 3.63 6.75 4.16 4.71 0.00 0.00 0.00 0.00 1.48 0.00 0.72 59.42 25.39 11.83 47.37 32.18 13.59 40.10 13.02 54.12 233.89

85 74 74 90 99 97 92 76 97 55 100

72 6 79 30 38 69 48 41 55 97 56 44 95

88 58 84 65 380 233 280 160 258 559 271

91 33 70 63 44 56 44 54 10 35 89 43 57 40

8 1 96 38 98 40 53 73 78 55 51 307 166 285 293 250 150 387 657 422 501 106

21 47 10 26 350 260 750 390 430 260 274 810 34.6 5130 8870 236.7 74430 20720 51060 108430 121400 111890

64 2132 1902 1497 43236 71842 18567 10518 10064 14065 85478 107425 114415 282717 2813459 5903848 4857767 1281389 3740646 5875725 3702246 10674748

United States States United Virgin Islands Kitts Saint and Nevis Commonwea of lth Dominica Panama Guatemala El Salvador Saint Vincent Nicaragua Trinidad and Tobago Barbados Puerto Rico Honduras RicaCosta Niue IslandsCook Nauru Norfolk Island Pitcairn Tokelau Tuvalu and Wallis Futuna Kiribati 78

94087 36032 2411322 6238835 2901651 2763526 16 1559728 3613597 60439400 80996964 12502490 51848041 11773984 843160.42 1250666.7 3235814.4 918933.33 318933.33 1984874.7 298552320 237585489 572789771

35 4 0 0 56 67 34 497 587 789 636 3983 1730 1505 1998 1526 1418 5109 2186 81 16980 27179 35477

7 4 3 68 113 598 731 548 137 591 686 211 564 750 333 60 117 347 199 193 767 1006

0.2 0.3 0.4 1.6 0.5 0.2 0.0 1.4 0.0 0.0 0.0 0.0 1.8 0.4 0.0 0.4 0.0 0.1 0.4 ------

6.94 0.26 0.16 3.21 2.63 3.43 7.41 4.14 8.25 4.45 6.61 0.00 6.62 4.32 3.32 6.22 89.74 86.30 20.75 11.07 46.81 20.04

92 60 90 90 92 95 97 69 39 98 47 90 89 10 100 100 100 100

29 65 50 20 57 60 51 30 63 70 26 21 30 11 113

40 28 55 62 82 87 48 383 221 579 555 676 1085 1705

85 92 15 14 87 68 21 22 24 23 90 61 47 87 72 53 91 85 89 29

2 0 4 14 14 11 15 61 11 43 43 62 19 20 13 279 151 287 293 135 170

540 180 720 460 700 200 460 2830 3660 27990 12190 18270 12173 18280 267710 452860 175020 748800 196850 7682300 2736690

2252 52698 97483 78167 57433 19933 150708 389927 181353 172720 105880 202238 781406 225850 124055 735874 3777463 5062310 3240503 18659520 14849093 35799361

nds

New Zealand Guam Solomon Islands Papua New Guinea Australia Marshall Isla Vanuatu Western Samoa Federated of State Micronesia Tonga Northern Mariana Islands New Caledonia Fiji American Samoa Palau French Polynesia Falkland Islands French Guiana Uruguay Chile Argentina Guyana 79

6826412 81113088 2.688E+09 639266187 370768000 188692756 394959028 126505164

9831 28061 82197 20999 548139 459838 107529 3610867

546 691 536 734 548 439 242 260 Food and Agriculture Organization of the the of Organization Agriculture and Food

2008,

0.6 0.6 0.1 0.9 1.6 0.1 0.5 0.0 ------Indicators

50 5.63 0.81 0.46 0.62 2.35 2. 1.45 1.34 World Development Development World

86 91 90 90 80 61 78 77

10 39 21 20 29 52 17 28 East Asia, World Bank’s -

92 65 114

71 80 88 71 59 54 70 60

3 7 20 26 36 43 13 19

Pan American Health Organization, WHO’s regional office for South

156000 882050 276840 397300 8459420 1109500 1280000 1084380

United Nations Statistics Division Statistics Nations United 426651 5069568 7906573 23173000 11793297 39954137 24684939 167997992 and

Suriname Data sources: WHO’s DengueNet, United Nations Venezuela Brazil Ecuador Colombia Peru Paraguay Bolivia

2 0.4 0.7 0.2 0.2 2.4 1.8 1.4 0. 0.3 0.5 2.7

Total rate

4 0.2 0.2 2.4 1.8 1.4 0.2 0.6 0. 0.7 0.2 0.3 0.5 2.7 Rate per 100000 per Rate

0.3 0.6 0.1 0.2 1.7 1.9 1.2 0.2 0.2 0.4 1.8 Other

3780387 3814597 3834607 3857187 3879947 3947507 4026897 4086947 4133097 4184177 4227697 3 675 800 3 675 900 3 733

Population

1.7 0.6 0.5 0.0 5.5 2.5 0.4 0.0 0.9 15.0 12.4 Pacific people Pacific Ethnicity of imported cases

6 9 7 8 23 14 26 93 70 55 11 19 Imported dengue cases to New Zealand 114 Cases

0.0 0.2 0.0 0.0 0.4 0.0 0.0 0.0 0.2 0.4 2.1 Maori

atory New ZealandPublic Health Observ New ZealandPublic Health Observatory

Year 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Data source: Year 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Data source: 81

desh Bangla

0 0 0 1 4 0 2 21 m 1075 2256 4244 4998 3744 4934 4010 4863 5618 5718 5979 5404 Vietna Cook IslandsCook

French Polynesia

1 Sri Lanka

atu

0 0 0 0 0 0 0

131 110 1 4 1 re

Vanu Singapo 6222 6422 6157 6115 5563 6542 8732 7209 7063 6617

Norfolk Island

Kenya 0 0 0 0 0 0 0 0 460 Tonga

1 1 2 2 India

0 0 0 0 0 0 49

163 200 6473 6884 8659 6262 4730 5204 5396 6489 7801

11819 8 3 Samoa Tonga New Caledonia

stan

1 Paki

0 0 0 0 0

15 154 359 2618

1 1 2 5304 6887 6552 6672 6315 6774 6945 8671 9458 12969 New Caledonia Vanuatu Malaysia

1 1 East East Timor

0 1

8 0 3 0 Islands 630 7344 7291 8118 7989 9759 14 33 43 5 11551 12578 15534 17424 143 Solomon Solomon Tonga Origin ofimported cases 32800

French Polynesia

1 Australia

0 3 0 0 0 0 13779 15987 15540 14697 15083 17187 22264 28985 33751 37247

Samoa 14 2 2 3 Fiji 430 25780 New Zealand short term departures to Pacific Island countries Indonesia

Number of dengue cases in the Pacific Island countries and Australia and countries Island Pacific the in cases dengue of Number

Tahiti

0 205 558 181 217 179 223 868 356

13007 15593 20621 20934 23649 26836 36200 44202 49037 53726

Australia

4 1 11 Cook IslandsCook

Samoa

5 5 1 5

Thailand

0 0 0 0 0 0 0 0 0 Fiji 69939 59624 49186 66723 65103 81502 96484 103834 109837 103798

5 2 2 36 Fiji Norfolk Island

World Health Organization Health World

Public Health Surveillance Public Health

1 6 41 75 Cook Islands

Year Year 1997 1998 1999 2000 1999 2000 2001 2002 2001 2002 2003 2004 2003 2004 2005 2006 2005 2007 2008 Data source: Data source: Statistics New Zealand Year 2002 2003 2004 2005 2006 2007 Data source: 82

Value of cargo unloaded (billion $) Year New Zealand Auckland 1991 14.8 1992 14.8 1993 16.8 1994 18.2 1995 20.2 1996 21.1 1997 20.9 10.3 1998 21.7 11.2 1999 23.4 12.4 2000 27.3 12.0 2001 31.4 13.3 2002 31.5 14.0 2003 31.7 14.3 2004 32.6 14.6 2005 35.4 14.8 2006 37.4 15.3 2007 39.9 15.7 Data source: Statistics New Zealand

(9336): 360 The Lancet State of the world’s forests world’s the of State

2002. Potential effect of population and and population of effect Potential 2002.

2008 2008 2008 2008 2008

Data source

k.org/U0FSM7AQ40 World Development Indicators Development World World Development Indicators Development World Indicators Development World Indicators Development World Indicators Development World culture Organization of the United Nations (FAO). 2009. 2009. (FAO). Nations United the of Organization culture

. Food and Agriculture Organization of the United Nations, Rome Nations, United the of Organization Agriculture and Food . 834 - World Bank’s World Bank’s http://go.worldbank.org/U0FSM7AQ40 World Bank’s http://go.worldbank.org/U0FSM7AQ40 World Bank’s http://go.worldban World Bank’s http://go.worldbank.org/U0FSM7AQ40 World Bank’s http://go.worldbank.org/U0FSM7AQ40 “UNdata” database Division Statistics United Nations http://data.un.org/Default.aspx Food and Agri 2009 Woodward. A. and Maindonald J. Wet, de N. S., Hales, model. empirical an fever: dengue of distribution on global changes climate 830

Surrogate for pollution

Human exposures Human infrastructure poor exposures, Human and availability water Health protectionhealth and care available pollution Pathogen water of storage and distribution Safe Pathogen indirect an and grounds breeding New measure of urbanisation conditions Climatic

Unit

ion with access

Person/km2 Percentage of total population per spent dollar US person Percentage of country GDP Percentage of total populat Percentage of total population Annual change rate See page 41

Variable

Population density Urbanisation Health expenditure Imports Improved water sources Visitors from dengue areas Deforestation rate indicator Climate 84