Diversity, Abundance and Prevalence of Medically Important Pathogens of Mosquitoes Caught During the Dry Winter Season in Madagascar

MSc Project Report 2015-2016

Diversity, abundance and prevalence of medically important pathogens of mosquitoes caught during the dry winter season in Madagascar

Supervisor: Dr. Thomas Walker

Candidate number: 109210

Word count: 9264 words

Project length: Standard

Submitted in part fulfilment of the requirements for the degree of MSc in Medical Entomology for Disease Control

September 2016

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1 TABLE OF CONTENTS

2 Abstract ...... v

3 Acknowledgements ...... vi

3.1 Acknowledgement of Academic Support ...... vi

3.2 Acknowledgement of Other Support ...... vii

1 Introduction ...... 1

1.1 Mosquito-Borne Diseases ...... 1

1.2 Malaria ...... 1

1.3 Arboviruses ...... 5

1.4 Madagascar ...... 6

1.5 Malagasy Mosquitoes ...... 8

2 Aims and objectives ...... 10

3 Hypothesis ...... 10

4 Methodology ...... 11

4.1 Madagascar Locations ...... 12

4.1.1 Rationale for Site Selection ...... 13

4.2 Vector sampling ...... 13

4.3 Identification, Storage and Transportation ...... 17

4.4 Laboratory Screening ...... 18

4.4.1 Sampling and Pooling Strategy ...... 18

4.4.2 RNA Extraction and cDNA Production ...... 19

4.4.3 PCR Assays ...... 20

5 Results ...... 23

5.1 Temperature Data and Humidity Data ...... 23

5.2 Qualitative Assessment of Sites ...... 26

5.3 Overall Mosquito Abundance ...... 26

5.4 Species Diversity ...... 30

5.5 Species Screened, Number of Individuals and Pool Sizes ...... 33

5.6 PCR Analysis ...... 33

6 Discussion ...... 38

6.1 Mosquito Abundance ...... 38

6.2 Vector Diversity ...... 39

6.3 Pathogen Screening ...... 40

6.4 Limitations ...... 41

6.5 Summary ...... 42

7 Recommendations ...... 42

8 References ...... 43

9 Appendix ...... 46

2 ABSTRACT

2.1 INTRODUCTION

Mosquitoes are responsible for significant human and animal disease through transmission of pathogens such as malaria and arboviruses. Outbreaks of malaria and arboviral diseases in Madagascar have typically been confined to the wet season when mosquito abundance is highest. However, there have been limited studies looking at mosquito abundance, activity or role in disease transmission in the dry season in Madagascar.

2.2 METHODS

Mosquitoes were sampled using eight CDC light traps and one zebu-baited trap from five sites for two nights each across Madagascar. Traps were selected to sample a variety of night-biting mosquitoes. Caught individuals underwent morphological identification and correct storage for transport and further laboratory analysis.

Non-bloodfed females of varying species were selected for RNA extraction and reverse transcription to produce cDNA for PCR analysis according to abundance and previous roles in disease transmission. cDNA underwent real-time PCR assays for malaria parasites and a range of arboviruses.

2.3 RESULTS

In total, 2051 mosquitoes were caught with 2050 identified. 29 different species were identified within 6 different mosquito genera. 18 caught species were previously identified as disease vectors. PCR analysis revealed two Culex decens positive for Rift Valley fever virus and seven Anopheles spp. positive for Plasmodium falciparum.

2.4 CONCLUSIONS

These results indicate the possibility of disease transmission during the dry season given the abundance of vector species and evidence of pathogen infections within caught females. Further research is needed to determine the importance of different vector species in dry season transmission to Madagascar transmission dynamics.

3 ACKNOWLEDGEMENTS

3.1 ACKNOWLEDGEMENT OF ACADEMIC SUPPORT

3.1.1.1 Project Development

The project idea originally came from my supervisor’s interest in collaborating on a project with Institut Pasteur de Madagascar (IPM). The project was generally developed by myself, my supervisor and his PhD student while details regarding field work were decided on in collaboration with IPM. My supervisor provided information on which PCRs would be possible to perform in the laboratory while I made the final decision on which to perform. Pooling strategies for PCRs were designed by myself with input by both my supervisor and his PhD student.

3.1.2 Contact, Input and Support

I had an initial meeting with my supervisor in order to discuss general proposals for projects. There were two further meetings, one of which was also attended by my supervisor’s PhD student for detailed planning of this project. There were several email exchanges to discuss administrative issues. Alongside this there was email communication with IPM regarding field work. Before travel there was a meeting with my supervisor and his PhD student to review objectives, ensure I had all required equipment and discuss contact details while in the field.

3.1.3 Main Research Work

On arrival in Madagascar I met with Dr. Sebastien Boyer of IPM to finalize field sites. I determined trap placement locations at each site with assistance by Dr. Fara Raharimalala and Dr. Luciano Tantely of IPM. Both assisted in all aspects of primary data collection in the field. I maintained sporadic contact with my supervisor. On return to London, while I performed the laboratory work myself I was assisted in high-cost or skill-dependent steps by my supervisor and his PhD student.

3.1.4 Writing-Up

My supervisor read one initial draft and provided suggestions focusing on structure and corrections on grammar, spelling, incorrect referencing and minor comments on content. A final draft was submitted to ensure that the report was fit for submission (checking spelling, title pages, table of contents, referencing, word count and anonymity).

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3.2 ACKNOWLEDGEMENT OF OTHER SUPPORT

I am very grateful to both Dr. Luciano Tantely and Dr. Fara Raharimalala for their assistance in the practicalities of field work. Additionally, I would like to thank Dr. Sebastien Boyer and the rest of the management at IPM for their help throughout the project. I would also like to thank the military personnel, local government, and village chiefs who assisted me in the field.

I am grateful to the London School of Hygiene and Tropical Medicine and Bayer Pharmaceuticals for providing me with funding without which I would have been unable to undertake this project

I would also like to thank my family for supporting me while I’ve worked on this project and providing me with somewhere to go to relax when it was finished. Last but not least I would like to thank my partner, Olivia, for helping me manage all the aspects of my life that I’ve neglected while doing this project, for putting up with me while I’ve been working on this and for performing a final proofread. Your support has been invaluable for this project and, more widely, during the entire MSc.

1 INTRODUCTION

1.1 MOSQUITO-BORNE DISEASES

Mosquito-borne diseases remain the most important vector-borne diseases world wide1 with mosquitoes transmitting a variety of pathogens including protozoal parasites (Plasmodium falciparum), viruses (dengue fever virus, Zika virus and others) and filarial nematodes (Wuchereria bancrofti, Brugia malayi, Brugia timori and others) alongside important veterinary diseases (such as Blue Tongue virus and Setaria spp. nematodes). As such, not only do they directly threaten human health, particularly child and adolescent health2, but they also threaten human livestock affecting the economies of many developing countries. Transmission occurs during blood-feeding where the pathogen is introduced into the circulatory system on injection of the proboscis before multiplication occurs2.

While mosquito-borne disease has been seen as a disease of the tropics with mosquito bites traditionally seen exclusively as a nuisance in more temperate areas this has not always been the case. Historical transmission has been shown even in the UK suggesting that it is possible for ongoing transmission to occur in more temperate areas.3 Alongside this, there is already evidence of invasion of temperate areas by major mosquito vectors bringing with them cases of mosquito-borne disease.4–6 In fact, with climate change it is theorized that this will become increasingly common7.

This is likely to add on to the millions of cases of the two most common mosquito-borne diseases malaria and dengue fever. Dengue fever virus is the most important mosquito-borne virus with 2500 million people at risk worldwide. However, even more important is Malaria which is a threat to 40% of the world’s population.1 Additionally, malaria has the highest number of cases with 500 million cases occurring annually with up to 2.7 million deaths1. Of the cases that do occur, despite distribution being in South America, Africa, the Caribbean, Asia, the Pacific Islands, Oceania and Central America 90% of cases occur in Africa alone.1

1.2 MALARIA

Malaria is caused by the protozoal blood parasite Plasmodium spp, which is exclusively transmitted to humans by Anopheles spp. mosquitoes. Transmission occurs during blood- feeding where infectious stages are injected from the salivary glands. These develop in the liver

and eventually the blood to become disease causing and to allow for further reproduction and transmission in the mosquito vector8. The typical symptoms of malaria are a relapsing-remitting fever, rigors, severe sweating and fever9 but may develop to involve cerebral complications, haemolytic anaemia or fetal complications in pregnant women10. Clinical malaria is geographically spread across the tropics with variations in dominant Plasmodium species.

There are five different species of human malaria that exist worldwide (Plasmodium ovale (two subspecies: P.o. curtisi and P.o. wallikeri11), Plasmodium vivax, Plasmodium falciparum (Pf), Plasmodium malariae and Plasmodium knowlesi)11–13. While overall they share some features such as disease transmission and basic case presentation there are important differences to note. Firstly, Plasmodium falciparum is the most likely to progress to severe malaria and due to the surface protein PfEMP-1 is the only one to cause cerebral and placental complications of malaria (while also the most likely cause of severe malaria anaemia)12. Plasmodium ovale (both subspecies) and vivax are important due to a dormant hypnozoites stage which allows for re- emergence of symptoms after initial anti-malarial treatment14.

Geographically, Plasmodium falciparum and malariae have global distribution, Plasmodium ovale is more common in West Africa, Plasmodium vivax is less common in West Africa (as the local population typically has a specific genetic phenotype which lacks the required Duffy antigen for P. vivax infection) and Plasmodium knowlesi is currently restricted to parts of Malaysia, Brunei and Borneo15. Figure 1 shows the endemicity of Plasmodium falciparum per 1000 people per annum in Africa.

Worldwide malaria transmission still accounted for 214 million cases and 839 thousand deaths annually in 2015 showing a global downward trend in both cases and mortality16. While globally this is an 18% decrease in case numbers (and a 48% decrease in mortality) from 2000 it does demonstrate that malaria is still an important cause of morbidity and mortality15. The majority of cases occur in the World Health Organization Africa Region (88%; this region includes Madagascar)15. Alongside this human cost of malaria there is an estimated £9.14 billion direct cost annually13 with intensive malaria being estimated to reduce annual growth of a nations GDP by 1.3%.17 This financial cost is significant as the majority of cases occur in already quite poverty struck regions.

While malaria vectors are all of the same genus (Anopheles spp.) there is a very large variation worldwide with each region of the world having its own set of dominant vectors18. Figure 2

details this large variation in species18. While generally Anopheles spp. is an endophillic and endophagic (indoor-biting) night-biter, specific control strategies should take into account differences15,19,20. The current techniques for malarial vector control are the use of Long-Lasting Insecticide Treated Nets (LLINS) and Indoor Residual Spraying (IRS) which aims to take advantage of the common mosquito behavior.16 Recently, these strategies have been challenged by the emergence of insecticide resistance in mosquito populations around the world. However, if this behavior should change or if the main vector species in a region were to change it may be appropriate to reassess control strategies.

Figure 1 – Plasmodium falciparum Incidence in Africa in 2015 in Cases per 1000 per annum (Adapted from Malaria Atlas Project 201576)

Figure 2- Map Showing the Geographic Distribution of Dominant Malaria Vectors (Adapted from Sinka et al. 201218)

1.3 ARBOVIRUSES

Arboviruses (Arthropod-Borne Virus) are viruses which are transmitted through arthropod vectors, typically during blood feeding. The most common of these vectors are mosquitoes and ticks, however sandflies and biting midges are also capable of transmitting specific arboviruses21. Arboviruses are classified 4 virus families: Bunyaviridae, Flaviviridae, Reoviridae and Togaviridae which themselves can be further subdivided. While the most significant mosquito-borne arboviruses are flaviviruses (flavi) (Dengue virus (DENV), Japanese encephalitis virus (JEV), West Nile virus (WNV), Zika virus (ZIKV) and Yellow fever virus (YFV)) there are still significant threats to human health found in the other arbovirus families (Rift valley fever virus (RVFV - bunyavirus) and Chikungunya virus (CHIKV - togavirus) are just two examples)22. The majority of these viruses are RNA viruses22 (with the exception of African swine fever virus) and share clinical symptoms of an acute febrile illness which may go on to develop virus-specific complications23.

Arbovirus transmission worldwide is a significant threat to global human health with 2.4 million cases of Dengue fever alone reported to the WHO in 201524 (with an estimated 93.6 million going unreported25) with other arboviruses also having an important disease burden26. This is not only a large scale global issue but also one that has been getting worse, with increasing case numbers and invasion into new areas22,27–29. This has been most recently demonstrated by the 2016 Zika virus crisis30, which may become an even larger threat with the 2016 Rio Olympics. With globalization, arboviruses are no longer constricted to the regions they are discovered, but may become global threats to human health22,31. Alongside the obvious consequences on morbidity and mortality, arboviral diseases have a large financial burden globally with Dengue virus costing approximately £6.4 billion annually32. Furthermore, this disease burden falls largely on developing areas of the world, hindering their ability to cope with public health issues on the whole27.

Much of the threat of arbovirus growth and expansion, particularly Dengue fever and Chikungunya, is due to the major mosquito vectors, Stegomyia (Aedes) aegypti and Stegomyia (Aedes) albopictus respectively, and their ability to easily invade new regions. This ability comes from a biological ability to easily adapt to most warm climates (with established populations from Southern Russia to Northern Australia)5 and the ability for eggs to remain viable for several months under dry conditions33. This allows eggs to be transported world-wide along shipping routes before hatching and rapidly establishing a population in the new region.

Additionally, Stegoymia (Aedes) aegypti has a large role in human disease due to its proximity to human populations (as its breeding sites of choice are man-made containers34) and the aggressive nature of its blood feeding35 (which resists traditional vector control techniques such as bed nets due to day biting). This can increase the number of cases in an area which has circulating arboviruses in the human population or can even introduce arboviruses to a region as vertical transmission occurs in mosquitoes36. While this is the most important arbovirus vector, there are large varieties of both anopheline and culicine mosquitoes which also have a role in transmission and which can complicate disease control37.

1.4 MADAGASCAR

Madagascar is one of the poorest nations in the world with approximately 91% of the population living in poverty. While access and quality of healthcare had been improving steadily over the past few decades, this progress has been hindered following the coup d’etat in 2009. Only recently has healthcare provision begun to improve again38. There are two main seasons, a wet Summer which lasts from December to March and a dry Winter from April to October. Malaria and arbovirus transmission is associated with the wet season with arbovirus outbreaks commonly following powerful tropical monsoons in the middle of summer39–41.

However, these seasons are not uniform across the island due to the large variation in climactic biomes and altitudes. In general, the West, East, Sambirano and North regions have a tropical climate, the Central Highlands have a temperate and the South region has an arid climate. Furthermore, the East region suffers the most from summer monsoons42,43. Additionally, species diversity along the island can potentially be traced to the huge diversity in regional fauna, flora, temperature, rainfall and altitude across these general regions37,41,44. Figure 3 demonstrates the general breakdown of the four major biogeographic domains alongside a simplified description of the typical bioclimatic breakdown of each of these domains42,44.

Figure 3 - Biogeographic Regions of Madagascar and Simplified Bioclimactic Descriptions (adapted from Cornet 197442 and Vences et al. 201144)

Humid/Montane – Rainforest,

Subhumid – Rainforest relics W - West, N - or grassland, North, E - East, S - South, C - Central Dry – Deciduous Forest, High Plateau and Sb - Sambirano Subarid – Spiny Forest

Socially, Madagascar has a mix of rural (64.9%) and urban (35.1%) populations with a rate of urbanization of 4.69%. Despite this, a large number of areas in Madagascar are completely unsettled and undeveloped. Urban populations are concentrated in larger cities (particularly regional capitals such as Antananarivo) with smaller rural communities focusing on animal husbandry and agriculture. There is a variety of indigenous ethnic groups which are spread across the island. Politically, Madagascar remains unstable, likely due to the previously mentioned economic and social factors alongside issues of corruption. Additionally, riots, protests and attacks are a frequent occurrence. Unfortunately, this means that the provision of healthcare is further limited while public health is severely underfunded with policies being undertaken fairly unreliably. This extends to basic services such as electricity and water which can limit the scientific research which can be performed in-country43.

There have been a number of arbovirus epidemics in Madagascar over the past decade including outbreaks of Rift Valley fever virus (2008, 2009), Dengue virus (2006), Chikungunya virus (2010, 2009, 2007, 2006) and sporadic cases of West Nile virus (2011)45–48 across the island. These diseases cause large-scale morbidity and mortality during each epidemic. The 2010 Chikungunya epidemic is the most recent large scale epidemic to occur in the country and involved two regions on the East coast of Madagascar with 2680 suspected cases of which 111 samples (of 126 submitted) tested positive on laboratory viral detection47,49. This level of epidemic combined with the difficulty in accessing good quality healthcare shows just how important the control of arbovirus transmission within Madagascar.

Additionally, malaria is an important cause of morbidity and mortality in Madagascar. While the global incidence of malaria may have been declining over the past decade, Madagascar’s incidence has actually been increasing between 2010-2014 15, potentially as a side effect of the 2009 coup d’etat38. 96% of these cases are Plasmodium falciparum with 4% Plasmodium vivax and no other species present. Current government measures to reduce malaria include Indoor Residual Spraying and the use of Insecticide-Treated Bednets, however these are concentrated during the peak transmission season in summer. Furthermore, the efficacy of these programs may be limited as Madagascar has the lowest amount of money spent on malaria control (per at-risk capita) of all the WHO designated East Africa/High Transmission Southern Africa nations15.

1.5 MALAGASY MOSQUITOES

Currently there have been 235 species of mosquito identified in Madagascar in 14 different genera. Table 1 shows the number of species within each genera and the number of species of medical or veterinary importance. However, a number of these “species” are in fact are in fact non-deciphered complexes, suggesting that the real number is higher. 135 (38%) of the species known to exist in Madagascar are isolated to Madagascar and 64 species (27%) are known to be of either medical or veterinary importance (isolates containing pathogens found). Of these 64, the vectors of human disease along with the diseases transmitted have been listed in Table A1 in the appendix.37

The most important malaria vectors in Madagascar are Anopheles arabiensis, Anopheles funestus and Anopheles gambiae s.s, with Anopheles merus (West/South region) and Anopheles mascareinsis important in specific foci. The two main arbovirus vectors are

Stegomyia (Aedes) aegypti and Stegomyia (Aedes) albopictus with a large variety of less significant vectors.

However, the majority of vector incrimination has occurred following disease outbreaks, typically in the wet season. This has limited the amount of information on species diversity during the winter seasons. Rationale for this was that temperatures were not conducive to the survival of adult mosquitoes while the lack of rainfall would prevent the emergence of new adults. This has recently been challenged in reference to Rift Valley fever virus vectors along and this view does not take into account the variation in geography of Madagascar41. There are gaps in vector research surrounding this winter dry season.

Table 1 - Summary of Madagascar Species Diversity by Genus (Adapted from data in Tantely et al.2016 37)

Genus Number of Number of Species of Medical or Number of Species of Medical or Species Veterinary importance (Global) Veterinary Importance (Local) Aedomyia spp 3 1 1

Aedes spp 35 15 8 Anopheles spp 26 16 10 Coquillettidia spp 3 2 1 Culex spp At least 50 22 9 Eretmapodites spp 4 3 1 Ficalbia spp 2 0 0 Hodgesia Spp At least 1 0 0 Lutzia spp 1 1 0 Mansonia spp 2 2 1 Mimomyia Spp 22 2 0 Orthopodmyia SPP 8 0 0 Toxorhynchites Spp 6 0 0 Uranotaenia spp 73 0 0

2 AIMS AND OBJECTIVES

To identify the variety and frequency of potential medically important mosquito vectors in the West, Central High Plateau, and North regions of Madagascar.

1. To trap and identify mosquitoes at five sites in Madagascar in the dry season As the majority of mosquito surveillance studies are undertaken immediately following epidemics or at times with high mosquito population densities, both of which tend to occur during the wet season, there is less of an understanding of mosquito populations which are active during winter. 2. To assess the presence of known vector species within these five sites The risk of mosquito-borne disease in the winter months is likely influenced by the number and diversity of active species known to transmit pathogens in Madagascar. 3. To screen the mosquitoes for major arboviruses and malaria known to circulate in Madagascar Screening mosquitoes for medically important pathogens could help identify disease foci which may require more advanced control strategies. Furthermore, this may turn vector diseases from year-wide low-level transmission with seasonal peaks into exclusively seasonal transmission which may aid with disease control and future elimination plans. 4. To screen the mosquitoes for potentially novel arboviruses or arboviruses not known to circulate in Madagascar Broad screening of flaviviruses (using Pan-Flavi PCR assays) can identify less known flaviviruses that could lead to the discovery of a novel virus in Madagascar. Screening of arboviruses not known to circulate in Madagascar (for example, Zika virus) could help inform public health practices and further understanding of the risk of disease importation into Madagascar.

3 HYPOTHESIS

The distribution and abundance of mosquitoes during the dry season in Madagascar will be correlated to environmental conditions including temperature, humidity, presence of water sources and presence of mammals (both human and non-human). A low pathogen infection rate is predicted in trapped mosquitoes given the absence of major disease epidemics during the dry winter season in Madagascar.

4 METHODOLOGY

The field work component of the study involved mosquito trapping utilizing both CDC light traps and a Zebu trap at five separate sites which form a transect from the Central High Plateau region to the North region. Trapping was performed over two nights with CDC light traps set up at dusk while the Zebu traps were put into place as close to night fall as possible. Light traps were taken down at dawn while Zebus were removed before dawn for mouth aspiration of the trap. Mosquitoes were placed inside a sealed container with chloroform soaked cotton wool until dead before identification was performed. Figure 4 is a map showing the location of the sample sites while Table 2 provides more details.

The laboratory component of the study involved the preservation of mosquitoes in RNALater at 4°C or lower where possible in order to prevent the degradation of viral RNA. Reverse transcription was performed in order to produce complementary DNA (cDNA). This cDNA was used in order to perform real-time PCR screening for viruses and Plasmodium falciparum malaria parasites.

4.1 MADAGASCAR LOCATIONS

Figure 4 - Map of Madagascar Showing Location of Study Sites. Right – More detailed map of northern half of Madagascar where study sites are located (Adapted from Google Maps).

Red Star – Site One, Yellow Star – Site Two, Green Star – Site Three, Blue Star – Site Four, Purple Star – Site Five

Table 2 - Summary of study site characteristics (Adapted from Google Maps)

Location Site Number Province GPS Way Point Altitude (m)

Anivorano North 1 Diego 12°45'52.2"S 49°14'19.3"E 357 Tsaramandroso 2 Mahajanga 16°21'62.2"S 46°59'34.4"E 84 Maevatanana 3 Mahajanga 17°01'37.8"S 46°45'61.7"E 64 Ankazobe 4 Anatananarivo 18°19'58.6"S 47° '06.33.3"E 1212 Ivato Imerimandroso 5 Anatananarivo 18°47'31.6"S 47°28'68.4"E 1261

4.1.1 Rationale for Site Selection

Several factors were taken into consideration when selecting broad sites in order to sample a wide variety of locations, cutting a large transect across the country. This would allow the gathering of a large variety of potential ecological factors for analysis and would also allow this project to potentially provide the most use to public health services by forming generalizable ideas regarding the data collected. Specific factors which were considered were variations in altitude, climate, local flora and fauna, population, percentage of urbanization, animal husbandry practices, agricultural practices, and proximity to water bodies. However, some sites were selected for these factors alongside special factors, for example Ivato imerimandroso due to its proximity to the main international airport of Madagascar.

On a village level, selections were based on practical reasons, notably security, availability of zebu, local permissions, availability of accommodation, proximity to rice paddies or similar water dependent crops, and proximity to areas for resupply. Important considerations were given to the willingness of residents with permission being sought from local military, civil and health authorities, alongside village chiefs. Further consent was obtained from residents whose houses would have traps or whose zebus were used.

4.2 VECTOR SAMPLING

Eight CDC Light Traps were placed around each site in pairs and labeled numerically with traps being placed in similar areas at each site to avoid confusion on analysis. CDC Light Traps were suspended at a height of approximately 160 cm above the ground (however, this was not possible at all sites). The first trap pair (Traps 1 and 2) were placed around the zebu baited trap from nearby dwellings. The second pair (Traps 3 and 4) were placed near poultry coops. The third pair (Traps 5 and 6) were placed inside of areas of denser vegetation (typically forested areas or crop fields). The fourth pair (Traps 7 and 8) were placed near potential breeding locations which varied depending on each site.

One Zebu-baited trap with a large size Zebu, (Bos taurus indicus – a local species of domestic cattle50), was placed in a way which simulated local animal husbandry practices (ie inside of a shed or nearby to a large corral or on its own tied to a post). The Zebu Trap was created using 8 posts driven into the ground with an internal rectangle and a larger external rectangle. An untreated mosquito net was hung from the internal posts and the zebu was placed inside. This

net was then sealed as best as possible. A second untreated mosquito net was hung from the external posts and suspended 3m above the ground allowing mosquitoes to enter. However, due to positive geotropism they would be trapped in the top of the net, unable to escape. Figure 5 shows a photograph of the Zebu trap while Figure 6 is a diagrammatical representation of a top-down view.

Light traps were typically hung before dusk in order to be operational during sunset and overnight before being collected at dawn. Batteries were then charged using a gas powered generator during the day before the traps were placed again. Before setting up traps, each light trap and each battery was tested to ensure that they functioned adequately. Zebu traps were set up following sunset and mosquitoes were collected by mouth aspiration before dawn. However, due to practical difficulties it was not possible to be completely uniform with the setting of traps across the nights and sites. Table A2 in the appendix details exact placement locations while Table A3 details exact times (East Africa Time Zone - UTC+03:00) and duration each trap was active across all 10 nights. Figure 7 shows photographs of trap placement at Site 1 as an example.

Temperature and humidity data was collected at each site with two separate EasyLog dataloggers used at each site. Overall, both sensors were placed the evening of the first night of trapping and removed the morning after the last night of trapping however, due to practical limitations this did vary across sites.

Figure 5 - Back View of Zebu Trap at Site 1

Figure 6 - Diagram of Zebu Trap, Top-Down View. Star represent wooden posts driven into ground. Rectangles show boundaries of the nets. Line from Zebu body was typically a rope already attached to the Zebu to keep it within the trap.

Figure 5 - Site 1 Light Traps; Top Row from Left: Light Trap 1, Light Trap 2, Light Trap 3, Light Trap 4; Bottom Row from Left: Light Trap 5, Light Trap 6, Light Trap 7 and Hanging Light Trap 8

4.3 IDENTIFICATION, STORAGE AND TRANSPORTATION

Morphological identification was performed by a world expert in Madagascar mosquito morphology, Dr. Luciano Tantely of Institut Pasteur de Madagascar37. Identification was based on previously validated morphological keys, personal experience and the use of morphometrics. Where possible mosquitoes were identified to species and were divided into males, non- bloodfed females and bloodfed females. Figure 8 shows two species identified, Anopheles squamosus and Culex antennatus.

Figure 6 - Above: Anopheles squamosus (non-bloodfed female), Bottom: Culex antennatus (non-bloodfed female)

Following identification, the mosquitoes were individually placed into 96 well plates and 100 microlitres (µl) of RNAlater (Sigma) was added and stored at 4 degree Celsius or lower to prevent viral RNA degradation51. However, due to the abundance of mosquitoes caught a shortage of material meant that mosquitoes caught at Site 5 were grouped together into Eppendorf tubes. Groups were made up of individuals with the same trap type, sex and species with a maximum of 31 individuals per tube.

4.4 LABORATORY SCREENING

4.4.1 Sampling and Pooling Strategy

Two 96-well PCR plates (representing 192 RNA extractions) were chosen to be filled in order to appropriately balance thoroughness with practicality (most importantly, finances and available time). It was decided that the first plate would be used for screening a sub-sample of the five most abundant species present across the five sites. Sub-sampling was performed first by exclusively selecting non-bloodfed females (in order to ensure that any pathogenic RNA detected was in the mosquito rather than in a blood meal) with approximately 50% of the number of bloodfed females sub-sampled. These were divided into pools with a minimum pool size of 3 and a maximum pool size of 5. Pools were used to maximize likelihood of having positive results with the remaining 50% still available for individual screening at a later date.

This strategy was also used for species which were highly abundant at a limited number of sites on plate 2 and on the most abundant non-Culex and non-Anopheles species present. This was done in order to increase diversity of species tested and the likelihood of identifying a wider range of pathogens which may be circulating. Other species of interest were selected for screening individually. Interest was defined as a limited number of individuals, limited amount of prior research on species and currently known vectoral capacity. Sub-sampling was dependent on the number of individuals present overall and is fully outlined in Table 3.

These strategies were selected for a variety of reasons. Firstly, the most abundant species may pose a greater threat of local transmission in the sampled areas, particularly if they are important vectors. Additionally, if these species are prevalent across the sample sites they may pose a large health risk to the entire population. Secondly, rarer species may have limited field data on them and as such any positive results could be more significant both for future research and for public health practice. Any rarer species which are highly abundant in particular locations may call for a change in public health policy specifically in these areas.

Table 3 - Pooling and Sampling Strategy

Is there any data suggesting that the species has the potential for transmission of disease? Yes No More than 30 individuals present Do not sample Yes No 20-30 Individuals Present Yes No 10-20 Individuals Present Yes No 50% Sub-Sampled in pools of 3-5 individuals All Individuals sampled from most abundant site and night, if this is more than 11 only All Individuals sampled from most abundant site, take 10 individuals All individuals sampled if this is more than 11 only take 10 individuals

4.4.2 RNA Extraction and cDNA Production

After mosquito species and pools had been decided, RNA extraction was performed followed by reverse transcription for cDNA production. Mosquitoes were placed either in pools or as individuals into 1.5mL Eppendorf Tubes already containing one 5mm stainless steel bead (Qiagen) sat in ice. Forceps were cleaned with ethanol between each sample. Afterwards, 1mL of Trizol (Sigma) was added to each tube under a fume hood (Astec) using an electronic pipette. Samples were loaded in batches of 48 into a bead beater (Qiagen) and were securely fastened before homogenizing at 20Hz for 1 minute. This step was repeated if the sample was not thoroughly homogenized.

200µl of Cholorform (Sigma-Aldrich) was added to each sample which was then shaken to thoroughly mix before being allowed to stand for 15 minutes. Samples were then centrifuged (Eppendorf) in batches of 18 at 6,000 rcf for 15 minutes at 4°C. Phase separated samples were kept on ice while batches were being centrifuged.

300µl of aqueous phase was transferred from each sample to a blue homogenization rack for RNeasy 96 procedure (Qiagen). 300µl of 70% ethanol was then added to each sample in RNase-free 96 well plates then pipette mixed before being transferred to a spin column rack. Racks were centrifuged (Eppendorf) at 5,600 rcf for 4 minutes with follow-through being disposed of. 400µl of Buffer RW1 (Qiagen) was added using the electronic pipette to each sample before the racks were centrifuged again at 5,600 rcf for 4 minutes. Follow-through was again discarded and 400µl of Buffer RPE (Qiagen) was added before 10 minutes of centrifuging

at 5,600 rcf. Spin columns were placed in an unused elution rack then 45µl of RNase-free water (Qiagen) was added to each sample before being incubated for 1 minute. Samples were centrifuged for 4 minutes and follow-through transferred to a 96 well PCR plate using electronic pipette for storage which was then sealed with foil. This was repeated for a second elution. Samples were stored at -80°C.

Reverse transcription was performed using Transcriptor First Strand cDNA Synthesis Kit

(Roche) using both anchored-oligo (dT)18 primer (Roche) and random hexamer primer (Roche). Reagents were centrifuged briefly before use (VWR) and kept on ice throughout. Template- primer mix was produced with 1µl anchored-oligo (dT)18 primer and 2µl random hexamer primer per sample and then centrifuged briefly (VWR). The 3µl of the mix was added per well to two 96-well PCR plates alongside 10µl of produced sample RNA. These plates were then heated at 65°C (Bio-Rad) for denaturing before being placed on ice. A reverse transcription master mix was produced with 4µl of transcriptor reverse transcriptase reaction buffer (Roche), 0.5µl protector RNase inhibitor (Roche), Deoxynucleotide mix (Roche) and transcriptor reverse transcriptase (Roche) per sample. 7µl of this master mix was added to each well which was then pipette mixed before incubation on the thermal cycler (Bio-Rad) at 25°C for 10 minutes then 55°C for 30 minutes then 85°C for 5 minutes before being held at 4°C. cDNA was stored at -20°C when not in use.

4.4.3 PCR Assays

Optimized real-time (rt-) PCR assays were performed to detect both a variety of arboviruses alongside Plasmodium falciparum from produced cDNA. Table 4 provides details on the individual assays used alongside the optimized cycling conditions. Optimization was performed using a multiple standards of decreasing concentration alongside multiple non-template controls. PCR assays were performed in two steps, an initial assay with 2-6 columns of cDNA alongside positive and negative controls in order to ensure the assay was working followed by a second assay with the remaining samples and positive and negative controls. Water acted as a non-template control for all of the assays performed. Where the negative control was significantly positive and did not allow for interpretation of results the assay was repeated. PCR assays were performed on a Roche LightCycler 96 with interpretation of results by quantification cycle (Cq) and melting temperature (Tm) on Roche LightCycler 96 software. Melt curve analysis using the software would be used in order to differentiate non-specific amplification from medically important amplification.

A full materials list detailing product codes, batch numbers and brands can be found in Table A4 in the appendix.

Table 4 - Summary of PCR Screening Methodology including Primes, Cycling Conditions, Controls and Reference Paper Assay Forward Reverse Primer Cycling Conditions Positive Reference Notes Primer Control Pan- 5’- 5’- 95°C 600s Pan-DENV Johnson et SYBR Assay Flavi GCMATHTGG GTRTCCCAKCC cDNA 10-3 al. 201052 TWCATGTGG- DGCNGTRTC-3’ 3’ 95°C – 10s 50 cycles 65°C – 10s 72°C – 10s 95°C – 10s Melt 65°C – 60s 97°C – 1 s Pan- 5′- 5′- 95°C 600s Pan-DENV Lai et al. SYBR Assay DENV TTGAGTAAAC GAGACAGCAG cDNA 10-3 200753 YRTGCTGCC GATCTCTGGTC 95°C – 10s 50 cycles TGTAGCTC-3′ TYTC-3′ 65°C – 10s 72°C – 10s 95°C – 10s Melt 65°C – 60s 97°C – 1 s ZIKV 5’- 5’- 95°C 600s ZIKV Lanciotti et Probe Assay CCGCTGCCC CCACTAACGTT Standards 106 al. 200854s (FAM): AACACAAG - CTTTTGCAGAC (Brazilian and 5’- 3’ AT -3’ Micronesian) AGCCTACCTTGA CAAGCAGTCAGA 95°C – 10s 45 cycles CACTCAA-3’ 52°C – 30s

CHIKV 5’- CTCATA 5’- 95°C 600s Ali CHIKV Ali et al. SYBR Assay CCGCATCCG ACATTGGCCC Brazil 201055 CATCAG-3’ CACAAT 95°C – 10s 40 cycles Standard 106 GAATTTG-3’ 56°C – 10s 72°C – 15s 95°C – 10s Melt 65°C – 60s 97°C – 1 s WNV 3’ 3’- 95°C 600s WNV Linke et al. Originally probe- CCTGTGTGA GCGTTTTAGCA Standards 200756 based but 95°C – 10s 45 cycles GCTGACAAA TATTGACAGCC 107-108 adapted to SYBR CTTAGT-5’ -5’ 65°C – 10s assay 72°C – 10s 95°C – 10s Melt 65°C – 60s 97°C – 1 s

Assay Forward Primer Reverse Primer Cycling Conditions Positive Reference Notes Control YFV 5’- 5’- 95°C 600s YFV Standard Dash et al. SYBR Assay AATCGAGTTGC TCCCTGAGCTT 106 201257 TAGGCAATAAA TACGACCAGA- 95°C – 10s 40 cycles CAC-3’ 3’ 68°C – 10s 72°C – 10s 95°C – 10s Melt 65°C – 60s 97°C – 1 s RVFV 5’- 5’- 95°C 600s RVFV seg Maquart et SYBR Assay CTAGCCGTTTC GACTGARGAYT Mad Standard al. 201458 ACAAACTGGG- CTGAATTGCAC 95°C – 10s 45 cycles 106 3’ C-3’ 60°C – 10s 72°C – 20s 95°C – 10s Melt 65°C – 60s 97°C – 1 s Pf 5′- 5′-ATATTG 95degrees 600s cDNA Marie et al. SYBR Assay TTACATCAGGA GATCTCCTGCA C extracted from 201359 ATGTTATTGC- AAT-3′ cultured Pf 3’ 95degrees 40 cycles infected blood C – 15s (Parasitaemia 58degrees approximately C -30s 10%) 95°C – 10s Melt 65°C – 60s 97°C – 1 s

5 RESULTS

5.1 TEMPERATURE DATA AND HUMIDITY DATA

Temperature and humidity data varied across all sites and generally followed a pattern of peaks and troughs depending on time with Figure 9 showing this pattern. The maximum temperature was 48.5°C recorded at Site 3 (however, this is likely to be an error, potentially due to sun exposure, as other sources cite the highest temperature on this date as 33°C60) with the lowest temperature 6°C at Site 5. Figure 10 is a box and whisker plot showing the five number summary of temperature data while Table 5 details these statistics. There was a statistically significant difference between temperatures across the five sites (P<0.0001) which was also statistically significant when comparing each individual site to one another (P<0.0001) when performing Kruskal Wallis Test and Dunn’s Multiple Comparison Test.

Similarly, humidity across all sites and between each site was statistically significant (P<0.0001) when performing the same statistical analyses as with temperature. The lowest humidity level was 21% at Site 3 and the highest was 100% humidity at Site 5. Figure 11 shows the box and whisker plot of humidity across all five sites with Table 6 showing the statistical five number summary.

Figure 7 - Temperature and Humidity over Time, Red Line Indicates Temperature (°C) while Blue Line Indiciates Humidity (%) Linked to Site

Site 1 Site 2 Site 3 Site 4 Site 5

Figure 8 - Box and Whisker Plot Comparing Temperature Data (in °C) Across Field Sites Showing Range, Median and Interquartile Range

) Temperature Data by Site

u i 50

C 40

g 30

(

e 20

r 10

e 0

T 1 2 3 4 5 e e e e e it it it it it S S S S S Site

Table 5 - Five Number Summary of Temperature Data (°C) Across Sites

Site 1 Site 2 Site 3 Site 4 Site 5

Minimum 16 19 19.5 11 6

25% Percentile 19.5 21.5 23.5 13 9

Median 20.5 24 26 14.5 11

75% Percentile 22.5 28 29.5 18.5 15.5

Maximum 34 40.5 48.5 26 37

Figure 9 - Box and Whiskers Plot Comparing Humidity (in %) Across Field Sites Showing Range, Median and Interquartile Range Humidity/Site 120

a 100

e 80

i 40

u 20

0 1 2 3 4 5 e e e e e it it it it it S S S S S Site

Table 6 - Five Number Summary of Humidity (%) Across Field Sites

Site 1 Site 2 Site 3 Site 4 Site 5 Minimum 43 31.5 21 44.5 25.5 25% Percentile 65 52.5 40 63.63 60.5 Median 73.75 57 49.5 79 88 75% Percentile 78 69.5 59.5 82 94.5 Maximum 82.5 81 73.5 89.5 101

5.2 QUALITATIVE ASSESSMENT OF SITES

Each site had qualitatively appreciable differences in flora and fauna. Table 7 summarizes these important differences, dividing them by man-made differences and natural differences.

Table 7 - Summary of Qualitative Site Differences

Site Climate Landsca Natural Flora Natural Agricultural Animal Construction Human population pe Fauna Practice Husbandry and Housing Practice 1 Windy Nearby Forested Many N/A Few zebu Buildings in Sparsely populated but not river and areas stray and poultry small family far from medium sized town swamp cats and clusters, dogs thatched roofs,sheet metal walls 2 Arid, dusty Nearby Scattered Many Rice Many zebu Buildings close Populated, evening and windy lake trees and stray paddies, kept to each other, gatherings in large groups bushes near dogs, swede, outdoors, thatched roofs, lake many production of goat pen wood walls flies cane alcohol

3 Very windy Nearby Trees Bats, Sugar cane. Many zebu Buildings close Populated lake nearby to few in paddocks, to each other, river with stray scattered concrete scattered cats poultry and houses or thin swine roam wood walls shrubbery with thatched roofs

4 Dry and Near to Small Very few N/A Few zebus Clay walls with Population spread out over cold large patches of stray indoors, thatched roofs larger area, water sources city dense cats poultry and or brick walls are man-made wells greenery swine in with sheet scattered around village pens metal roofs

5 Temperate Many Grass Many Rice Many zebu Very few Very sparsely populated but and humid canals small paddies, kept indoors, concrete near capital and airport and rice birds banana some poultry houses paddies, plantation gathered in near two locations airport

5.3 OVERALL MOSQUITO ABUNDANCE

Overall, 2051 specimens were caught and 2050 were morphologically identified to species or species complex (with one being identified only to genus as identifying features had been damaged during the trapping process). Of the 2051, 93 (4.53%) were male, 1397 (68.11%) were non-bloodfed females and 561 (27.35%) were bloodfed females. Figures 13 and 14 show the overall totals and species diversity at each site over the two nights for light-trap caught and zebu-trap caught mosquitoes respectively. Figure 12 is the key for both figures.

1395 (68.02%) of samples were caught in light traps overall while 656 (31.98%) were caught in zebu traps. The mean nightly catch in a light trap was 139.5 mosquitoes (95% CI = 39.47, 239.5) and in a zebu trap was 65.6 (95% CI = 6.159, 125). This difference was not statistically significant (p=0.1055) on Wilcoxon matched-pairs signed rank test. Site 2 had the greatest number of individuals caught (782 individuals) while Site 3 had the smallest number (57 individuals). The mean number of individuals caught was 410.2 individuals per site (95% CI = 53.57, 766.8). There was a statistical significance in the number of individuals caught at each site on Friedman’s Test (P<0.0001) however Dunn’s multiple comparison test showed that this relationship was only present between Sites 2/3, 2/4 and 2/5 (P=0.0004, 0.0015 and <0.0001 respectively). Anopheles coustani Anopheles funestus Anopheles gambiae SL Anopheles maculipalpis Anopheles mascareinsis Anopheles pauliani Anopheles pharaoensis Total=30 Anopheles rufipes Anopheles squamosus Figure 10 - Species Key to Map Pie Charts Culex antennatus (Figure 9/10) Culex bitaeniorhynchus Culex decens Culex giganteus Culex poicilipes Culex quinquefasciatus Culex tritaeniorhynchus Culex univittatus Ficalbia circumtestacea Mansonia uniformis Lutzia tigripes Uranotaenia alboabdominalis Uranotaenia anopheloides Uranotaenia antsai Uranotaenia neireti Uranotaenia sp. Stegomyia (Aedes) aegypti Stegomyia (Aedes) albocephalus Stegomyia (Aedes) circumlateolus Aedomyia furfurea Aedomyia madagascarica

Figure 11 - Pie Charts Showing Mosquito Species DistributionSite 1 and General Numbers per Site in Light Traps

Anopheles coustani Anopheles funestus Anopheles gambiae SL Anopheles maculipalpis Anopheles mascareinsis Anopheles rufipes Culex antennatus Culex bitaeniorhynchus Culex decens Culex quinquefasciatus Culex univittatus N=232 Ficalbia circumtestacea Site 3 Mansonia uniformis Lutzia tigripes Anopheles coustani Uranotaenia Sitealboabdominalis 2 Anopheles gambiae SL Uranotaenia neireti Anopheles maculipalpis Uranotaenia sp. Anopheles coustani Anopheles mascareinsis Anopheles funestus Anopheles pauliani Anopheles gambiae SL Anopheles rufipes Anopheles maculipalpis Culex antennatus Anopheles mascareinsis Culex bitaeniorhynchus Anopheles pauliani Culex decens Anopheles pharaoensis Culex poicilipes Anopheles rufipes Culex univittatus Anopheles squamosus N=56 Ficalbia circumtestacea Culex antennatus Uranotaenia alboabdominalis Culex bitaeniorhynchus Uranotaenia neireti N=396 Culex decens Aedes aegypti Culex giganteus Culex poicilipes Culex tritaeniorhynchus Culex univittatus Ficalbia circumtestacea Site 4 Site 5 Mansonia uniformis Uranotaenia alboabdominalis Uranotaenia anopheloides Anopheles coustani Anopheles coustani Uranotaenia antsai Anopheles funestus Anopheles squamosus Uranotaenia neireti Anopheles gambiae SL Culex antennatus Aedes albocephalus Anopheles maculipalpis Culex giganteus Aedes circumlateolus Anopheles mascareinsis Culex quinquefasciatus Aedomyia furfurea Anopheles rufipes Culex univittatus Aedomyia madagascarica Anopheles squamosus Culex antennatus Culex poicilipes Culex quinquefasciatus Culex univittatus N=600 N=111

Figure 12 - Pie Charts Showing Mosquito Species Distribution and General Numbers per Site in Zebu Traps

Site 1

Anopheles coustani Anopheles funestus Anopheles gambiae SL Anopheles maculipalpis Anopheles mascareinsis Anopheles pauliani Anopheles rufipes Culex antennatus Culex giganteus Culex quinquefasciatus Culex univittatus N=142 Mansonia uniformis

Site 2 Site 3 Anopheles coustani Anopheles pauliani Anopheles gambiae SL Anopheles pauliani Anopheles pharaoensis Anopheles rufipes Anopheles squamosus Culex antennatus Culex bitaeniorhynchus Culex decens Culex poicilipes Culex tritaeniorhynchus N=386 Culex univittatus N=1 Mansonia uniformis

Site 5

Anopheles coustani Anopheles gambiae SL Culex antennatus Culex poicilipes

N=0

N=127

5.4 SPECIES DIVERSITY

Overall, 30 different species were identified and collected across the 5 sites. Of these 30, 29 were identified to species and one to genus (this was a single individual which has been removed from future results and discussion regarding species diversity). Of the 29 identified species, 9 were Anopheles spp. (30%), 8 were Culex spp. (26.67%), 5 were Uranotaenia spp. (16.67%), 3 were Stegomyia (Aedes) spp. (10%), 2 were Aedomyia spp. (6.67%) and 3 were other genera with a single species - Lutzia tigripes, Ficalbia circumtestacea and Mansonia uniformis (3.33%).

Site 2 had the greatest variety of species (26 species) while Site 5 had the lowest number of distinct species (8 species) with the mean being 15.8 (95% CI = 7.18, 24.42) species. Figure 15 shows the number of species by genus each site. A Friedman test showed overall there was a statistically significant difference in the number of species (P<0.0001) however further testing using Dunn’s multiple comparisons test showed that this was only present between Sites 2/4 (P=0.022) and Sites 2/5 (P=0.0045).

The majority of species caught (18 species, 60%) were known to be vectors of multiple diseases in Madagascar. Table 8 details the species present and their potential to be a disease vector37,61. Multiple vectors were found at each site with Site 2 showing the greatest vector diversity (18 species of 26, 69.23%) while Site 5 showed the lowest vector diversity while also having the highest proportion being a vector (8 species of 8, 100%). A Friedman Test showed general statistical significance in the number of vector species at each site (P=0.0006) however Dunn’s multiple comparisons test showed that this was significance was only present between sites 2/5 (P=0.0211).

Figure 13 - Number of Mosquito Species by Genus with Additional Category for Total Number of Species at each Field Site. NB: 1 individual omitted due to incomplete identification (Uranotaenia sp.)

Number of Species

c 30 e Anopheles spp.

S Culex spp.

f 20 Steogmyia (Aedes) spp.

n Aedomyia spp.

Uranotaenia spp.

f 10

Other

b Total

m 0

N 1 2 3 4 5 e e e e e it it it it it S S S S S Site

Table 8 - Summary of Caught Species and Potential Vector Role

Species Disease Transmission in Madagascar Disease Transmission in Africa Anopheles coustani Perinet virus, Rift Valley fever virus, West Nile Zika virus (Africa) virus, Babanki virus Malaria Wuchereria bancrofti Anopheles funestus Malaria Pongola virus, O’nyong nyong virus, Bwamba virus, Wuchereria bancrofti Nyando virus, Chikungunya virus, Wesselsbron virus, Bozo virus, Akabane virus, Tanga virus, Tatagiune virus, Orungo virus (Ethiopia) Anopheles gambiae s.l. Mengo virus, Ganjam virus, Tatagiune virus, As Madagascar (Africa) Ilesha virus, O’nyong nyong virus, Bwamba virus Malaria Wuchereria bancrofi Anopheles maculipalpis West Nile virus Wuchereria bancrofti (Africa) Malaria (Africa) Anopheles mascarensis Ngari virus Endemic to Madagascar Malaria Wuchereria bancrofti Anopheles pauliani Rift valley fever virus, Andasibe virus, West Endemic to Madagascar Nile virus Wuchereria bancrofti

Species Disease Transmission in Madagascar Disease Transmission in Africa Anopheles pharoensis None Birao virus, Rift valley fever virus, Ngari virus, Bangui virus, Babanki virus, Wesselsbron virus, Sanar virus (Ethiopia, Egypt, Eritrea) Wuchereria bancrofti (Ethiopia, Egypt, Eritrea) Malaria (Ethiopia, Egypt, Eritrea) Anopheles rufipes None Chikungunya, Wesselsbrons virus, Gomoka virus (Africa) Malaria (Africa) Anopheles squamosus Rift Valley fever virus, Andasibe virus Birao virus, Babanki virus (Ethiopia) Wuchereria bancrofti Culex antennatus Rift Valley fever virus, West Nile virus, Perinet Ngari virus, Babanki virus, Bagaza virus, Wesselsbron virus, Babanki virus virus, West Nile virus (Africa) Wuchereria bancrofti Culex bitaeniorhynchus None Sindbis virus, Sagiyama virus, Getah virus, Rift Valley fever virus (Africa) Culex decens West Nile virus, Babanki virus Sindbis virus, Usutu virus, Moussa virus, Bagaza virus, West Nile virus, M’Poko virus, Mossuril virus, Kamese virus and Rift Valley Fever virus(Africa) Culex giganteus None None Culex poicilipes None Ngari virus, West Nile virus, Bagaza virus, Rift Valley fever virus, Sanar Virus (Africa) Culex quinquefasciatus West Nile virus, Babanki virus, Perinet virus Rift Valley fever virus, Chikungunya, West Nile virus, Wuchereria bancrofti Mengo virus Culex tritaenorhynchus West Nile virus, Mengo virus Japanese Encephalitis virus, Sindbis virus, Ngari virus, Babanki virus, Rift Valley fever virus, Sagiyama virus, Oya virus, Akabane virus, Getah virus, Yunnan orbivirus, Banzi virus (Unspecified) Culex univittatus Mengo virus, Babanki virus West Nile virus, Babanki virus, Bagaza virus, Rift Valley fever virus (Africa) Wuchereria bancrofti (Africa) Ficalbia circumtestacea None None Mansonia uniformis Rift Valley fever virus, Babanki virus, Perinet Middleburg virus, Yata virus, Zika virus, Chikungunya virus, West Nile virus virus, O’Nyong Nyong virus, 16 other arboviruses Wuchereria bancrofti, Dirofilaria spp. (Unspecified) Lutzia tigripes None Ntaya virus, West Nile virus, Sindbis virus, Babanki virus, Bobia virus, Mossuril virus, Kamese virus (Central African Republic) Uranotaenia alboabdominalis None None Uranotaenia anopheloides None None Uranotaenia antsai None None Uranotaenia neireti None None Aedes aegypti Babanki virus, MMP 158 virus, West Nile As Madagascar (Global) virus, Yellow Fever virus, Dengue virus, Zika virus, Chikungunya virus, O’nyong nyong virus, 16 other viruses Aedes albocephalus None West Nile Virus (Africa/Middle East) Aedes circumlateolum West Nile virus Simbu viruses, West Nile virus, Spondweni virus, Pongola virus, Rift Valley fever virus (Africa) Aedomyia furfurea None None Aedomyia madagascarica West Nile virus Endemic to Madagascar

5.5 SPECIES SCREENED, NUMBER OF INDIVIDUALS AND POOL SIZES

A total of 596 individuals from 19 different species were screened. There were 85 individuals screened with the remaining 511 individuals screened in 107 pools of size ranging between 3 individuals and 5 individuals. 19 different species were screened of which there were 9 Anopheles spp., 1 Aedomyia spp., 1 Mansonia spp., 2 Aedes spp. and 6 Culex spp. Table 9 provides a summary of screening strategy.

Table 9 - Summary of Mosquitos Screened by PCR

Species Number of Individuals Screened Screening Strategy

Anopheles coustani 108 Pooled Anopheles mascarensis 38 Pooled Anopheles squamosus 40 Pooled Culex antennatus 105 Pooled Culex univittatus 151 Pooled Mansonia uniformis 17 Pooled Culex quinquefasciatus 15 Pooled Anopheles maculipalpis 17 Pooled Anopheles funestus 10 Individuals Anopheles gambiae s.l. 11 Individuals Anopheles pharoensis 7 Individuals Anopheles rufipes 11 Individuals Anopheles pauliani 6 Individuals Culex poiciplipes 10 Individuals Aedomyia madgascarica 9 Individuals Culex bitaeniorhynchus 10 Individuals Culex decens 9 Individuals Aedes albocephalus 1 Individual Aedes circumlateolum 1 Individual

5.6 PCR ANALYSIS

None of the non-template controls (negative controls) returned significantly positive in a way which would obscure analysis while all the positive controls resulted in successful amplification and resulting fluorescent curves. Table 10 shows a list of significantly positive results alongside details of the sample.

Two Culex decens individuals and one Aedes albocephalus returned low-level positive results for Rift Valley fever virus on an initial PCR however only the Culex decens individuals showed the same when the assay was repeated. Additionally, when these were run using gel electrophoresis (Invitrogen) the Culex decens individuals had a similar PCR product size (~200 bp) to the positive control as shown in Figure 17 A/B.

Three Anopheles gambiae s.l. individuals showed positivity for CHIKV on PCR assay however showed significantly larger PCR products (~300 bp) on gel electrophoresis compared to the CHIKV positive control (~150 bp) as shown in Figure 18 A/B.

Finally, on Plasmodium falciparum assays a total of seven samples across a variety of came back positive which were then confirmed on PCR gel electrophoresis, as shown in Figure 19 A/B.

Other results of note that lay outside of the cut-off for “positive” were a number of results on the Pan-Flavi and Pan-DENV assays which showed varying Cqs and Tms. This was important as both were developed in order to detect a wide variety of viruses (Pan-Flavi to broadly detect flaviviruses and Pan-DENV to detect all dengue serotypes). As such, comparing Tm to that of the positive control (particularly in the case of Pan-Flavi) with a cut-off would lead to the exclusion of potentially positive samples which have a different virus/serotype to the control. On the other hand, detailed melt curve analysis was required as the amount of degeneracies present in the assays could lead to non-specific amplification and fluorescence. This is shown in Figure 16.

Table 10 - Summary of Positive Results on PCR

Sample Sample Details (Site/Night/Trap) Pan-Flavi Pan-DENV ZIKV CHIKV WNV YFV RVFV Pf Culex decens 2/2/LT/Individual ------+ - Culex decens 3/2/LT/Individual ------+ - Anopheles coustani 5/2/ZT/Pool of 5 ------+ Anopheles gambiae s.l. 3/1/LT/Individual ------+ Anopheles gambiae s.l. 3/1/LT/Individual ------+ Anopheles gambiae s.l. 3/1/LT/Individual ------+ Anopheles squamosus 4/1/LT/Pool of 5 ------+ Anopheles rufipes 2/2/LT/Individual ------+ Anopheles funestus 2/2/LT/Individual ------+

Figure 14 - Melt Curve Analysis of Pan-Flavi PCR Screen for Samples 1-80 Alongside Positive and Negative Controls

Figure 15A: Melt Curve Analysis of Rift Valley Fever virus screening samples

Positive Control

Aedes Negative Control Culex decens A albocephalus Culex decens B

Figure 17B: Rift Valley Fever sample screening on PCR Gel Electrophoresis

M=Size Ladder, 1=Aedes albocephalus, 2=Culex decens A, 3=Culex decens B, 4=Positive Control, 5=Negative Control

Figure 16A: Melt Curve Analysis of Chikungunya virus screening samples

Positive Control

Anopheles gambiae s.l.

Figure 18B: Chikungunya sample screening on PCR Gel Electrophoresis

M=Size Ladder, 1-3=Anopheles gambiae s.l., 4=Positive Control, 5=Negative Control

Figure 17A - Top: Melt Curve Analysis of Plasmodium falciparum virus screening assay 1, Bottom: Melt Curve Analysis of Plasmodium falciparum virus screening assay 2

Positive Control Anopheles funestus Anopheles gambiae s.l. Anopheles coustani Anopheles rufipes

Positive Control

Anopheles squamosus

19B: PCR gel electrophoresis of positives from both assays

M=Size Ladder, 1=Anopheles funestus, 2- 4=Anopheles gambiae s.l., 5=Anopheles rufipes, 6=Anopheles coustani, 7=Anopheles squamosus, 8=Positive control, 9/10=Negative controls

6 DISCUSSION

6.1 MOSQUITO ABUNDANCE

The lack of statistical significance between numbers caught in each trap type per night, both overall and at most locations, may be due to a number of reasons. The first option may be that there really is no relationship between number of individuals caught and the type of trap used in night trapping. However, this conclusion cannot be confirmed and the lack of statistical significance may also be due to the limited number of nights spent trapping at each site. Additionally, the proximity of traps to each other in some sites (particularly of traps 1 and 2 which were placed near zebu storage) could have led to a certain degree of sampling bias. As such, it is difficult to definitively assess the potential difference in individuals caught at each site.

However, the number of mosquitoes caught and the species diversity both showed statistically significant correlations with the site localities. This could be explained by the differences in temperature and humidity which also showed statistically significant differences between sites. Adult mosquito activity has been known to decrease generally where temperature and humidity is lower while mortality rates tend to be higher, as such it would be expected that increased mosquito populations are present at higher humidities and temperatures.62,63 While this was overall the case (with first Site 2 and then Site 1 having the highest number of individuals caught) the location with the highest temperature, Site 3, had the lowest number of individuals caught. However, this site had the lowest median humidity, suggesting that humidity may play a larger role in adult mosquito population size than temperature during the dry winter season in Madagascar. It is also important to note that the species caught were active even in areas with low temperatures suggesting some species may be cold tolerant.

Qualitative factors from each site should be taken into account, despite the fact that it may be difficult to quantify any statistically significant relationships. One factor which may be relevant is the population density of zebu and humans (two blood meal sources for females). However, area most densely populated by zebus and humans, Site 3, had the lowest overall mosquito abundance. One potential explanation is the proximity of Site 3 to a large colony of insectivorous bats which may have predated any mosquitoes that were active in this locality.

6.2 VECTOR DIVERSITY

Mosquito species that have previously been associated with pathogen transmission were found at each site. Dominant vector species were actually confined to specific regions with Anopheles mascarensis (Site 1), Culex antennatus (Sites 2 and 5), Anopheles gambiae s.l. (Site 3) and Culex univitattus (Site 4) being the dominant species in different regions. Overall, it is important to note that the species with the largest populations overall and in each site are all vector species. Each of these mosquito species is a vector for different diseases suggesting that the most important mosquito-borne disease in each area may differ. For example, Anopheles mascarensis and Anopheles gambiae s.l. are major malaria vectors while Culex antennatus and Culex univitattus are West Nile virus and Rift Valley Fever virus vectors.

This information is important as anecdotal discussions with local government and communities suggested that there may be decreased use of insecticide-treated netting during this dry season due to a perceived reduction in risk. Furthermore, the wide diversity of mosquito species caught suggests that current strategies to control mosquito-borne disease in Madagascar, currently focusing on IRS and LLINs (which may be effective at controlling malaria overall), may not be effective in all parts of the island or for targeting different disease vectors as these vectors will have varying behaviours.

The data also suggests that bio-climatic zone may not be the most important factor in determining dominant species. For example, sites 2 and 3 are both in the West region with a dry climate but have differing dominant species. Similarly, sites 2 and 5 are in different biogeographic regions and have differing climates but have the same dominant mosquito species. It is possible that the microclimate, temperature and humidity, plays a role, however, sites 2 and 5 sharing the same dominant species despite having significantly different temperature and humidity readings further suggests that other factors are at play.

The number of species at each site was also statistically significant suggesting that site factors influence the variety of species which are capable of existing. Again, temperature and humidity may play a significant role as survival in locations differs based on species adaptation to conditions. This has already been shown for Stegomyia (Aedes) aegypti and Stegomyia (Aedes) albopictus in Madagascar and may explain the differences here as well64. Stegomyia (Aedes) aegypti and Stegomyia (Aedes) albopictus were likely excluded from this project by sampling bias introduced by exclusively performing night trapping.

Additionally, the differences in population densities of humans and livestock across the sites may influence the distribution of mosquito species due to differences in host selection65. This variation may be significant for public health measures, particularly in Site 5 which has proximity to the international airport and had vectors accounting for the entirety of the catches over both nights.

6.3 PATHOGEN SCREENING

Pooling was performed in order to maximize the potential positivity, something commonly done when screening wild mosquitoes as infection rates for both arboviruses and malaria parasites is quite low. This means that any positive results are of importance as they likely show an underestimated, but important, amount of pathogen in the wild mosquito population66–68. Furthermore, any positives are significant as there is a definite potential for transmission to humans as samples are non-bloodfed females. This suggests that detected pathogens are within the mosquito itself (rather than a blood meal) and that the mosquito was ready to take another blood meal.

The Pan-Flavi assay, which was designed to amplify the NS5 gene of all known flaviviruses, showed a lot of amplification at a wide variety of Cqs which required extensive melt-curve analysis owing to the number of degeneracies in the assay itself. While this increases sensitivity it decreases specificity by increasing non-specific amplification and risk of contamination. However, the fact that amplification varied significantly (both in Cq and Tm) suggests that there may also be mosquito-only flaviviruses. As there are more than 70 different viruses with the genus Flavivirus, sequencing of PCR products would be required to determine the specific virus.

Although Culex decens has previously been shown to harbor RVFV outside of Madagascar, this is the first time this species has been RVFV positive in Madagascar. Furthermore, the two Culex decens were found at different sites suggesting that the risk for ongoing transmission may not be contained to specific areas.

However, more interesting is the variety of positively infected mosquitoes for Plasmodium falciparum in every site present. This is important as, from anecdotal discussions in the field, the local communities do not think there is a risk of malaria transmission in the dry season. This is reflected in the decreased adherence to disease control strategies (particularly LLINs).

On a global scale, the 2016 Zika virus epidemic in South America demonstrates that rare or novel viruses can pose a threat to international health. Zika is at risk of entering Madagascar as isolates have been drawn from a number of mosquitoes in continental Africa.69–71 This is due to its proximity to Africa and the presence of numerous air and sea ports. Furthermore, there are a number of mosquito species in Africa from which Zika virus has been isolated also exist in Madagascar, including the presumed main South American vector Stegomyia (Aedes) aegypti.37,69–71 A number of common mosquitoes in areas with Zika have yet to be screened for Zika, suggesting the potential for other vectors to exist70. As such, despite no mosquito samples showing amplification for Zika in this project it is important to continue screening in the future.

However, Madagascar has a number of rare viruses which could follow the same trajectory as Zika virus if exported. Examples of these are Babanki, Ngari, Perinet, Bunyawemra, Wesselbron’s or Ileshi virus72. The risk these diseases pose currently is quite low, although, it is important to continually review their presence in order to properly reassess potential risk levels to global health. Furthermore, it is likely that a number of viruses which may infect humans have yet to be identified (particularly if they share symptoms with the more common arboviruses and are being diagnosed symptomatically). Increasing this risk is the fact that the vector species for these viruses are found world-wide.

6.4 LIMITATIONS

This study was limited by both time and funding. The entomological survey was only two nights at each site which may have reduced numbers sampled. Additionally, due to daytime travel it was not practical to perform any daytime sampling, meaning day-biting mosquitoes, importantly Stegomyia (Aedes) aegypti and Stegomyia (Aedes) albopictus, would not have been sampled. Furthermore, the limitation in time also meant that the entomological survey was restricted to 5 sites across the North to the Central Highlands, omitting the Southern half of the island.

This lack of funding and time also limited the work that was possible in the laboratory and led to the creation of pools. Pooling can complicate PCR analysis and it was no possible to screen all mosquitoes collected. However, the pooling strategy was aimed to provide the most amount of information on pathogen infection rates. Additionally, the nature of qPCR analysis requires the interpretation of raw data and this method of pathogen detection has sensitivity limits, potentially preventing the detection of very low levels of pathogens.

6.5 SUMMARY

Currently, a lot is known about the overall diversity of mosquito species in Madagascar and the ecological factors that contribute to it, particularly during the wet season. However, this information doesn’t exist to the same degree for the dry season. Additionally, while there has been work done on identifying new vectors for disease, this has been limited in scope. Finally, there is very little information on risk of disease transmission in the dry season.

This initial short-term entomological survey of mosquito populations in Madagascar during the dry season accompanied by PCR screening reveals a few things of note. Firstly, despite local beliefs there were in fact vector populations present during the dry season at all five research sites. Furthermore, there were also detectable levels of malaria in a variety of the species screened suggesting that there may be low levels of ongoing transmission outside of the main malaria season.

7 RECOMMENDATIONS

There are a few important next steps following this project for research. Firstly, on the two Culex decens individuals positive for RVFV, sequencing is required in order to confirm positivity46,73,74. Additionally, a second Plasmodium falciparum assay which evaluates a second gene, 18S ssuRNA gene, alongside the results of the Cox 1 gene PCR analysis would confirm positive samples75.

Additionally, blood meal analysis on the bloodfed individuals collected during this study could provide interesting data regarding host preference, particularly if there are differences within the same species across different sites. This could be combined with screening for disease in species which tested positive during this project to determined levels of risk in human populations at each site.

Finally, due to the equipment and expertise present in the laboratory it would be interesting to perform Wolbachia spp. screening on Anopheles species collected to determine rates of infection. While Wolbachia spp. work in Anopheles spp. is still in its infancy, the wide variety of species collected would allow a comprehensive Wolbachia spp. prevalence study in numerous Anopheles species.

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9 APPENDIX

Table 11 - Summary of Malagasy Mosquito Species and Diseases They Transmit

Species Madagascar Rest of World Anopheles coustani Perinet virus, Rift Valley fever virus, West Nile Zika virus (Africa) virus, Babanki virus Malaria Wuchereria bancrofti Anopheles tenebrosus None Malaria Anopheles fusicolor Babanki virus, Perinet virus, Rift Valley fever None virus Wuchereria bancrofti Anopheles gambiae s.s Mengo virus, Ganjam virus, Tatagiune virus, As Madagascar (Africa) Ilesha virus, O’nyong nyong virus, Bwamba virus Malaria Wuchereria bancrofi Anopheles arabiensis Mengo virus, Ganjam virus, Tatagiune virus, As Madagascar (Africa) Ilesha virus, O’nyong nyong virus, Bwamba virus Malaria Wuchereria bancrofi Anopheles merus Mengo virus, Ganjam virus, Tatagiune virus, As Madagascar (Africa) Ilesha virus, O’nyong nyong virus, Bwamba virus Malaria Wuchereria bancrofi Anopheles brunnipes None Malaria (Ethiopia) Anopheles flavicosta None Wuchereria bancrofti (Ethiopia) Plasmodium spp (Ethiopia) Middleburg virus (Ethiopia) Anopheles funestus Malaria Pongola virus, O’nyong nyong virus, Bwamba virus, Wuchereria bancrofti Nyando virus, Chikungunya virus, Wesselsbron virus, Bozo virus, Akabane virus, Tanga virus, Tatagiune virus, Orungo virus (Ethiopia) Anopheles squamosus Rift Valley fever virus, Andasibe virus Birao virus, Babanki virus (Ethiopia) Wuchereria bancrofti Anopheles pharaoensis None Birao virus, Rift valley fever virus, Ngari virus, Bangui virus, Babanki virus, Wesselsbron virus, Sanar virus (Ethiopia, Egypt, Eritrea) Wuchereria bancrofti (Ethiopia, Egypt, Eritrea) Malaria (Ethiopia, Egypt, Eritrea) Anopheles maculipalpis West Nile Virus Wuchereria bancrofti (Africa) Malaria (Africa) Anopheles rufipes None Chikungunya, Wesselsbrons virus, Gomoka virus (Africa) Malaria (Africa) Anopheles pretoriensis None Wesselbrons virus, Ngari virus (Senegal) Malaria (Africa) Anopheles mascarensis Ngari virus Endemic to Madagascar Malaria Wuchereria bancrofti

Anopheles pauliani Rift valley fever virus, Andasibe virus, West Nile Endemic to Madagascar virus Wuchereria bancrofti Aedomyia madagascarica West Nile virus Endemic to Madagascar Aedes durbanensis None Rift Valley fever virus (Kenya) Aedes domesticus None Bunyamwera virus (Cameroon)

Aedes fowleri None Bagaza virus, Zika virus, Kedougou virus, Simbu viruses, Pongola virus, Rift valley fever virus (Africa) Aedes dalzieli None Rift Valley fever virus, Dengue 2 virus, Chikungunya virus, Babanki virus, Middleburg virus, Ndumu virus, Bagaza virus, Wesselsbron virus, West Nile virus, Bouboui virus, Kedougou virus, Bunyamwera virus, Shokwe virus, Ngari virus, Simbu viruses, Pongola virus, Zika virus (Africa) Undetermined Nematoda (Africa) Aedes masoalensis Mengo virus Endemic to Madagascar Aedes natronius None Uganda S virus Aedes argenteopunctatus Dakar Bat virus Semlinki forest virus, Nkolbisson virus, Shokwe virus, Middleburg virus, Dengue 2 Virus, Chikungunya virus, Wesselsbron virus, Bunyamwera virus, Pongola virus, Gomoka virus, Ngari virus (Africa) Undertemined Nematoda (Africa Aedes fryeri None Spondweni virus (Mozambique) Aedes Madagascarensis West Nile virus Endemic to Madagascar Aedes vittatus None Dengue 2 virus, Chikungunya virus, Zika virus, Yellow Fever virus, Wesselbrons virus, Saboya virus, Ngari virus, Simbu viruses, Pongola virus, Gomoka virus, Sindbis virus Aedes circumlateolum West Nile virus Simbu viruses, West Nile virus, Spondweni virus, Pongola virus, Rift Valley fever virus (Africa) Aedes ambreensis Unclassived MMP 158 virus Endemic to Madagascar Aedes cartroni Mengo virus Endemic to Madagascar Aedes aegypti Babanki virus, MMP 158 virus, West Nile virus, As Madagascar (Global) Yellow Fever virus, Dengue virus, Zika virus, Chikungunya virus, O’nyong nyong virus, 16 other viruses Aedes albopictus Babanki virus, Simbu viruses, Cache Valley West Nile virus (North America) virus, La Crosse virus, Potosi virus, Chikungunya, Dengue virus, Dengue virus, Banna virus Coquillettidia grandideri Rift Valley fever virus Endemic to Madagascar Coquillettidia Metallica None West Nile virus, Babanki virus, Middleburg virus (Africa) Culex antennatus Rift Valley fever virus, West Nile virus, Perinet Ngari virus, Babanki virus, Bagaza virus, virus, Babanki virus Wesselsbron virus, West Nile virus (Africa) Wuchereria bancrofti Culex decens West Nile virus, Babanki virus Sindbis virus, Usutu virus, Moussa virus, Bagaza virus, West Nile virus, M’Poko virus, Mossuril virus and Kamese virus (Africa) Culex duttoni None Yaounde virus, Ar 11266 B virus, Uganda S virus (Africa) Culex guiarti None Babanki virus, West Nile virus, 10 other viruses

Culex neavi None 16 viruses Culex pipiens None Rift Valley fever virus, Sindbis virus (Africa) West Nile virus (North America) Culex quinquefasciatus West Nile virus, Babanki virus, Perinet virus Rift Valley fever virus, Chikungunya, West Nile virus, Wuchereria bancrofti Mengo virus Culex scottii West Nile virus No Information Culex simpsoni Rift Valley fever virus No information Culex sitiens None Mossuril virus (Mozambique) Murray Valley Encephalitis virus, Japanese Encephalitis virus, Sepik virus and Sindbis virus (New Guinea) Culex tritaeniorhynchus West Nile virus, Mengo virus Japanese Encephalitis virus, Sindbis virus, Ngari virus, Babanki virus, Rift Valley fever virus, Sagiyama virus, Oya virus, Akabane virus, Getah virus, Yunnan orbivirus, Banzi virus (Unspecified) Culex perfuscus None 19 viruses (Africa) Culex univittatus Mengo virus, Babanki virus West Nile virus, Babanki virus, Bagaza virus, Rift Valley fever virus (Africa) Wuchereria bancrofti (Africa) Culex vansomereni Babanki virus, Rift Valley fever virus West Nile virus (Africa, laboratory conditions) Culex weschei None West Nile virus, Mossuril virus, Sindbis virus, Chikungunya, Babanki virus, Wesselsbron virus, Mengo virus (Africa) Culex cinereus None 16 viruses Culex nebulosus None Ntaya virus, Babanki virus, Middleburg virus, Bagaza virus, Yaounde virus, M’Poko virus, Tai virus (Africa) Culex mouchetti None Ntaya virus (Africa) Culex annulioris Rift Valley fever virus Sindbis virus, Middleburg virus (Africa) Culex bitaeniorhynchus None Sindbis virus, Sagiyama virus, Getah virus, Rift Valley fever virus (Africa) Culex poicilipes None Ngari virus, West Nile virus, Bagaza virus, Rift Valley fever virus, Sanar Virus (Africa) Culex rubinotus None Rift Valley fever virus, Uganda S virus, Germiston virus, Banzi virus, Witwatersand virus (Africa) Eretmapodites oedipodeios None Eret 147 (Cameroon) Eretmapodites Mengo virus Rift Valley fever viruses, Unspecified Bunyaviridae, Quinquevittatus Unspecified Flaviviridae (Africa) Eretmapodites None Okola virus (Kenya) subsimplicipes Lutzia tigripes None Ntaya virus, West Nile virus, Sindbis virus, Babanki virus, Bobia virus, Mossuril virus, Kamese virus (Central African Republic) Mansonia Africanus None Spondweni virus, Middleburg virus, Pongola virus, Rift Valley fever virus and 13 other viruses (Africa) Brugia patei (Africa) Mansonia Uniformis Rift Valley fever virus, Babanki virus, Perinet Middleburg virus, Yata virus, Zika virus, virus, West Nile virus Chikungunya virus, O’Nyong Nyong virus, 16 other Wuchereria bancrofti, Dirofilaria spp. arboviruses (Unspecified) Mimomyia hispida None Babanki virus, Bagaza virus, West Nile virus (Africa) Mimomyia splendens None Babanki virus, Bagaza virus, West Nile virus (Africa)

Table 12 - Detailed Summary of Trap Placement

SITE/NIGHT TRAP GPS CO-ORDINATES ALTITUDE (M) 1 LT1 12°45.522' S 049°14.193' E 364

LT2 12°45.526' S 049°14.195' E 365

LT3 12°45.500' S 049°14.190 E 365

LT4 12°45.484' S 049°14.186' E 362

LT5 12°45.501' S 049°14.268' E 351

LT6 12°45.503' S 049°14.281' E 371

LT7 12°45.455' S 049°14.213' E 357

LT8 12°45.448' S 049°14.208' E 357

ZT 12°45.520' S 049°14.197 E 364 2 LT1 16°21.662' S 046°59.344' E 76

LT2 16°21.656' S 046°59.396' E 76

LT3 16°21.657' S 046°59.368' E 78

LT4 16°21.664' S 046°59.371' E 77

LT5 16°21.669' S 046°59.387' E 80

LT6 16°21.654' S 046°59.385' E 81

LT7 16°21.687' S 046°59.324' E 68

LT8 16°21.644' S 046°59.394' E 68

ZT 16°21.663' S 046°59.339' E 76 3 LT1 17°01.378' S 046°45.617' E 4

LT2 17°01.384' S 046°45.633' E 6

LT3 17°01.409' S 046°45.598' E 3

LT4 17°01.412' S 046°45.598' E 3

LT5 17°01.517' S 046°45.586' E 3

LT6 17°01.419' S 046°45.572' E 3

LT7 17°01.462' S 046°45.593' E 3

LT8 17°01.467' S 046°45.584' E 4

ZT 17°01.379' S 046°45.607' E 5

SITE/NIGHT TRAP GPS CO-ORDINATES ALTITUDE (M) 4 LT1 18°19.586'S 047°06.333' E 1232

LT2 18°19.580' S 047°06.334' E 1233

LT3 18°19.594' S 047°06.343' E 1231

LT4 18°19.597'S 047°06.338' E 1232

LT5 18°19.594' S 047°06.335' E 1230

LT6 18°19.598'S 047°06.336' E 1230

LT7 18°19.564' S 047°06.338' E 1230

LT8 18°19.574' S 047°06.340' E 1230

ZT 18°19.586'S 047°06.333' E 1232 5 LT1 18°47.316' S 047°28.684' E 3

LT2 18°47.320' S 047°28.682' E 3

LT3 18°47.372' S 047°28.704' E 3

LT4 18°47.368' S 047°28.729' E 4

LT5 18°47.338' S 047°28.660' E 3

LT6 18°47.338' S 047°28.666' E 3

LT7 18°47.359' S 047°28.726' E 3

LT8 18°47.370' S 047°28.686' E 3

ZT 18°47.321' S 047°28.676' E 3

Table 13 - Summary of Trap Placement Times and Duration of Trap Activity

NIGHT ONE NIGHT TWO SITE Trap Trap Up Trap Down Duration Trap Up Trap Down Duration 1 LT1 4:26 PM 6:23 AM 13h57m 4:12 PM 5:58 AM 12h46m

LT2 4:22 PM 6:23 AM 14h01m 4:14 PM 6:00 AM 12h46m

LT3 4:34 PM 6:21 AM 13h47m 4:17 PM 5:55 AM 12h38m

LT4 4:36 PM 6:21 AM 13h45m 4:19 PM 5:55 AM 12h36m

LT5 3:54 PM 6:10 AM 14h16m 4:04 PM 5:55 AM 12h51m

LT6 4:00 PM 6:06 AM 14h6m 4:06 PM 5:50 AM 12h44m

LT7 4:13 PM 6:03 AM 13h50m 4:02 PM 5:46 AM 12h44m

LT8 4:12 PM 6:03 AM 13h51m 4:02 PM 5:46 AM 12h44m

ZT 6:05 PM 5:26 AM 11h21m 6:05 PM 5:26 AM 11h21m 2 LT1 4:56 PM 6:50 AM 13h54m 4:52 PM 6:26 AM 13h34m

LT2 4:58 PM 6:52 AM 13h54m 4:54 PM 6:29 AM 13h35m

LT3 5:15 PM 6:36 AM 13h21m 5:10 PM 6:12 AM 13h02m

LT4 5:18 PM 6:34 AM 13h16m 5:23 PM 6:10 AM 12h47m

LT5 5:28 PM 6:25 AM 12h57m 5:15 PM 6:04 PM 12h49m

LT6 5:30 PM 6:32 AM 13h02m 4:17 PM 6:00 PM 13h43m

LT7 5:06 PM 6:41 AM 13h35m 4:58 PM 6:20 AM 13h22m

LT8 5:08 PM 6:45 AM 13h37m 5:00 PM 6:21 AM 13h21m

ZT 6:45 PM 5:26 AM 10h41m 6:30 PM 5:35 AM 11h05m

NIGHT ONE NIGHT TWO SITE Trap Trap Up Trap Down Duration Trap Up Trap Down Duration 3 LT1 6:06 PM 6:00 AM 11h54m 5:04 PM 6:03 AM 12h59m

LT2 6:10 PM 6:09 AM 11h59m 5:07 PM 6:04 AM 12h57m

LT3 6:16 PM 6:14 AM 11h58m 5:23 PM 6:09 AM 12h46m

LT4 6:20 PM 6:17 AM 11h57m 5:26 PM 6:08 AM 12h42m

LT5 6:53 PM 6:21 AM 11h28m 5:18 PM 6:13 AM 12h55m

LT6 6:48 PM 6:24 AM 11h36m 5:20 PM 6:14 AM 12h54m

LT7 6:30 PM 6:33 AM 12h03m 5:35 PM 6:19 AM 12h44m

LT8 6:34 PM 6:38 AM 12h04m 5:35 PM 6:19 AM 12h44m

ZT 8:04 PM 6:00 AM 09h56m 8:06 PM 5:55 AM 09h53m 4 LT1 6:57 PM 6:03 AM 11h06m 4:43 PM 6:06 AM 13h23m

LT2 6:59 PM 6:03 AM 11h04m 4:44 PM 6:08 AM 13h22m

LT3 6:55 PM 6:21 AM 11h26m 4:48 PM 6:14 AM 13h26m

LT4 6:53 PM 6:19 AM 11h26m 4:48 PM 6:13 AM 13h25m

LT5 7:05 PM 6:11 AM 11h06m 4:51 PM 6:13 AM 13h22m

LT6 7:09 PM 6:12 AM 11h03m 4:54 PM 6:17 AM 13h23m

LT7 7:02 PM 6:06 AM 11h04m 4:57 PM 6:17 AM 13h20m

LT8 7:04 PM 6:05 AM 11h01m 4:59 PM 6:21 AM 13h22m

ZT 7:55 PM 6:00 AM 10h05m 7:04 PM 6:10 AM 11h06m 5 LT1 5:49 PM 6:36 AM 12h47m 4:54 PM 6:30 AM 13h36m

LT2 5:50 PM 6:34 AM 12h44m 4:57 PM 6:30 AM 13h33m

LT3 6:03 PM 6:27 AM 12h24m 4:53 PM 6:18 AM 13h25m

LT4 6:06 PM 6:25 AM 12h19m 4:35 PM 6:14 AM 13h39m

LT5 5:40 PM 6:31 AM 12h51m 4:48 PM 6:25 AM 13h37m

LT6 5:43 PM 6:31 AM 12h48m 4:51 PM 6:25 AM 13h34m

LT7 6:09 PM 6:26 AM 12h17m 4:40 PM 6:14 AM 13h34m

LT8 5:58 PM 6:29 AM 12h31m 4:45 PM 6:28 AM 13h43m

ZT 6:35 PM 6:34 AM 11h59m 4:59 PM 6:10 AM 13h11m

Table 14 - Full List of Materials Used in Laboratory

Material Brand Product/Model Code Batch Code Notes Dissecting Microscope Olympus SZ51 N/A Used in Lab for Photography Centrifuge Eppendorf 5810 N/A Used in Lab for centrifuging spin columns, calibrated 06/07/2016 by Arena Instrumentation Fume Hood Astec BFC20-001 N/A Used in Lab, calibrated 18/7/16 by Walker Safety Cabinets Thermal Cycler Bio-Rad T100 N/A Used in Lab, safety tested November 2015 Pipettes Eppendorf Research Plus N/A Used in Lab, calibrated 10/7/2016 by Starlab Electronic Pipette Eppendorf Multipette M4 N/A Used in Lab, calibrated 10/7/2016 by Starlab Centrifuge Eppendorf 5418R N/A Used in Lab for centrifuging phase separations, safety tested November 2015 Centrifuge VWR Mini-Star Silverline N/A Used in Lab for centrifuging individual reagents and mixes, safety tested November 2015 Vortex Grant-Bio PV-1 N/A Used in Lab for centrifuging individual reagents and mixes Centrifuge VWR PCR Plate Spinner N/A Used in Lab for centrifuging PCR plates, safety tested November 2015 PCR Machine Roche LightCycler 96 N/A Used in Lab Bead Beater Qiagen TissueLyser II N/A Used in Lab, safety tested November 2015 Stainless Steel Beeds Qiagen 5 mm N/A Used in Lab RNeasy 96 Kit Qiagen 74181 154017010 Used in Lab Transcriptor First Strand Roche 04 897 030 001 11831820 Used in Lab cDNA Synthesis Kit Triazol (TRI Reagent) Sigma 93289-100ML BCBQ5065V Used in Lab BCBL0850V Primers IDT Various Various Used in Lab FastStart Essential DNA Roche 06 402 682 001 N/A Used in Lab Probe SYBR Green Roche 04 887 352 001 N/A Used in Lab PCR Grade Water Roche 04 897 030 001 N/A Used in Lab Nuclease Free Water Bio-Rad 4206448 720001682 Used in Lab Chloroform Sigma-Aldrich C2432-500ML SHBF3553V Used in Lab E-Gel EX with SYBR Gold Invitrogen G402002 U19016 Used in Lab, Expiry 2% Agarose 19/10/2016 E-Gel iBase PCR Machine Invitrogen G6400 N/A Used in Lab

10 STUDENT’S QUESTIONNAIRE

Candidate No: 109210 MSc Medical Entomology for Disease Control

Project Supervisor: Dr. Thomas Walker

Project Title: Diversity, abundance and prevalence of medically important pathogens of mosquitoes caught during the dry winter season in Madagascar

As part of our assessment procedure for student projects we are asking you to complete the following short questionnaire. Please tick the most appropriate statements in each section and bind it into your project. A copy of this questionnaire must be bound into your finished project report.

Who initiated the project?

My supervisor

How much help did you get in developing the project?

none: I decided on the design alone

some: I used my initiative but was helped by suggestions from my supervisor

substantial: My supervisor had most say, but I added ideas of my own

maximal: I relied on the supervisor for ideas at all stages

not applicable: the nature of the project was such that I had minimal opportunity to contribute to the design

How much help did you get in carrying out the work for the project?

none: I worked alone with no supervisor input

minimal: I worked alone with very little supervisor input

appropriate: I asked for help when needed

substantial: the supervisor gave me more assistance than expected

excessive: the supervisor had to give me excessive assistance to enable me to get data

What was the degree of technical difficulty involved?

slight: data easily obtained

moderate: data were moderately difficult to obtain

substantial: data were difficult to obtain

How much help were you given in the analysis and interpretation of any results?

none

standard: My supervisor discussed the results with the me and advised on statistics and presentation

substantial: My supervisor pointed out the significance of the data and told me how to analyse it

How much help were you given in finding appropriate references?

none

some: only a few references were provided

substantial: most references were given by my supervisor

maximal: the supervisor supplied all the references used by me

How much help did you get in writing the report?

none: my supervisor did not see the report until it was submitted

minor: my supervisor saw and commented on parts of the report

standard: my supervisor saw and commented on the first draft of the report

substantial: my supervisor gave more assistance than standard

How much time was spent on the project?

too little to expect adequate data*

sufficient

too much*

During the course of the work was your contact with your supervisor

Daily

Weekly

Monthly

Varied but at regular intervals

Never

Was this contact with your supervisor

too infrequent

infrequent but sufficient

frequent but not excessive

excessive

Overall my experiences were incredibly positive across all dimensions. Field work was planned smoothly in advance with contingencies in place for a variety of issues. Issues that arose in the field were dealt with swiftly by my team and I, either by following a plan already prepared or by using our personal knowledge and skills. On arrival back in the laboratories at LSHTM I was taught by my supervisor on how to act independently and on my own to collect data. However, I never felt like I was abandoned or out of my depth as I knew I could go to his office and ask for help if necessary (however, where possible I tried to solve issues myself). When it comes to data interpretation and statistical significance, I deduced the majority of the tests I should use however my supervisor advised me on which statistical software package I should use. Finally, my supervisor helped me by looking at my report draft and giving me advice.

However, there was one main issue which involved the administration at LSHTM. In particular, this questionnaire was only sent to me and my fellow students on the 5th of September 2016, two days before final submission. This is particularly frustrating for students who submitted early because they had to begin other work. It is a shame that this occurred as it is a bit of a negative note to end what has been an excellent project period and more generally a thoroughly enjoyable year with the School.

THIS QUESTIONNAIRE MUST BE INCLUDED INTO YOUR PROJECT REPORT