Virus Jumping

Virus Jumping:

How Viruses Move from One Species to Another

Megan W Howard Biological Sciences University of Alaska, Anchorage Viruses Inﬂuenza A

SARS coronavirus

SARS-CoV SARS-Coronavirus (SARS-CoV)

• 2002-2003, Guangdong province, China • Atypical viral pneumonia • >8000 cases, >800 dead. ~10% mortality • SARS-like viruses in palm civet's, raccoon dogs, bats • Ansera found in blood from before epidemic

Zoonosis Downloaded from rstb.royalsocietypublishing.org on December 12, 2011

1066 G.-p. Zhao SARS molecular epidemiology

2 0 Foshan 6 4 Shenzhen 2 +Heyuan 0 2 0 Jiangmen 6 4 Zhongshan 2 0 10 8 6 4 Guangzhou 2 0 2 0 Zhaoqing 16 Nov 24Nov 02 Dec 10 Dec 18 Dec 26 Dec 03 Jan 11 Jan 19 Jan 27 Jan 04 Feb 12 Feb 20 Feb 28 Feb onset date 2002 2002 2002 2002 2002 2002 2003 2003 2003 2003 2003 2003 2003 2003 31 Jan 2003 21 Feb 2003 60 Guangzhou 50 Shenzhen Foshan no. of SARS cases Guangdong Province Jiangmen Heyuan 40 Zhongshan Zhaoqing Guangzhou 30 Dongguan N 20

early phase middle phase late phase

beginning of beginning of Hospital ZS-2 hotel M outbreak outbreak

FigureZhou et al. Phil. 1. The triphasicTrans SARS. R. Soc. B 2007 362, 1063-1081 epidemic in Guangdong Province, China. Shown are the number of daily documented SARS cases reported from individual cities of the Guangdong Province, China, up to February 2003. The early, middle and late phases of the epidemic are defined in the text. The map shows the geographical distribution of cases belonging to the early phase by administrative districts of Guangdong Province. Original epidemiological data were collected and analysed by the Guangdong Center for Disease Control and Prevention. The cases reported from the cities of Heyuan and Shenzhen were combined and treated as Shenzhen cases, because the Heyuan index case was infected in Shenzhen, and after this nosocomial infection no additional infections were reported in Heyuan. The order of the cities is arranged from top to bottom based on the disease onset date of their respective index cases, starting from the earliest to the latest dates of onset. Adapted from Chinese (2004). Because the three patients were received by the same The third patient, ZZF, is a 44-year-old business- hospital within one week, it was difficult to identify the man who specialized in the wholesale of fish. His onset exact transmission path for individual patients. Later, date was 22 January 2003. First, he visited a local clinic viral genomic sequencing data (Chinese 2004) indi- on 26 January 2003 without notable infectious disease cated that only one kind of viral genotype was observed symptoms and was then admitted to the HZS-2 in the secondary and tertiary infections of the HZS-2 Hospital on 30 January 2003. Besides other atypical Hospital and other related hospitals (see below). pneumonia syndromes, he began to have diarrhoea on Therefore, the following epidemiological facts are 1 February and was transferred to the HZS-3 Hospital. critical for chasing the origin of this viral lineage. During his less than 48 h stay in the HZS-2 Hospital, The onset date for SGQ was 8 January 2003 and he he infected more than 30 hospital staff members and was transferred from a Shunde local hospital to the patients, and two of them finally died. Later, within an HZS-2 Hospital in Guangzhou on 18 January 2003. He 8-day stay in the HZS-3 Hospital, he infected 21 infected his brother (SQS, onset date, 18 January hospital staff members and one of them died. He was 2003) and several hospital staff in a Shunde hospital further transferred to Hospital GZS-8 on 8 February (maybe with other atypical pneumonia patients in that 2003. He probably infected three hospital staff hospital). He was confined to the ICU of the HZS-2 members there, while he had an intubation procedure Hospital until death on 31 January 2003 and probably for ventilatory support. Apparently, he did not cause infected one ICU staff member and a patient. further hospital infections in Hospital GZS-8. He The onset date for SQS was 18 January 2003. First, began to recover on 10 February 2003 and was released he was treated in a local hospital of Shunde and then from the hospital on 21 March 2003. transferred to the HZS-2 Hospital on 24 January, ZZF infected 23 relatives, visiting friends and where he came into contact with many hospital staff workers with close contacts. Two of them finally died. members as well as other patients in the ward specialized Tertiary and quaternary infections did occur in the for respiratory infectious diseases. He had relatively mild HZS-2 Hospital but not in the HZS-3 Hospital, which is symptoms. He recovered and was released from the clearly due to better isolation control. The infection in hospital on 16 February 2003. the Ward L2 of Hospital GZS-8 seems to have been

Phil. Trans. R. Soc. B (2007)

Zoonoses: 61-70% of all modern EIDs

Ebola Zoonosis Rabies Inﬂuenza SARS MERS

How do viruses jump? or What causes interspecies transmission S Coronaviruses M • +ssRNA viruses N • Ancestral CoV – Insecvorous Bats

• Many spillovers ‘jumps’ E Coronavirus Host Jumps

MERS-CoV NL63 OC43

BCoV

229E SARS-CoV

PEDV

Graham and Baric. Nature Reviews Microbiology. 11, 836–848 (2013) Coronavirus Host Jumps

MERS-CoV NL63 OC43

BCoV

229E SARS-CoV

PEDV

Graham and Baric. Nature Reviews Microbiology. 11, 836–848 (2013) Coronaviruses jump hosts by binding different cells Cell Cell Virus Virus Clues to the pandemic were in the anatomy

N S RNA

©[email protected] Coronavirus S S

M VERY large N ~200 kDa ~200 A long X 70 A wide

Very variable E Mutaons aﬀect receptor binding speciﬁcity ssue tropism host range triggering of conformaonal changes membrane fusion acvity pH of fusion, site of entry angenicity Clues to the pandemic were in the anatomy Non-silent nucleode changes in S genes of human and animal SARS-CoV isolates Animal Human

Y. Guan, et al., Science 302:276 (2003) Interspecies Transmission? Viruses adapt A quasispecies is a group of viruses related by a similar mutaon or mutaons, compeng Quasispecies within a highly mutagenic environment.

Figure 1. RNA viruses exist as a quasispecies. A virus replicating with a high mutation rate will generate a diverse mutant repertoire over the course of a few generations. In these trees, each branch indicates two variants linked by a point mutation and the concentric circles represent serial replication cycles.Moving to a new species = Selective Pressure The resulting distribution is often represented as a cloud centered on a master sequence. This two dimensional schematic is a vast oversimplification of the intraquasispecies connectivity. In the mathematical formulations of quasispecies theory, sequence space is multidimensional, with numerous branches between variants. doi:10.1371/journal.ppat.1001005.g001

Lauring and Andino. PLOS Pathogens 6(7):e1001005 (2010) in which mutation generates a swarm of candidate genomes that is The Problem of Fitness and Survival of the pruned by natural selection. According to population genetics, the Flattest frequency of a given variant in a population is closely approximated by its ability to survive and reproduce—its fitness. Mutation and selection are the most fundamental processes in In quasispecies formulations, where mutation rates are elevated, evolution. In Darwinian evolution, natural selection acts on frequency is also subject to the probability that the variant will be existing genetic variation to optimize fitness. Conceptually, fitness generated de novo by mutation of its neighbors in sequence space refers to how well a given organism ‘‘fits’’ into its environment, [12]. In RNA viruses, the contribution of mutation to genotype often reflected in how well it survives and reproduces [19]. In frequency is significant, and variants are ‘‘coupled’’ in sequence experimental settings, precise fitness measurements are para- space [18]. That is, a low fitness variant can be maintained at a mount, and virologists typically use replicative capacity as a higher than expected frequency because it is coupled to a well- surrogate for fitness [20]. While replication is useful as a first represented, higher fitness genotype in sequence space. The approximation, other factors such as immune escape, transmissi- phenomenon of mutational coupling is one of the defining bility, and cellular tropism are important components of fitness in characteristics of a quasispecies, as it places individual mutants the dynamic host environment [21]. Furthermore, because within a functional network of variants [2]. quasispecies theory adds the complexity of mutant networks, we Viral populations evolve within a fitness landscape where the must incorporate a population-based model into our fitness ‘‘ground level’’ is a representation of the range of genotypes in definition. sequence space. The ‘‘altitude’’ at any given location is the fitness Measuring the fitness of individual variants within a population associated with that particular genotype. The environment and its may misrepresent the fitness of a quasispecies. Early experiments with selective pressures determine the contours of the corresponding vesicular stomatitis virus (VSV) established that high fitness variants landscape, and adaptation to an environment involves a could be suppressed to low levels within a complex population [22]. mutational walk from one point in the fitness landscape to another Conversely, longitudinal studies of dengue virus isolates have (Figure 2A). In quasispecies theory, a network of mutationally identified defective clones that are stably maintained at a high coupled variants will span the corresponding peaks and valleys of frequency in the population [23]. Further consideration of the fitness the fitness landscape. A fast replicating population well suited to a landscape model may explain these paradoxical results (Figure 2B). given environment will inhabit a high and narrow peak in the Consider two populations, generated from either a fast replicator or a fitness landscape, while a less fit but more genetically diverse slower one. At low mutation rates, the fast replicator will triumph population will occupy a lower, broader one. because its progeny are genetically identical and generated more A viral quasispecies, then, is a cloud of diverse variants that are quickly. In the fitness landscape, this population occupies a tall, genetically linked through mutation, interact cooperatively on a narrow peak, where there is little genotypic diversity and maximal functional level, and collectively contribute to the characteristics of fitness. In RNA viruses, elevated mutation rates mean that a fast the population. The unit of selection is the population as a whole, replicator will give rise to genetically diverse progeny, many of which and the nature of the functional interactions among genetically are significantly less fit than the parent. Quasispecies theory predicts distinct variants is therefore of critical importance to pathogenesis that slower replicators will be favored if they give rise to progeny that in infected hosts. These effects and their biomedical implications are on average more fit; these populations occupy short, flat regions of are described below. the fitness landscape [18]. This effect, termed survival of the flattest,

PLoS Pathogens | www.plospathogens.org 2 July 2010 | Volume 6 | Issue 7 | e1001005 Small mutations over time can cause big changes!

SARS SARS unable adapted to to infect humans humans Virus Chatter

HUMAN VIRUSES Human Exclusive HAVE ANIMAL ORIGINS Human Adapted

(Wolfe, Panosian and Diamond, 2007, Nature) Transmissible

Weakly Human VIRAL Adapted CHATTER Not Human Adapted

Rabies Yellow Fever SARS/Ebola Influenza HIV-1

Downloaded from rstb.royalsocietypublishing.org on December 12, 2011

1066 G.-p. Zhao SARS molecular epidemiology

early phase middle phase late phase

beginning of beginning of Hospital ZS-2 hotel M outbreak outbreak

Phil. Trans. R. Soc. B (2007) Interspecies movement!

Canine coronavirus

Feline parvovirus NATURE | Vol 451 | 21 February 2008 LETTERS NATURE | Vol 451 | 21 February 2008 LETTERS

reveals a significant rise in the number of EID events they have caused pathogen distribution19, and (2) that the importance of these drivers reveals a significant rise in the number of EID events they have caused pathogen distribution19, and (2) that the importance of these drivers over time, controlling for reporting effort (GLMP,JID F 5 49.8, depends on the category of EID event. In particular, we hypothesize over time, controlling for reporting effort (GLMP,JID F 5 49.8, depends on the category ofP , EID0.001, event. d.f. In5 particular,57). This rise we hypothesize corresponds to climate anomalies that EID events caused by zoonotic pathogens from wildlife are sig- P , 0.001, d.f. 5 57). This rise corresponds to climate anomalies that EID events caused byoccurring zoonotic during pathogens the 1990s from16 wildlife, adding are support sig- to hypotheses that cli- nificantly correlated with wildlife biodiversity, and those caused by occurring during the 1990s16, adding support to hypotheses that cli- nificantly correlated withmate wildlife change biodiversity, may drive and the those emergence caused of by diseases that have vectors drug-resistant pathogens are more correlated with socio-economic mate change may drive the emergence of diseases that have vectors drug-resistant pathogenssensitive are more to correlated changes in with environmental socio-economic conditions such as rainfall, conditions than those caused by zoonotic pathogens. sensitive to changes in environmental conditions such as rainfall, conditions than those causedtemperature by zoonotic and severe pathogens. weather events17. However, this controversial We tested these hypotheses by examining the relationship between temperature and severe weather events17. However, this controversial We tested these hypothesesissue by requires examining further the analyses relationship to test causalbetween relationships between EID the spatial pattern of the different categories of EID events (zoonotic issue requires further analyses to test causal relationships between EID the spatial pattern of the differentevents and categories climate change of EID18. events We also (zoonotic report that EID events caused by pathogens originating in wildlife and non-wildlife, drug-resistant events and climate change18. We also report that EID events caused by pathogens originating indrug-resistant wildlife and microbes non-wildlife, (which drug-resistant represent 20.9% of the EID events and vector-borne pathogens, Supplementary Fig. 2), and socio- drug-resistant microbes (which represent 20.9% of the EID events and vector-borne pathogens,in our Supplementary database) have significantly Fig. 2), and increased socio- with time, controlling economic variables (human population density and human popu- in our database) have significantly increased with time, controlling economic variables (humanfor reporting population effort density (GLM andP,JID humanF 5 5.19, popu-P , 0.05, d.f. 5 57). This is lation growth), environmental variables (latitude, rainfall) and an for reporting effort (GLMP,JID F 5 5.19, P , 0.05, d.f. 5 57). This is lation growth), environmentalprobably variables related (latitude,to a corresponding rainfall) and rise in an antimicrobial drug use, ecological variable (wildlife host species richness) (see Methods). 2,7,12 probably related to a corresponding rise in antimicrobial drug use, ecological variable (wildlifeparticularly host species in high-latitude richness) developed (see Methods). countries . We found that human population density was a common significant particularly in high-latitude developed countries2,7,12. We found that human populationA recent density analysis was showed a common a latitudinal significant spatial gradient in human independent predictor of EID events in all categories, controlling 19 A recent analysis showed a latitudinal spatial gradient in human independent predictor ofpathogen EID events species in richnessall categories, increasing controlling towards the Equator , in com- for spatial reporting bias by country (see Methods, Table 1 and 19 mon with the distributional pattern of species richness found in Supplementary Table 3). This supports previous hypotheses that pathogen species richness increasing towards the Equator , in com- for spatial reporting bias by country (see Methods, Table20 1 and mon with the distributional pattern of species richness found in Supplementary Table 3).many This other supports taxonomic previous groups hypotheses. Environmental that parameters that disease emergence is largely a product of anthropogenic and demo- many other taxonomic groups20. Environmental parameters that disease emergence is largelypromote a product pathogen of anthropogenic transmission and at demo-lower latitudes (for example, graphic changes, and is a hidden ‘cost’ of human economic develop- higher temperatures and precipitation) are hypothesized to drive this ment2,4,7,9,13. Wildlife host species richness is a significant predictor promote pathogen transmission at lower latitudes (for example, graphic changes, and is a hidden ‘cost’ of human economic develop- pattern19. Our analyses suggest that there is no such pattern in EID for the emergence of zoonotic EIDs with a wildlife origin, with no role higher temperatures and precipitation) are hypothesized to drive this ment2,4,7,9,13. Wildlife host species richness is a significant predictor 19 events, which are concentrated in higher latitudes (Supplementary for human population growth, latitude or rainfall (Table 1). The pattern . Our analyses suggest that there is no such pattern in EID for the emergence of zoonoticFig. 1).EIDs The with highest a wildlife concentration origin, with of no EID role events per million square emergence of zoonotic EIDs from non-wildlife hosts is predicted events, which are concentrated in higher latitudes (Supplementary for human population growth,kilometres latitude of land or was rainfall found (Table between 1). 30 The and 60 degrees north and by human population density, human population growth, and lati- Fig. 1). The highest concentration of EID events per million square emergence of zoonotic EIDsbetween from 30 non-wildlife and 40 degrees hosts south, is predicted with the main hotspots in the tude, and not by wildlife host species richness. EID events caused by kilometres of land was found between 30 and 60 degrees north and by human population density,northeastern human United population States, growth, western and Europe, lati- Japan and southeastern drug-resistant microbes are affected by human population density between 30 and 40 degrees south, with the main hotspots in the tude, and not by wildlife hostAustralia species (Fig. richness. 2). We EID hypothesize events caused that (1) by socioeconomic drivers and growth, latitude and rainfall. The pattern of EID events caused by northeastern United States, western Europe, Japan and southeastern drug-resistant microbes are(such affected as human by human population population density, antibiotic density drug use and agricul- vector-borne diseases was not correlated with any of the environ- Australia (Fig. 2). We hypothesize that (1) socioeconomic drivers and growth, latitude and rainfall.tural practices) The pattern are major of EID determinants events caused of by the spatial distribution of mental or ecological variables we examined, although we note that (such as human population density, antibiotic drug use and agricul- vector-borne diseases wasEID not events, correlated in addition with to any the of ecological the environ- or environmental conditions the climate variable used in this analysis (rainfall) does not represent tural practices) are major determinants of the spatial distribution of mental or ecological variablesthat may we examined, affect overall although (emerging we note and that non-emerging) human climate change phenomena. EID events, in addition to the ecological or environmental conditions the climate variable used in this analysis (rainfall) does not represent The incidence of EID events has increased since 1940, reaching a that may affect overall (emerging andmaximum in the 1980s (Fig. 1). We tested whether the increase non-emerging) human climate change phenomena. Figure 1 | Number of EID events per decade. EID a b through me was largely aributable to increasing infecous disease 100 100 events (defined as the temporal origin of an EID, reporng effort (that is, through more efficient diagnosc methods Figure 1 | NumberHelminths of EID events per decade. EID Zoonotic: unspecified represented by the original case or cluster of cases a and more thorough surveillance2,3,13) by calculang the annual b num- 100 100 events (definedFungi as the temporal origin of an EID, Zoonotic: non-wildlife that represents a disease emerging in the human ber of arcles published in the Journal of Infecous Diseases (JID) 80 Protozoa 80 Zoonotic: wildlife since 1945 (see Methods). Controlling for reporng effort, the num- represented byViruses the originalor prions case or cluster of cases Non-zoonotic population—see Methods) are plotted with Helminths Zoonotic: unspecified Bacteria or rickettsiae respect to a, pathogen type, b, transmission type, Fungi ber of EID events sll shows a highly significant relaonship with me Zoonotic: non-wildlife that represents a disease emerging in the human c d 80 Protozoa (generalized linear model with Poisson errors, offset by log(JID arcles) 80 Zoonotic: wildlife population—see Methods) are plotted with , drug resistance and , transmission mode (see Viruses or prions (GLM ), F 5 96.4, P , 0.001, Non-zoonoticd.f. 5 57). This provides the first Bacteria or rickettsiae 60 60 keys for details). analycal support for previous suggesons that the threat of EIDs to P,JID respect to a, pathogen type, b, transmission type, global health is increasing1,2,14. To further invesgate the peak in EID c, drug resistance and d, transmission mode (see 60 events in the 1980s, we examined the most frequently cited driver of 60 keys for details). EID emergence during this period (see Supplementary Table 1). 40 40 Increased suscepbility to infecon caused the highest pro- poron of events during 1980–90 (25.5%), and we therefore suggest that the Number of EID events 40 spike in EID events in the 1980s is due largely to the 40 emergence of new diseases associated with the HIV/AIDS pandemic2,13. 20 20 Number of EID events

20 20 0 0 1940 1950 1960 19701980 1990 20001940 1950 1960 1970 1980 1990 2000

100 c 100 d 0 0 1940 1950 1960 19701980 1990 20001940 1950 1960 1970 1980 1990 2000 Drug-resistant Vector-borne Non drug-resistant Non vector-borne 100 c 100 d 80 80

Drug-resistant Vector-borne Non drug-resistant Non vector-borne 80 80 60 60

60 60 40 40 Number of EID events

40 40 20 20 Number of EID events

20 20 0 0 19401950 19601970198019902000 1940 1950 1960 1970 1980 1990 2000 Decade Decade

• Why are we seeing more zoonoses now than ever before?

•LETTERS Are there hot spots for zoonoc events? NATURE | Vol 451 | 21 February 2008

Figure 2 | Global richness map of No. of EID events 1 2–3 4–5 6–7 8–11 the geographic origins of EID events from 1940 to 2004. The map is derived for EID events caused by all pathogen types. Circles represent one degree grid cells, and the area of the circle is proportional to the number of events in the cell.

Our study examines the role of only a few drivers to understand Australia and some parts of Asia, than in developing regions. This disease emergence, whereas many other factors (for example, land contrasts with our risk maps (Fig. 3), which suggest that predicted use change, agriculture) have been causally linked to EIDs6,21. emerging disease hotspots due to zoonotic pathogens from wildlife However, until more rigorous global data sets of these drivers become and vector-borne pathogens are more concentrated in lower-latitude available, data on human population density and growth act as developing countries. We conclude that the global effort for EID reasonable proxies for such anthropogenic changes. Other likely surveillance and investigation is poorly allocated, with the majority future improvements to the model would include a more accurate of our scientific resources focused on places from where the next accounting for temporal and spatial ascertainment biases—for important emerging pathogen is least likely to originate. We advocate example, the development of global spatial data sets of the amount re-allocation of resources for ‘smart surveillance’ of emerging disease of funding per capita for infectious disease surveillance. hotspots in lower latitudes, such as tropical Africa, Latin America and Our analyses provide a basis for developing a predictive model for Asia, including targeted surveillance of at-risk people to identify early the regions where new EIDs are most likely to originate (emerging case clusters of potentially new EIDs before their large-scale emer- disease ‘hotspots’). A visualization of the regression results from gence. Zoonoses from wildlife represent the most significant, growing Table 1 for EID events from each category (Fig. 3) identifies these threat to global health of all EIDs (see our data in Fig. 1, and recent regions globally. This approach may be valuable for deciding where reviews1,2,5,8,9,13,14). Our findings highlight the critical need for health to allocate global resources to pre-empt, or combat, the first stages of monitoring4,14,23 and identification of new, potentially zoonotic disease emergence10,14,18,22. Our analysis shows that there is a high pathogens in wildlife populations, as a forecast measure for EIDs. spatial reporting bias for EID events (see Methods, Supplementary Finally, our analysis suggests that efforts to conserve areas rich in Fig. 3), reflecting greater surveillance and infectious disease research wildlife diversity by reducing anthropogenic activity may have added effort in richer, developed countries of Europe, North America, value in reducing the likelihood of future zoonotic disease emergence.

Table 1 | Socio-economic, environmental and ecological correlates of EID events Pathogen type Zoonotic: wildlife Zoonotic: non-wildlife Number of EID event grid cells 147–156 49–53 bBbB log(JID articles) 0.34-0.37*** 1.41–1.45 0.40–0.49*** 1.49–1.63 log[human pop. density (persons per km2)] 0.56–0.64*** 1.75–1.90 0.88–1.06*** 2.41–2.89 Human pop. growth (change in persons per km2,1990–2000){ 0.09–0.45 1.09–1.56 0.86–1.31** 2.37–3.71 Latitude (decimal degrees) 0.002–0.017 1.00–1.02 0.024–0.040* 1.02–1.04 Rainfall (mm) (0.14–0.06)x1023 1.00–1.00 (0.32–0.57)x1023# 1.00–1.00 Wildlife host richness 0.008–0.013** 1.01–1.01 20.015 to 20.003 0.99–1.00 Constant 29.81 to 28.78*** 213.84 to 211.73***

Pathogen type Drug-resistant Vector-borne Number of EID event grid cells 59–64 81–88 bBbB log(JID articles) 0.46–0.53*** 1.62–1.71 0.17–0.21*** 1.18–1.23 log[human pop. density (persons per km2)] 1.03–1.27*** 2.87–3.92 0.41–0.49*** 1.51–1.63 Human pop. growth (change in persons per km2, 1990–2000){ 1.21–1.70*** 2.73–5.06 20.08 to 0.31 0.93–1.37 Latitude (decimal degrees) 0.047–0.072** 1.04–1.07 20.015 to 0.002 0.98–1.00 Rainfall (mm) (0.35–0.61)x1023* 1.00–1.00 (0.10–0.28)x1023 1.00–1.00 Wildlife host richness (20.01 to 0.16)x1022 1.00–1.02 (0.28–0.74)x1022 1.00–1.01 Constant 217.45 to 214.41*** 28.21 to 27.53*** Columns represent multivariable logistic regressions for EID events split according to the type of pathogen responsible. Numbers represent the rangeofvaluesobtainedfrom10randomdrawsofthe possible grid squares, where b represents the regression coefficients and B represents the odds ratio for the independent variables in the model. Higher odds ratios indicate that variable value increases have a higher likelihood of being associated with an EID event; probability value equals the median probability from 10 random draws of the possible grid squares where ***P , 0.001, **P , 0.01, *P , 0.05, #P , 0.1. Results from each random draw are shown in Supplementary Table 3. { See Methods for details. 992 © 2008 Nature Publishing Group NATURE | Vol 451 | 21 February 2008 LETTERS NATURE | Vol 451 | 21 FebruaryWhere is the Risk? 2008 LETTERS a b Figure 3 | Global distribution of relative risk of an EID event. Maps Figure 3 | Global distribution of a b are derived for EID events caused by a, zoonotic pathogens from wildlife, relative risk of an EID event. Maps b, zoonotic pathogens from non- wildlife, c, drug-resistant pathogens are derived for EID events caused by and d, vector-borne pathogens. The relative risk is calculated from a, zoonotic pathogens from wildlife, regression coefficients and variable values in Table 1 (omitting the b, zoonotic pathogens from non- c d variable measuring reporting effort), categorized by standard wildlife, c, drug-resistant pathogens deviations from the mean and mapped on a linear scale from green and d, vector-borne pathogens. The (lower values) to red (higher values). relative risk is calculated from regression coefficients and variable values in Table 1 (omitting the c d variable measuring reporting METHODS SUMMARY 10. Burke, D. S. in Pathology of Emerging Infections (eds Nelson, A. M. & Horsburgh, C. effort), categorized by standard R.) 1–12 (American Society for Microbiology, Washington DC, 1998). Biological, temporal and spatial data on human EID ‘events’ were collected from 11. Cleaveland, S., Laurenson, M. K. & Taylor, L. H. Diseases of humans and their deviations from the mean and the literature from 1940 (yellow fever virus, Nuba Mountains, Sudan) until 2004 domestic mammals: Pathogen characteristics, host range and the risk of (poliovirus type 2 in Uttar Pradesh, India) (n 5 335, see Supplementary Data for emergence. Phil. Trans. R. Soc. Lond. B 356, 991–999 (2001). mapped on a linear scale from green data and sources). Global allocation of scientific resources for disease surveil- 12. Cohen, M. L. Changing patterns of infectious disease. Nature 406, 762–767 lance has been focused on rich, developed countries (Supplementary Fig. 3). It is (2000). (lower values) to red (higher thus likely that EID discovery is biased both temporally (by increasing research 13. Lederberg, J., Shope, R. E. & Oakes, S. C. J. Emerging Infections: Microbial Threats to effort into human pathogens over the period of the database) and spatially (by Health in the United States (Institute of Medicine, National Academies Press, values). the uneven levels of surveillance across countries). We account for these biases by Washington DC, 1992). quantifying reporting effort in JID and including it in our temporal and spatial 14. King, D. A., Peckham, C., Waage, J. K., Brownlie, J. & Woolhouse, M. E. J. Infectious analyses. JID is the premier international journal (highest ISI impact factor 2006: diseases: Preparing for the future. Science 313, 1392–1393 (2006). http://portal.isiknowledge.com/) of human infectious disease research that pub- 15. Morse, S. S. Factors in the emergence of infectious diseases. Emerging Infect. Dis. 1, 7–15 (1995). lishes papers on both emerging and non-emerging infectious diseases without a 16. Houghton, J. T., et al. (eds) Climate Change 2001: The Scientific Basis (Cambridge specific geographical bias. To investigate the drivers of the spatial pattern of EID Univ. Press, Cambridge, UK, 2001). events, we compared the location of EID events to five socio-economic, envir- 17. Patz, J. A., Campbell-Lendrum, D., Holloway, T. & Foley, J. A. Impact of regional onmental and ecological variables matched onto a terrestrial one degree grid of climate change on human health. Nature 438, 310–317 (2005). the globe. We carried out the spatial analyses using a multivariable logistic 18. Rogers, D. J. & Randolph, S. E. Studying the global distribution of infectious regression to control for co-variability between drivers, with the presence/ diseases using GIS and RS. Nature Rev. Microbiol. 1, 231–237 (2003). absence of EID events as the dependent variable and all drivers plus our measure 19. Guernier, V., Hochberg, M. E. & Guegan, J. F. O. Ecology drives the worldwide of spatial reporting bias by country as independent variables (n 5 18,307 ter- distribution of human diseases. PLoS Biol. 2, 740–746 (2004). restrial grid cells). Analyses were conducted on subsets of the EID events—those 20. Grenyer, R. et al. Global distribution and conservation of rare and threatened10. Burke, D. S. in Pathology of Emerging Infections (eds Nelson, A. M. & Horsburgh, C. causedMETHODS by zoonotic pathogens SUMMARY (defined in our analyses as pathogens that origi- vertebrates. Nature 444, 93–96 (2006). nated in non-human animals) originating in wildlife and non-wildlife species, 21. Wolfe, N. D., Dunavan, C. P. & Diamond, J. Origins of major human infectious R.) 1–12 (American Society for Microbiology, Washington DC, 1998). andBiological, those caused by drug-resistant temporal and vector-borne and spatial pathogens. data on humandiseases. EIDNature ‘events’447, 279–283 were (2007). collected from 22. Ferguson, N. M. et al. Strategies for containing an emerging influenza pandemic11. in Cleaveland, S., Laurenson, M. K. & Taylor, L. H. Diseases of humans and their Fullthe Methods literatureand any associated from references 1940 are available (yellow in the feveronline version virus, of NubaSoutheast Mountains, Asia. Nature 437, 209 Sudan)–214 (2005). until 2004 the paper at www.nature.com/nature. 23. Kuiken, T. et al. Public health — pathogen surveillance in animals. Science 309, domestic mammals: Pathogen characteristics, host range and the risk of (poliovirus type 2 in Uttar Pradesh, India) (n 51680335,–1681 (2005). see Supplementary Data for Received 2 August; accepted 11 December 2007. emergence. Phil. Trans. R. Soc. Lond. B 356, 991–999 (2001). Supplementary Information is linked to the online version of the paper at 1.data Morens, andD. M., Folkers, sources). G. K. & Fauci, Global A. S. The challenge allocation of emerging and of re- scientificwww.nature.com/nature. resources for disease surveil- 12. Cohen, M. L. Changing patterns of infectious disease. Nature 406, 762–767 emerging infectious diseases. Nature 430, 242–249 (2004). Acknowledgements We thank the following for discussion, assistance and 2.lance Smolinski, has M. S., Hamburg, been M. focused A. & Lederberg, on J. Microbial rich, Threats developed to Health: countries (Supplementary Fig. 3). It is (2000). comments: K. A. Alexander, T. Blackburn, S. Cleaveland, I. R. Cooke, Emergence, Detection, and Response (National Academies Press, Washington DC, A. A. Cunningham, J. Davies, A. P. Dobson, P. J. Hudson, A. M. Kilpatrick, thus2003). likely that EID discovery is biased both temporally (by increasing research 13. Lederberg, J., Shope, R. E. & Oakes, S. C. J. Emerging Infections: Microbial Threats to J. R. C. Pulliam, J. M. Rowcliffe, W. Sechrest, L. Seirup and M. E. J. Woolhouse, and in 3. Binder, S., Levitt, A. M., Sacks, J. J. & Hughes, J. M. Emerging infectious diseases: particular V. Mara and N. J. B. Isaac for analytical support. This project was effortPublic health into issues for human the 21st century. pathogensScience 284, 1311– over1313 (1999). the period of the database) and spatially (by Health in the United States (Institute of Medicine, National Academies Press, supported by NSF (Human and Social Dynamics; Ecology), NIH/NSF (Ecology of 4. Daszak, P., Cunningham, A. A. & Hyatt, A. D.Emerging infectious diseases of wildlife Infectious Diseases), NIH (John E. Fogarty International Center), Eppley the— threats uneven to biodiversity levels and human of health. surveillanceScience 287, 443–449 across (2000). countries). We account for these biases by Washington DC, 1992). Foundation, The New York Community Trust, V. Kann Rasmussen Foundation and 5. Taylor, L. H., Latham, S. M. & Woolhouse, M. E. J. Risk factors for human disease a Columbia University Earth Institute fellowship (K.E.J.). quantifyingemergence. Phil. Trans. reporting R. Soc. Lond. B 356, 983 effort–989 (2001). in JID and including it in our temporal and spatial 14. King, D. A., Peckham, C., Waage, J. K., Brownlie, J. & Woolhouse, M. E. J. Infectious 6. Patz, J. A. et al. Unhealthy landscapes: Policy recommendations on land use Author Contributions P.D. conceived and directed the study and co-wrote the diseases: Preparing for the future. Science 313, 1392–1393 (2006). analyses.change and infectious JID disease is the emergence. premierEnviron. Health international Perspect. 112, journalpaper with (highest K.E.J.; K.E.J. coordinated ISI impact and conducted factor the analyses 2006: with M.A.L., A.S., 1092–1098 (2004). N.G.P. and D.B.; N.G.P. compiled the EID event database; and J.L.G provided the15. Morse, S. S. Factors in the emergence of infectious diseases. Emerging Infect. Dis. 1, 7.http://portal.isiknowledge.com/) Weiss, R. A. & McMichael, A. J. Social and environmental risk factors of in human the mammal infectious distribution disease data. All authors research were involved that in the design pub- of the study, the emergence of infectious diseases. Nature Med. 10, S70–S76 (2004). interpretation of the results and commented on the manuscript. 7–15 (1995). 8.lishes Woolhouse, papers M. E. J. & Gowtage-Sequeria, on both emerging S. Host range and emerging and non-emerging and infectious diseases without a reemerging pathogens. Emerging Infect. Dis. 11, 1842–1847 (2005). Author Information Reprints and permissions information is available at 16. Houghton, J. T., et al. (eds) Climate Change 2001: The Scientific Basis (Cambridge 9.specific Morse, S. S. in Emerging geographical Viruses (ed. Morse, bias. S. S.) 10– To28 (Oxford investigate Univ. Press, New thewww.nature.com/reprints. drivers of the Correspondence spatial pattern and requests of for materials EID should be York, 1993). addressed to P.D. ([email protected]). Univ. Press, Cambridge, UK, 2001). events, we compared the location of EID events to five socio-economic, envir- 17. Patz, J. A., Campbell-Lendrum, D., Holloway, T. & Foley, J. A. Impact of regional onmental and ecological variables matched onto a terrestrial one degree grid of climate change on human health. Nature 438, 310–317 (2005). the globe. We carried out the spatial analyses using a multivariable logistic 18. Rogers, D. J. & Randolph, S. E. Studying the global distribution of infectious regression to control for co-variability between drivers, with the presence/ 993 diseases using GIS and RS. Nature Rev. Microbiol. 1, 231–237 (2003). © 2008 Nature Publishing Group absence of EID events as the dependent variable and all drivers plus our measure 19. Guernier, V., Hochberg, M. E. & Guegan, J. F. O. Ecology drives the worldwide of spatial reporting bias by country as independent variables (n 5 18,307 ter- distribution of human diseases. PLoS Biol. 2, 740–746 (2004). restrial grid cells). Analyses were conducted on subsets of the EID events—those 20. Grenyer, R. et al. Global distribution and conservation of rare and threatened caused by zoonotic pathogens (defined in our analyses as pathogens that origi- vertebrates. Nature 444, 93–96 (2006). nated in non-human animals) originating in wildlife and non-wildlife species, 21. Wolfe, N. D., Dunavan, C. P. & Diamond, J. Origins of major human infectious and those caused by drug-resistant and vector-borne pathogens. diseases. Nature 447, 279–283 (2007). 22. Ferguson, N. M. et al. Strategies for containing an emerging influenza pandemic in Full Methods and any associated references are available in the online version of Southeast Asia. Nature 437, 209–214 (2005). the paper at www.nature.com/nature. 23. Kuiken, T. et al. Public health — pathogen surveillance in animals. Science 309, 1680–1681 (2005). Received 2 August; accepted 11 December 2007. Supplementary Information is linked to the online version of the paper at 1. Morens, D. M., Folkers, G. K. & Fauci, A. S. The challenge of emerging and re- www.nature.com/nature. emerging infectious diseases. Nature 430, 242–249 (2004). Acknowledgements We thank the following for discussion, assistance and 2. Smolinski, M. S., Hamburg, M. A. & Lederberg, J. Microbial Threats to Health: comments: K. A. Alexander, T. Blackburn, S. Cleaveland, I. R. Cooke, Emergence, Detection, and Response (National Academies Press, Washington DC, A. A. Cunningham, J. Davies, A. P. Dobson, P. J. Hudson, A. M. Kilpatrick, 2003). J. R. C. Pulliam, J. M. Rowcliffe, W. Sechrest, L. Seirup and M. E. J. Woolhouse, and in 3. Binder, S., Levitt, A. M., Sacks, J. J. & Hughes, J. M. Emerging infectious diseases: particular V. Mara and N. J. B. Isaac for analytical support. This project was Public health issues for the 21st century. Science 284, 1311–1313 (1999). supported by NSF (Human and Social Dynamics; Ecology), NIH/NSF (Ecology of 4. Daszak, P., Cunningham, A. A. & Hyatt, A. D.Emerging infectious diseases of wildlife Infectious Diseases), NIH (John E. Fogarty International Center), Eppley — threats to biodiversity and human health. Science 287, 443–449 (2000). Foundation, The New York Community Trust, V. Kann Rasmussen Foundation and 5. Taylor, L. H., Latham, S. M. & Woolhouse, M. E. J. Risk factors for human disease a Columbia University Earth Institute fellowship (K.E.J.). emergence. Phil. Trans. R. Soc. Lond. B 356, 983–989 (2001). 6. Patz, J. A. et al. Unhealthy landscapes: Policy recommendations on land use Author Contributions P.D. conceived and directed the study and co-wrote the change and infectious disease emergence. Environ. Health Perspect. 112, paper with K.E.J.; K.E.J. coordinated and conducted the analyses with M.A.L., A.S., 1092–1098 (2004). N.G.P. and D.B.; N.G.P. compiled the EID event database; and J.L.G provided the 7. Weiss, R. A. & McMichael, A. J. Social and environmental risk factors in the mammal distribution data. All authors were involved in the design of the study, the emergence of infectious diseases. Nature Med. 10, S70–S76 (2004). interpretation of the results and commented on the manuscript. 8. Woolhouse, M. E. J. & Gowtage-Sequeria, S. Host range and emerging and reemerging pathogens. Emerging Infect. Dis. 11, 1842–1847 (2005). Author Information Reprints and permissions information is available at 9. Morse, S. S. in Emerging Viruses (ed. Morse, S. S.) 10–28 (Oxford Univ. Press, New www.nature.com/reprints. Correspondence and requests for materials should be York, 1993). addressed to P.D. ([email protected]).

“EID events are dominated by zoonoses (60.3% of EIDs): the majority of these (71.8%) originate in wildlife (for example, severe acute respiratory virus, Ebola virus), and are increasing signiﬁcantly over me.”

“EID origins are signiﬁcantly correlated with socio-economic, environmental and ecological factors…”

Substanal number of zoonoses come from Bats. Why Bats? Myotis lucifugus • Flying mammal • Ubiquitous • Long-lived and High survivability • 20% of all known mammal species • Close associaon with humans • Highly acve immune system

Excellent Reservoir Host

hp://www.hww.ca/en/species/mammals/bats.html Coronavirus Host Jumps

MERS-CoV NL63 OC43

BCoV

229E SARS-CoV

PEDV

Graham and Baric. Nature Reviews Microbiology. 11, 836–848 (2013) Virus Jumping in Alaska? 27.7 News Feat Bird Flu MH 25/7/06 10:11 AM Page 349

NATURE|Vol 442|27 July 2006 Adaptations happen where populations mix NEWS FEATURE A ROUTE FOR FLU? As the place where two major bird flyways overlap, Alaska is under watch as a possible entry point for H5N1 into the United States. East Atlantic East Africa/ West Asia Mississippi/ Americas

carrying H5N1, or whether Central Asia the virus might leap from Atlantic/ such a carrier into people or Americas into the $29-billion poultry industry of the United States. Pacific This year, the US depart- ments of agriculture and the Ocean interior will lead a $29 mil- East Asia/ Pacific/ lion effort to test wild birds for Black Sea/ Mediterranean Australia Americas H5N1 and other avian flu viruses in Alaska and other parts of the United States. “We’re at the nexus of bird migration from the Canadian Arc- FAO tic to Russia and southeast Asia,” says Robert Leedy, chief of the US Fish and Wildlife Ser- vice’s office of migratory bird management in Weybridge revealed that H5N1 viruses taken And that’s why James Anchorage, Alaska. “If H5N1 is going to be from dead wild birds in Europe are very similar Sedinger and his team of young biologists, transferred in wild birds, the most likely avenue to H5N1 viruses found in Mongolia, Siberia including Comstock, are spending their sum- is Alaska.” and Qinghai Lake. Scientists have also reported mer swabbing birds’ rears on the Yukon Delta. that healthy birds in China were carrying the Sedinger, a wildlife biologist at the Univer- Crossing continents H5N1 strain just before their autumn migration sity of Nevada at Reno, has been studying But some scientists have reservations about last year3. That suggests the birds could have migratory birds in Alaska since 1977. His field the testing programme. Many flu experts think caused the outbreak of the virus in Europe last camp sits at the edge of the tidal Tutakoke poultry smuggling or imports, rather than autumn, by carrying it to the continent from River, near where it runs into the Bering Sea. migrating birds, are far more likely to bring in east Asia. The camp rattles with the noise of birds at all the virus. And others point out that it’s still not Other studies have implied that wild birds hours in summer, when the sun sets for three clear how or whether wild birds contribute to shuttled the virus between Europe and Africa, hours every night, and if you don’t watch your H5N1 outbreaks in domestic poultry. Given where H5N1 first showed up in February. A step you’re likely to stumble into a mother bird these uncertainties, some question the deci- team of researchers recently suggested that sitting on a nest tucked into the grass. sion to spend millions of dollars hunting for This is breeding central, not just for migrat- flu in Alaska, when the H5N1 virus is already “It’s much more important to ing eiders, but also for black brants — chunky racing across the rest of the globe. “More infor- sea geese that fly from Alaska to points south mation is always better, so you can’t complain go where the disease is in the for the winter. Over decades of work, Sedinger about that,” says William Karesh, director of developing countries, to see has snapped identifying bands on thousands the Wildlife Conservation Society’s Field Vet- how the thing is spreading.” of brants at Tutakoke. Some of the birds have erinary Program based in New York City. “But been found to spend the winter in China, it’s much more important to go where the dis- — William Karesh Japan and Korea. The US government’s plan to ease is in the developing countries, to see how head off bird flu focuses on about 29 such this thing is spreading.” migrating birds may have transmitted H5N1 to species that spend time in Alaska and travel to Until last year, no one thought that migra- Nigeria, the first African country to report the Asia. About 15,000 birds will be tested in the tory birds played any serious role in the spread virus4. The scientists sequenced genes from bird state. Most will be trapped live by biologists. of H5N1. But in July 2005, a team of virologists flu viruses found in chickens on poultry farms. But the Department of Interior will also test reported that some 6,000 migratory birds had They discovered that many of the viruses, birds shot by sport and subsistence hunters died of an H5N1 outbreak at the Qinghai Lake which seemed to cluster into three genetic and — if and when they happen — dead ones nature reserve in China1. Many of the dead groups, were similar to those found on other found among mass bird die-offs. birds were bar-headed geese, which fly from continents, including one strain that has been China to India and Myanmar every year. Since found only in wild birds in Europe. What’s Out for the count that report, the H5N1 strain has been found in more, the virus outbreaks in poultry were found So far, 3,772 samples from Alaska have been dead migratory birds in Asia, Russia, Europe, along major bird migration corridors. tested, and none has turned up positive. But Africa and the Middle East. Four people even But none of these studies can conclusively that’s not surprising. Most of the samples taken died from bird flu after collecting feathers show that migratory birds transmit the virus. from live wild birds around the world are clear from infected wild swans in Azerbaijan2. So In all cases, the wild birds themselves could of H5N1, says Ward Hagemeijer of Wetlands what role do migratory birds play in spreading have caught H5N1 from poultry or from International, a group that coordinates volun- H5N1around the world? some ‘bridge’ group, such as crows, jays or teer surveys of migratory birds. Last winter, Genetic studies may help to answer this grackles. Officials agree that answering ques- Wetlands International tested almost 6,000 question. This May, Ian Brown of the United tions about the role of wild birds will require a wild birds for H5N1 along migration paths Kingdom’s Veterinary Laboratories Agency in lot more field work in live, migrating birds. in Africa, Europe and Asia. And scientists 349 © 2006 Nature Publishing Group

Alaskan Bats – Myotis lucifugus

Photo: Gabor Csorba, HNHM What does this mean in AK?

• Viral distribuon in host species populaons. • Insecvorous Bat populaons Samples from all across Alaska are being analyzed Thanks: Quesons Undergraduate Ongoing Collaboraons: Students: • Douglas Causey • Maegan Lange • Eric Bortz • Melanie Wright • US Forest Service • Nathaneal Ray Chugach ▫ Jessica Iles • Alaska Department of Oﬃce of Undergraduate Fish and Game Research and ▫ Marian Snively Scholarship ▫ Dave Tessler