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Global trends in emerging infectious diseases

Global trends in emerging infectious diseases Vol 451 | 21 February 2008 |doi:10.1038/nature06536 LETTERS 1 2 3 3 3 4 Kate E. Jones , Nikkita G. Patel , Marc A. Levy , Adam Storeygard {, Deborah Balk {, John L. Gittleman & Peter Daszak Emerging infectious diseases (EIDs) are a significant burden on prion pathogens constitute only 25.4% of EID events, in contrast to 1–3 global economies and public health . Their emergence is thought previous analyses which suggest that 37–44% of emerging pathogens 5,8,11 to be driven largely by socio-economic, environmental and eco- are viruses or prions and 10–30% bacteria or rickettsia . This 1–9 logical factors , but no comparative study has explicitly analysed follows our classification of each individual drug-resistant microbial these linkages to understand global temporal and spatial patterns strain as a separate pathogen in our database, and reflects more of EIDs. Here we analyse a database of 335 EID ‘events’ (origins of accurately the true significance of antimicrobial drug resistance for EIDs) between 1940 and 2004, and demonstrate non-random glo- global health, in which different pathogen strains can cause separate significant outbreaks . In broad concurrence with previous studies bal patterns. EID events have risen significantly over time after 5,8,11 controlling for reporting bias, with their peak incidence (in the on the characteristics of emerging human pathogens , we find the 1980s) concomitant with the HIV pandemic. EID events are domi- percentages of EID events caused by other pathogen types to be nated by zoonoses (60.3% of EIDs): the majority of these (71.8%) 10.7% for protozoa, 6.3% for fungi and 3.3% for helminths (see originate in wildlife (for example, severe acute respiratory virus, Supplementary Data and Supplementary Table 2 for a detailed com- Ebola virus), and are increasing significantly over time. We find parison to previous studies). that 54.3% of EID events are caused by bacteria or rickettsia, The incidence of EID events has increased since 1940, reaching a reflecting a large number of drug-resistant microbes in our data- maximum in the 1980s (Fig. 1). We tested whether the increase base. Our results confirm that EID origins are significantly corre- through time was largely attributable to increasing infectious disease lated with socio-economic, environmental and ecological factors, reporting effort (that is, through more efficient diagnostic methods 2,3,13 and provide a basis for identifying regions where new EIDs are and more thorough surveillance ) by calculating the annual num- most likely to originate (emerging disease ‘hotspots’). They also ber of articles published in the Journal of Infectious Diseases (JID) reveal a substantial risk of wildlife zoonotic and vector-borne EIDs since 1945 (see Methods). Controlling for reporting effort, the num- originating at lower latitudes where reporting effort is low. We ber of EID events still shows a highly significant relationship with conclude that global resources to counter disease emergence are time (generalized linear model with Poisson errors, offset by log(JID poorly allocated, with the majority of the scientific and surveil- articles) (GLM ), F5 96.4, P, 0.001, d.f.5 57). This provides P,JID lance effort focused on countries from where the next important the first analytical support for previous suggestions that the threat 1,2,14 EID is least likely to originate. of EIDs to global health is increasing . To further investigate the In the global human population, we report the emergence of 335 peak in EID events in the 1980s, we examined the most frequently infectious diseases between 1940 and 2004. Here we define the first cited driver of EID emergence during this period (see Supplementary Table 1). Increased susceptibility to infection caused the highest pro- temporal origination of an EID (that is, the original case or cluster of cases representing an infectious disease emerging in human popula- portion of events during 1980–90 (25.5%), and we therefore suggest that the spike in EID events in the 1980s is due largely to the emer- tions for the first time—see Methods and Supplementary Table 1) as 2,13 an EID ‘event’. Our database includes EID events caused by newly gence of new diseases associated with the HIV/AIDS pandemic . evolved strains of pathogens (for example, multi-drug-resistant The majority (60.3%) of EID events are caused by zoonotic tuberculosis and chloroquine-resistant malaria), pathogens that have pathogens (defined here as those which have a non-human animal 5,8 recently entered human populations for the first time (for example, source), which is consistent with previous analyses of human EIDs . HIV-1, severe acute respiratory syndrome (SARS) coronavirus), and Furthermore, 71.8% of these zoonotic EID events were caused by pathogens that have probably been present in humans historically, pathogens with a wildlife origin—for example, the emergence of but which have recently increased in incidence (for example, Lyme Nipah virus in Perak, Malaysia and SARS in Guangdong Province, China. The number of EID events caused by pathogens originating in disease). The emergence of these pathogens and their subsequent spread have caused an extremely significant impact on global health wildlife has increased significantly with time, controlling for report- 1–3 and economies . Previous efforts to understand patterns of EID ing effort (GLM F5 60.7, P, 0.001, d.f.5 57), and they consti- P,JID emergence have highlighted viral pathogens (especially RNA viruses) tuted 52.0% of EID events in the most recent decade (1990–2000) as a major threat, owing to their often high rates of nucleotide sub- (Fig. 1). This supports the suggestion that zoonotic EIDs represent an 1,2,7,13,14 increasing and very significant threat to global health . It also stitution, poor mutation error-correction ability and therefore 5,8,10,11 higher capacity to adapt to new hosts, including humans . highlights the importance of understanding the factors that increase However, we find that the majority of pathogens involved in EID contact between wildlife and humans in developing predictive 4,6,9,15 events are bacterial or rickettsial (54.3%). This group is typically approaches to disease emergence . represented by the emergence of drug-resistant bacterial strains Vector-borne diseases are responsible for 22.8% of EID events in (for example, vancomycin-resistant Staphylococcus aureus). Viral or our database, and 28.8% in the last decade (Fig. 1). Our analysis 1 2 Institute of Zoology, Zoological Society of London, Regents Park, London NW1 4RY, UK. Consortium for Conservation Medicine, Wildlife Trust, 460 West 34th Street, 17th Floor, New 3 4 York, New York 10001, USA. Center for International Earth Science Information Network, Earth Institute, Columbia University, 61 Route 9W, Palisades, New York 10964, USA. Odum School of Ecology, University of Georgia, Athens, Georgia 30602, USA. {Present addresses: Department of Economics, Brown University, Providence, Rhode Island 02912, USA (A.S.); School of Public Affairs, Baruch College, City University of New York, 1 Bernard Baruch Way, Box D-0901, New York, New York 10010, USA (D.B.). © 2008Nature PublishingGroup NATURE |Vol 451 |21 February 2008 LETTERS reveals a significant rise in the number of EID events they have caused pathogen distribution , and (2) that the importance of these drivers over time, controlling for reporting effort (GLM F5 49.8, depends on the category of EID event. In particular, we hypothesize P,JID P, 0.001, d.f.5 57). This rise corresponds to climate anomalies that EID events caused by zoonotic pathogens from wildlife are sig- occurring during the 1990s , adding support to hypotheses that cli- nificantly correlated with wildlife biodiversity, and those caused by mate change may drive the emergence of diseases that have vectors drug-resistant pathogens are more correlated with socio-economic sensitive to changes in environmental conditions such as rainfall, conditions than those caused by zoonotic pathogens. temperature and severe weather events . However, this controversial We tested these hypotheses by examining the relationship between issue requires further analyses to test causal relationships between EID the spatial pattern of the different categories of EID events (zoonotic events and climate change . We also report that EID events caused by pathogens originating in wildlife and non-wildlife, drug-resistant drug-resistant microbes (which represent 20.9% of the EID events and vector-borne pathogens, Supplementary Fig. 2), and socio- in our database) have significantly increased with time, controlling economic variables (human population density and human popu- for reporting effort (GLM F5 5.19, P, 0.05, d.f.5 57). This is lation growth), environmental variables (latitude, rainfall) and an P,JID probably related to a corresponding rise in antimicrobial drug use, ecological variable (wildlife host species richness) (see Methods). 2,7,12 particularly in high-latitude developed countries . We found that human population density was a common significant A recent analysis showed a latitudinal spatial gradient in human independent predictor of EID events in all categories, controlling pathogen species richness increasing towards the Equator , in com- for spatial reporting bias by country (see Methods, Table 1 and mon with the distributional pattern of species richness found in Supplementary Table 3). This supports previous hypotheses that many other taxonomic groups . Environmental parameters that disease emergence is largely a product of anthropogenic and demo- promote pathogen transmission at lower latitudes (for example, graphic changes, and is a hidden ‘cost’ of human economic develop- 2,4,7,9,13 higher temperatures and precipitation) are hypothesized to drive this ment . Wildlife host species richness is a significant predictor pattern . Our analyses suggest that there is no such pattern in EID for the emergence of zoonotic EIDs with a wildlife origin, with no role events, which are concentrated in higher latitudes (Supplementary for human population growth, latitude or rainfall (Table 1). The Fig. 1). The highest concentration of EID events per million square emergence of zoonotic EIDs from non-wildlife hosts is predicted kilometres of land was found between 30 and 60 degrees north and by human population density, human population growth, and lati- between 30 and 40 degrees south, with the main hotspots in the tude, and not by wildlife host species richness. EID events caused by northeastern United States, western Europe, Japan and southeastern drug-resistant microbes are affected by human population density Australia (Fig. 2). We hypothesize that (1) socioeconomic drivers and growth, latitude and rainfall. The pattern of EID events caused by (such as human population density, antibiotic drug use and agricul- vector-borne diseases was not correlated with any of the environ- tural practices) are major determinants of the spatial distribution of mental or ecological variables we examined, although we note that EID events, in addition to the ecological or environmental conditions the climate variable used in this analysis (rainfall) does not represent that may affect overall (emerging and non-emerging) human climate change phenomena. Figure 1 | Number of EID events per decade. EID 100 100 events (defined as the temporal origin of an EID, represented by the original case or cluster of cases Helminths Zoonotic: unspecified Fungi Zoonotic: non-wildlife that represents a disease emerging in the human Protozoa Zoonotic: wildlife 80 80 population—see Methods) are plotted with Viruses or prions Non-zoonotic Bacteria or rickettsiae respect to a, pathogen type, b, transmission type, c, drug resistance and d, transmission mode (see keys for details). 60 60 40 40 20 20 1940 1950 1960 1970 1980 1990 2000 1940 1950 1960 1970 1980 1990 2000 100 d Vector-borne Drug-resistant Non vector-borne Non drug-resistant 80 80 20 20 0 0 1940 1950 1960 1970 1980 1990 2000 1940 1950 1960 1970 1980 1990 2000 Decade Decade © 2008Nature PublishingGroup Number of EID events Number of EID events LETTERS 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 6,21 use change, agriculture) have been causally linked to EIDs . 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 1,2,5,8,9,13,14 regions globally. This approach may be valuable for deciding where reviews ). Our findings highlight the critical need for health 4,14,23 to allocate global resources to pre-empt, or combat, the first stages of monitoring and identification of new, potentially zoonotic 10,14,18,22 disease emergence . 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 wildlife diversity by reducing anthropogenic activity may have added Fig. 3), reflecting greater surveillance and infectious disease research 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 bB bB 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 km )] 0.56–0.64*** 1.75–1.90 0.88–1.06*** 2.41–2.89 Human pop. growth (change in persons per km ,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 23 23 Rainfall (mm) (0.14–0.06)x 10 1.00–1.00 (0.32–0.57)x 10 # 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 bB bB 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 km )] 1.03–1.27*** 2.87–3.92 0.41–0.49*** 1.51–1.63 Human pop. growth (change in persons per km , 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 23 23 Rainfall (mm) (0.35–0.61)x 10 * 1.00–1.00 (0.10–0.28)x 10 1.00–1.00 22 22 Wildlife host richness (20.01 to 0.16)x 10 1.00–1.02 (0.28–0.74)x 10 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 range of values obtained from 10 random draws of the 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. © 2008Nature PublishingGroup NATURE |Vol 451 |21 February 2008 LETTERS Figure 3 | Global distribution of a b relative risk of an EID event. Maps are derived for EID events caused by a, zoonotic pathogens from wildlife, b, zoonotic pathogens from non- wildlife, c, drug-resistant pathogens and d, vector-borne pathogens. The relative risk is calculated from regression coefficients and variable values in Table 1 (omitting the variable measuring reporting c d effort), categorized by standard deviations from the mean and mapped on a linear scale from green (lower values) to red (higher values). 10. Burke, D. S. in Pathology of Emerging Infections (eds Nelson, A. M. & Horsburgh, C. METHODS SUMMARY 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 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) (n5 335, see Supplementary Data for emergence. Phil. Trans. R. Soc. Lond. B 356, 991–999 (2001). 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). 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, Washington DC, 1992). the uneven levels of surveillance across countries). We account for these biases by 14. King, D. A., Peckham, C., Waage, J. K., Brownlie, J. & Woolhouse, M. E. J. Infectious quantifying reporting effort in JID and including it in our temporal and spatial diseases: Preparing for the future. Science 313, 1392–1393 (2006). analyses. JID is the premier international journal (highest ISI impact factor 2006: 15. Morse, S. S. Factors in the emergence of infectious diseases. Emerging Infect. Dis. 1, http://portal.isiknowledge.com/) of human infectious disease research that pub- 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 (n5 18,307 ter- distribution of human diseases. PLoS Biol. 2, 740–746 (2004). 20. Grenyer, R. et al. Global distribution and conservation of rare and threatened restrial grid cells). Analyses were conducted on subsets of the EID events—those vertebrates. Nature 444, 93–96 (2006). caused by zoonotic pathogens (defined in our analyses as pathogens that origi- 21. Wolfe, N. D., Dunavan, C. P. & Diamond, J. Origins of major human infectious nated in non-human animals) originating in wildlife and non-wildlife species, diseases. Nature 447, 279–283 (2007). and those caused by drug-resistant and vector-borne pathogens. 22. Ferguson, N. M. et al. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, 209–214 (2005). Full Methods and any associated references are available in the online version of 23. Kuiken, T. et al. Public health — pathogen surveillance in animals. Science 309, the paper at www.nature.com/nature. 1680–1681 (2005). Received 2 August; accepted 11 December 2007. Supplementary Information is linked to the online version of the paper at www.nature.com/nature. 1. Morens, D. M., Folkers, G. K. & Fauci, A. S. The challenge of emerging and re- 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 Author Information Reprints and permissions information is available at reemerging pathogens. Emerging Infect. Dis. 11, 1842–1847 (2005). 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. (daszak@conservationmedicine.org). © 2008Nature PublishingGroup doi:10.1038/nature06536 enterohaemorrhagic Escherichia coli in ‘‘Peru’’). These locations were assigned METHODS corresponding boundaries from ESRI sub-regional or regional data and we EID event definition. In this paper, we are analysing the process of disease randomly selected only one grid cell from the possible grid cells to represent emergence, not just the pathogens that cause them. Therefore, we focus on each particular event. This treated these lesser known events equivalently to those EID ‘events’, which we define as the first temporal emergence of a pathogen in that were assigned a specific point location. a human population which was related to the increase in distribution, increase in Driver definitions. Definitions of the spatial drivers used are as follows: (1) incidence or increase in virulence or other factor which led to that pathogen 25 2 2,4,5,8,13,15 ‘Human population density’ for 2000 (persons per km ); (2) ‘Human popu- being classed as an emerging disease . We chose the 1940 cut-off based on 2 lation growth’, calculated between 1990 and 2000 .We used a dummy variable the Institute of Medicine’s examples of a currently or very recently emerging to indicate grid cells that experienced rapid growth in human population. This disease, all of which had their likely temporal origins within this time period. variable was set to 1 for grid cells where the 1990–2000 human population Single case reports of a new pathogen were not considered to represent the growth exceeded 25% over the decade, and was set to 0 elsewhere; (3) emergence of a disease, and emergence was normally represented by reports, ‘Latitude’ (absolute latitude of the central point of each grid cell, decimal in more than one peer-reviewed paper, of a cluster of cases that were identified in degrees); (4) ‘Rainfall’ (average rainfall per year, mm); (5) ‘Wildlife host species humans for the first time, or (for previously known diseases) considered signifi- richness’. We calculated mammalian species richness as a proxy for wildlife host cantly above background. Only events that had sufficient corroborating evidence species richness. Richness grids were generated from geographic distribution for their geographic and temporal origin were included in our analysis. We based maps for 4,219 terrestrial mammalian species . our data collection on the list of EIDs in ref. 5 updated to 2004. Unlike this 5 Controlling for sampling bias. For our temporal analysis, we included the previous study , we treated different drug-resistant strains of the same microbial number of JID articles per year since 1945 (n 5 17,979 articles) as an offset TOTAL species as separate pathogens and the cause of separate EID events (for example, in our generalized linear model using a Poisson error structure. To control for the emergence of the chloroquine-resistant strain of the malaria pathogen bias in our spatial analysis, we calculated the frequency of the country listed as the (Plasmodium falciparum) in Trujillo, Venezuela in 1957 and the sulphadoxine- address for every author (lead author and coauthors) in each JID article since pyrimethamine-resistant strain in Sa Kaeo, Thailand in 1981). 1973. This generated a measure of reporting effort for each country which was Variable definitions. The biological, temporal and spatial variable definitions of matched to the one degree spatial grid for analysis and was included in the an EID event used are as follows: italic font indicates classes of the variables. (1) multiple logistic regression models. ‘Pathogen’, name of pathogen associated with the EID event. (2) ‘Year’ (the Regression analysis. Each logistic regression was repeated ten times using a earliest year in which the first cluster of cases representing each EID event was separate random draw of the EID event grids for those events where the region reported to have occurred was taken where a range of years was given). (3) reported covered more than one grid cell. The analyses are summarized in ‘Pathogen type’ (PathType): (i) bacterial; (ii) rickettsial; (iii) viral; (iv) prion; Table 1, and given in full in Supplementary Table 3. Different random draws (v) fungal; (vi) helminth; (vii) protozoan. (4) ‘Transmission type’ (TranType): can produce a different number of grid cells with events, even though the num- (0) non-zoonotic (disease emerged without involvement of a non-human host); ber of events does not change. For graphical purposes (that is, in Figs 2 and 3, (1) zoonotic (disease emerged via non-human to human transmission, not and Supplementary Figs 1 and 2), we display the first random draw of the EID including vectors). (5) ‘Zoonotic type’ (ZooType): (0) non-zoonotic (disease event grids. Human population density and number of JID articles were log- emerged via human to human transmission); (1) non-wildlife (zoonotic EID transformed before analysis. Statistical analyses were carried out using SPSS (v. event caused by a pathogen with no known wildlife origin); (2) wildlife (zoonotic 28 29 12.0) and R (v. 2.2.1) . As the spatial autocorrelation (measured using Moran’s EID event caused by a pathogen with a wildlife origin); (3) unspecified (zoonotic I) in the EID event occurrence spatial grids was low (0.1), the data were assumed EID event caused by a pathogen with an unknown origin). (6) ‘Drug resistance’ to represent independent points in these analyses. (DrugRes): (0) event not caused by a drug-resistant microbe; and (1) event caused by a drug-resistant microbe. (7) ‘Transmission mode’ (TranMode): (0) 24. Environmental Research Systems Institute (ESRI). Data & Maps, Version 9.1 pathogen causing the EID event not normally transmitted by a vector; and (1) (Environmental Research Systems Institute, Inc., Redlands, California, 2005). pathogen causing the event transmitted by a vector. (8) ‘Driver’. We classified the 25. Center for International Earth Science Information Network (CIESIN) & Centro most commonly cited underlying primary causal factor (or ‘driver’) associated Internacional de Agricultura Tropical (CIAT). Gridded Population of the World, with the EID event according to the classes listed in refs 2, 13. We re-classified Version 3 (GPWv3): Population Grids (SEDAC, Columbia University, New York, 2005); available at Æhttp://sedac.ciesin.columbia.edu/gpwæ. ‘Economic development and land use’ and ‘Technology and industry’ to form 26. International Institute for Applied Systems Analysis (IIASA) & Food and more descriptive categories: ‘Agricultural industry changes’, ‘Medical industry Agricultural Organization (FAO). Global Agro-Ecological Zones (GAEZ) changes’, ‘Food industry changes’, ‘Land use changes’ and ‘Bushmeat’. (9) (FAO/IIASA, Rome, 2000); available at Æhttp://www.fao.org/ag/agl/agll/gaez/ ‘Location’. Description of where the first cluster of cases representing each index.htmæ. EID event was reported to have occurred. For these descriptions, accurate 27. Sechrest, W. Global Diversity, Endemism and Conservation of Mammals. Thesis, spatial coordinates (point location data) were found for 51.8% of EID events Univ. Virginia (2003). (n5 220) using Global Gazetteer v.2.1 (http://www.fallingrain.com/world/) 28. SPSS. SPSS for Windows, Version 12.0 (SPSS Inc., Chicago, 2006). and these were assigned to corresponding one degree terrestrial spatial grids. 29. R Development Core Team. R: A language and environment for statistical Some EID event locations were lesser known and only described sub-regionally computing, reference index, Version 2.2.1 (R Foundation for Statistical or regionally (for example, SARS in ‘‘Guangdong Province, China’’ or Computing, Vienna, Austria, 2005). © 2008Nature PublishingGroup http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Pubmed Central

Global trends in emerging infectious diseases

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Abstract

Vol 451 | 21 February 2008 |doi:10.1038/nature06536 LETTERS 1 2 3 3 3 4 Kate E. Jones , Nikkita G. Patel , Marc A. Levy , Adam Storeygard {, Deborah Balk {, John L. Gittleman & Peter Daszak Emerging infectious diseases (EIDs) are a significant burden on prion pathogens constitute only 25.4% of EID events, in contrast to 1–3 global economies and public health . Their emergence is thought previous analyses which suggest that 37–44% of emerging pathogens 5,8,11 to be driven largely by socio-economic, environmental and eco- are viruses or prions and 10–30% bacteria or rickettsia . This 1–9 logical factors , but no comparative study has explicitly analysed follows our classification of each individual drug-resistant microbial these linkages to understand global temporal and spatial patterns strain as a separate pathogen in our database, and reflects more of EIDs. Here we analyse a database of 335 EID ‘events’ (origins of accurately the true significance of antimicrobial drug resistance for EIDs) between 1940 and 2004, and demonstrate non-random glo- global health, in which different pathogen strains can cause separate significant outbreaks . In broad concurrence with previous studies bal patterns. EID events have risen significantly over time after 5,8,11 controlling for reporting bias, with their peak incidence (in the on the characteristics of emerging human pathogens , we find the 1980s) concomitant with the HIV pandemic. EID events are domi- percentages of EID events caused by other pathogen types to be nated by zoonoses (60.3% of EIDs): the majority of these (71.8%) 10.7% for protozoa, 6.3% for fungi and 3.3% for helminths (see originate in wildlife (for example, severe acute respiratory virus, Supplementary Data and Supplementary Table 2 for a detailed com- Ebola virus), and are increasing significantly over time. We find parison to previous studies). that 54.3% of EID events are caused by bacteria or rickettsia, The incidence of EID events has increased since 1940, reaching a reflecting a large number of drug-resistant microbes in our data- maximum in the 1980s (Fig. 1). We tested whether the increase base. Our results confirm that EID origins are significantly corre- through time was largely attributable to increasing infectious disease lated with socio-economic, environmental and ecological factors, reporting effort (that is, through more efficient diagnostic methods 2,3,13 and provide a basis for identifying regions where new EIDs are and more thorough surveillance ) by calculating the annual num- most likely to originate (emerging disease ‘hotspots’). They also ber of articles published in the Journal of Infectious Diseases (JID) reveal a substantial risk of wildlife zoonotic and vector-borne EIDs since 1945 (see Methods). Controlling for reporting effort, the num- originating at lower latitudes where reporting effort is low. We ber of EID events still shows a highly significant relationship with conclude that global resources to counter disease emergence are time (generalized linear model with Poisson errors, offset by log(JID poorly allocated, with the majority of the scientific and surveil- articles) (GLM ), F5 96.4, P, 0.001, d.f.5 57). This provides P,JID lance effort focused on countries from where the next important the first analytical support for previous suggestions that the threat 1,2,14 EID is least likely to originate. of EIDs to global health is increasing . To further investigate the In the global human population, we report the emergence of 335 peak in EID events in the 1980s, we examined the most frequently infectious diseases between 1940 and 2004. Here we define the first cited driver of EID emergence during this period (see Supplementary Table 1). Increased susceptibility to infection caused the highest pro- temporal origination of an EID (that is, the original case or cluster of cases representing an infectious disease emerging in human popula- portion of events during 1980–90 (25.5%), and we therefore suggest that the spike in EID events in the 1980s is due largely to the emer- tions for the first time—see Methods and Supplementary Table 1) as 2,13 an EID ‘event’. Our database includes EID events caused by newly gence of new diseases associated with the HIV/AIDS pandemic . evolved strains of pathogens (for example, multi-drug-resistant The majority (60.3%) of EID events are caused by zoonotic tuberculosis and chloroquine-resistant malaria), pathogens that have pathogens (defined here as those which have a non-human animal 5,8 recently entered human populations for the first time (for example, source), which is consistent with previous analyses of human EIDs . HIV-1, severe acute respiratory syndrome (SARS) coronavirus), and Furthermore, 71.8% of these zoonotic EID events were caused by pathogens that have probably been present in humans historically, pathogens with a wildlife origin—for example, the emergence of but which have recently increased in incidence (for example, Lyme Nipah virus in Perak, Malaysia and SARS in Guangdong Province, China. The number of EID events caused by pathogens originating in disease). The emergence of these pathogens and their subsequent spread have caused an extremely significant impact on global health wildlife has increased significantly with time, controlling for report- 1–3 and economies . Previous efforts to understand patterns of EID ing effort (GLM F5 60.7, P, 0.001, d.f.5 57), and they consti- P,JID emergence have highlighted viral pathogens (especially RNA viruses) tuted 52.0% of EID events in the most recent decade (1990–2000) as a major threat, owing to their often high rates of nucleotide sub- (Fig. 1). This supports the suggestion that zoonotic EIDs represent an 1,2,7,13,14 increasing and very significant threat to global health . It also stitution, poor mutation error-correction ability and therefore 5,8,10,11 higher capacity to adapt to new hosts, including humans . highlights the importance of understanding the factors that increase However, we find that the majority of pathogens involved in EID contact between wildlife and humans in developing predictive 4,6,9,15 events are bacterial or rickettsial (54.3%). This group is typically approaches to disease emergence . represented by the emergence of drug-resistant bacterial strains Vector-borne diseases are responsible for 22.8% of EID events in (for example, vancomycin-resistant Staphylococcus aureus). Viral or our database, and 28.8% in the last decade (Fig. 1). Our analysis 1 2 Institute of Zoology, Zoological Society of London, Regents Park, London NW1 4RY, UK. Consortium for Conservation Medicine, Wildlife Trust, 460 West 34th Street, 17th Floor, New 3 4 York, New York 10001, USA. Center for International Earth Science Information Network, Earth Institute, Columbia University, 61 Route 9W, Palisades, New York 10964, USA. Odum School of Ecology, University of Georgia, Athens, Georgia 30602, USA. {Present addresses: Department of Economics, Brown University, Providence, Rhode Island 02912, USA (A.S.); School of Public Affairs, Baruch College, City University of New York, 1 Bernard Baruch Way, Box D-0901, New York, New York 10010, USA (D.B.). © 2008Nature PublishingGroup NATURE |Vol 451 |21 February 2008 LETTERS reveals a significant rise in the number of EID events they have caused pathogen distribution , and (2) that the importance of these drivers over time, controlling for reporting effort (GLM F5 49.8, depends on the category of EID event. In particular, we hypothesize P,JID P, 0.001, d.f.5 57). This rise corresponds to climate anomalies that EID events caused by zoonotic pathogens from wildlife are sig- occurring during the 1990s , adding support to hypotheses that cli- nificantly correlated with wildlife biodiversity, and those caused by mate change may drive the emergence of diseases that have vectors drug-resistant pathogens are more correlated with socio-economic sensitive to changes in environmental conditions such as rainfall, conditions than those caused by zoonotic pathogens. temperature and severe weather events . However, this controversial We tested these hypotheses by examining the relationship between issue requires further analyses to test causal relationships between EID the spatial pattern of the different categories of EID events (zoonotic events and climate change . We also report that EID events caused by pathogens originating in wildlife and non-wildlife, drug-resistant drug-resistant microbes (which represent 20.9% of the EID events and vector-borne pathogens, Supplementary Fig. 2), and socio- in our database) have significantly increased with time, controlling economic variables (human population density and human popu- for reporting effort (GLM F5 5.19, P, 0.05, d.f.5 57). This is lation growth), environmental variables (latitude, rainfall) and an P,JID probably related to a corresponding rise in antimicrobial drug use, ecological variable (wildlife host species richness) (see Methods). 2,7,12 particularly in high-latitude developed countries . We found that human population density was a common significant A recent analysis showed a latitudinal spatial gradient in human independent predictor of EID events in all categories, controlling pathogen species richness increasing towards the Equator , in com- for spatial reporting bias by country (see Methods, Table 1 and mon with the distributional pattern of species richness found in Supplementary Table 3). This supports previous hypotheses that many other taxonomic groups . Environmental parameters that disease emergence is largely a product of anthropogenic and demo- promote pathogen transmission at lower latitudes (for example, graphic changes, and is a hidden ‘cost’ of human economic develop- 2,4,7,9,13 higher temperatures and precipitation) are hypothesized to drive this ment . Wildlife host species richness is a significant predictor pattern . Our analyses suggest that there is no such pattern in EID for the emergence of zoonotic EIDs with a wildlife origin, with no role events, which are concentrated in higher latitudes (Supplementary for human population growth, latitude or rainfall (Table 1). The Fig. 1). The highest concentration of EID events per million square emergence of zoonotic EIDs from non-wildlife hosts is predicted kilometres of land was found between 30 and 60 degrees north and by human population density, human population growth, and lati- between 30 and 40 degrees south, with the main hotspots in the tude, and not by wildlife host species richness. EID events caused by northeastern United States, western Europe, Japan and southeastern drug-resistant microbes are affected by human population density Australia (Fig. 2). We hypothesize that (1) socioeconomic drivers and growth, latitude and rainfall. The pattern of EID events caused by (such as human population density, antibiotic drug use and agricul- vector-borne diseases was not correlated with any of the environ- tural practices) are major determinants of the spatial distribution of mental or ecological variables we examined, although we note that EID events, in addition to the ecological or environmental conditions the climate variable used in this analysis (rainfall) does not represent that may affect overall (emerging and non-emerging) human climate change phenomena. Figure 1 | Number of EID events per decade. EID 100 100 events (defined as the temporal origin of an EID, represented by the original case or cluster of cases Helminths Zoonotic: unspecified Fungi Zoonotic: non-wildlife that represents a disease emerging in the human Protozoa Zoonotic: wildlife 80 80 population—see Methods) are plotted with Viruses or prions Non-zoonotic Bacteria or rickettsiae respect to a, pathogen type, b, transmission type, c, drug resistance and d, transmission mode (see keys for details). 60 60 40 40 20 20 1940 1950 1960 1970 1980 1990 2000 1940 1950 1960 1970 1980 1990 2000 100 d Vector-borne Drug-resistant Non vector-borne Non drug-resistant 80 80 20 20 0 0 1940 1950 1960 1970 1980 1990 2000 1940 1950 1960 1970 1980 1990 2000 Decade Decade © 2008Nature PublishingGroup Number of EID events Number of EID events LETTERS 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 6,21 use change, agriculture) have been causally linked to EIDs . 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 1,2,5,8,9,13,14 regions globally. This approach may be valuable for deciding where reviews ). Our findings highlight the critical need for health 4,14,23 to allocate global resources to pre-empt, or combat, the first stages of monitoring and identification of new, potentially zoonotic 10,14,18,22 disease emergence . 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 wildlife diversity by reducing anthropogenic activity may have added Fig. 3), reflecting greater surveillance and infectious disease research 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 bB bB 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 km )] 0.56–0.64*** 1.75–1.90 0.88–1.06*** 2.41–2.89 Human pop. growth (change in persons per km ,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 23 23 Rainfall (mm) (0.14–0.06)x 10 1.00–1.00 (0.32–0.57)x 10 # 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 bB bB 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 km )] 1.03–1.27*** 2.87–3.92 0.41–0.49*** 1.51–1.63 Human pop. growth (change in persons per km , 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 23 23 Rainfall (mm) (0.35–0.61)x 10 * 1.00–1.00 (0.10–0.28)x 10 1.00–1.00 22 22 Wildlife host richness (20.01 to 0.16)x 10 1.00–1.02 (0.28–0.74)x 10 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 range of values obtained from 10 random draws of the 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. © 2008Nature PublishingGroup NATURE |Vol 451 |21 February 2008 LETTERS Figure 3 | Global distribution of a b relative risk of an EID event. Maps are derived for EID events caused by a, zoonotic pathogens from wildlife, b, zoonotic pathogens from non- wildlife, c, drug-resistant pathogens and d, vector-borne pathogens. The relative risk is calculated from regression coefficients and variable values in Table 1 (omitting the variable measuring reporting c d effort), categorized by standard deviations from the mean and mapped on a linear scale from green (lower values) to red (higher values). 10. Burke, D. S. in Pathology of Emerging Infections (eds Nelson, A. M. & Horsburgh, C. METHODS SUMMARY 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 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) (n5 335, see Supplementary Data for emergence. Phil. Trans. R. Soc. Lond. B 356, 991–999 (2001). 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). 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, Washington DC, 1992). the uneven levels of surveillance across countries). We account for these biases by 14. King, D. A., Peckham, C., Waage, J. K., Brownlie, J. & Woolhouse, M. E. J. Infectious quantifying reporting effort in JID and including it in our temporal and spatial diseases: Preparing for the future. Science 313, 1392–1393 (2006). analyses. JID is the premier international journal (highest ISI impact factor 2006: 15. Morse, S. S. Factors in the emergence of infectious diseases. Emerging Infect. Dis. 1, http://portal.isiknowledge.com/) of human infectious disease research that pub- 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 (n5 18,307 ter- distribution of human diseases. PLoS Biol. 2, 740–746 (2004). 20. Grenyer, R. et al. Global distribution and conservation of rare and threatened restrial grid cells). Analyses were conducted on subsets of the EID events—those vertebrates. Nature 444, 93–96 (2006). caused by zoonotic pathogens (defined in our analyses as pathogens that origi- 21. Wolfe, N. D., Dunavan, C. P. & Diamond, J. Origins of major human infectious nated in non-human animals) originating in wildlife and non-wildlife species, diseases. Nature 447, 279–283 (2007). and those caused by drug-resistant and vector-borne pathogens. 22. Ferguson, N. M. et al. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437, 209–214 (2005). Full Methods and any associated references are available in the online version of 23. Kuiken, T. et al. Public health — pathogen surveillance in animals. Science 309, the paper at www.nature.com/nature. 1680–1681 (2005). Received 2 August; accepted 11 December 2007. Supplementary Information is linked to the online version of the paper at www.nature.com/nature. 1. Morens, D. M., Folkers, G. K. & Fauci, A. S. The challenge of emerging and re- 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 Author Information Reprints and permissions information is available at reemerging pathogens. Emerging Infect. Dis. 11, 1842–1847 (2005). 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. (daszak@conservationmedicine.org). © 2008Nature PublishingGroup doi:10.1038/nature06536 enterohaemorrhagic Escherichia coli in ‘‘Peru’’). These locations were assigned METHODS corresponding boundaries from ESRI sub-regional or regional data and we EID event definition. In this paper, we are analysing the process of disease randomly selected only one grid cell from the possible grid cells to represent emergence, not just the pathogens that cause them. Therefore, we focus on each particular event. This treated these lesser known events equivalently to those EID ‘events’, which we define as the first temporal emergence of a pathogen in that were assigned a specific point location. a human population which was related to the increase in distribution, increase in Driver definitions. Definitions of the spatial drivers used are as follows: (1) incidence or increase in virulence or other factor which led to that pathogen 25 2 2,4,5,8,13,15 ‘Human population density’ for 2000 (persons per km ); (2) ‘Human popu- being classed as an emerging disease . We chose the 1940 cut-off based on 2 lation growth’, calculated between 1990 and 2000 .We used a dummy variable the Institute of Medicine’s examples of a currently or very recently emerging to indicate grid cells that experienced rapid growth in human population. This disease, all of which had their likely temporal origins within this time period. variable was set to 1 for grid cells where the 1990–2000 human population Single case reports of a new pathogen were not considered to represent the growth exceeded 25% over the decade, and was set to 0 elsewhere; (3) emergence of a disease, and emergence was normally represented by reports, ‘Latitude’ (absolute latitude of the central point of each grid cell, decimal in more than one peer-reviewed paper, of a cluster of cases that were identified in degrees); (4) ‘Rainfall’ (average rainfall per year, mm); (5) ‘Wildlife host species humans for the first time, or (for previously known diseases) considered signifi- richness’. We calculated mammalian species richness as a proxy for wildlife host cantly above background. Only events that had sufficient corroborating evidence species richness. Richness grids were generated from geographic distribution for their geographic and temporal origin were included in our analysis. We based maps for 4,219 terrestrial mammalian species . our data collection on the list of EIDs in ref. 5 updated to 2004. Unlike this 5 Controlling for sampling bias. For our temporal analysis, we included the previous study , we treated different drug-resistant strains of the same microbial number of JID articles per year since 1945 (n 5 17,979 articles) as an offset TOTAL species as separate pathogens and the cause of separate EID events (for example, in our generalized linear model using a Poisson error structure. To control for the emergence of the chloroquine-resistant strain of the malaria pathogen bias in our spatial analysis, we calculated the frequency of the country listed as the (Plasmodium falciparum) in Trujillo, Venezuela in 1957 and the sulphadoxine- address for every author (lead author and coauthors) in each JID article since pyrimethamine-resistant strain in Sa Kaeo, Thailand in 1981). 1973. This generated a measure of reporting effort for each country which was Variable definitions. The biological, temporal and spatial variable definitions of matched to the one degree spatial grid for analysis and was included in the an EID event used are as follows: italic font indicates classes of the variables. (1) multiple logistic regression models. ‘Pathogen’, name of pathogen associated with the EID event. (2) ‘Year’ (the Regression analysis. Each logistic regression was repeated ten times using a earliest year in which the first cluster of cases representing each EID event was separate random draw of the EID event grids for those events where the region reported to have occurred was taken where a range of years was given). (3) reported covered more than one grid cell. The analyses are summarized in ‘Pathogen type’ (PathType): (i) bacterial; (ii) rickettsial; (iii) viral; (iv) prion; Table 1, and given in full in Supplementary Table 3. Different random draws (v) fungal; (vi) helminth; (vii) protozoan. (4) ‘Transmission type’ (TranType): can produce a different number of grid cells with events, even though the num- (0) non-zoonotic (disease emerged without involvement of a non-human host); ber of events does not change. For graphical purposes (that is, in Figs 2 and 3, (1) zoonotic (disease emerged via non-human to human transmission, not and Supplementary Figs 1 and 2), we display the first random draw of the EID including vectors). (5) ‘Zoonotic type’ (ZooType): (0) non-zoonotic (disease event grids. Human population density and number of JID articles were log- emerged via human to human transmission); (1) non-wildlife (zoonotic EID transformed before analysis. Statistical analyses were carried out using SPSS (v. event caused by a pathogen with no known wildlife origin); (2) wildlife (zoonotic 28 29 12.0) and R (v. 2.2.1) . As the spatial autocorrelation (measured using Moran’s EID event caused by a pathogen with a wildlife origin); (3) unspecified (zoonotic I) in the EID event occurrence spatial grids was low (0.1), the data were assumed EID event caused by a pathogen with an unknown origin). (6) ‘Drug resistance’ to represent independent points in these analyses. (DrugRes): (0) event not caused by a drug-resistant microbe; and (1) event caused by a drug-resistant microbe. (7) ‘Transmission mode’ (TranMode): (0) 24. Environmental Research Systems Institute (ESRI). Data & Maps, Version 9.1 pathogen causing the EID event not normally transmitted by a vector; and (1) (Environmental Research Systems Institute, Inc., Redlands, California, 2005). pathogen causing the event transmitted by a vector. (8) ‘Driver’. We classified the 25. Center for International Earth Science Information Network (CIESIN) & Centro most commonly cited underlying primary causal factor (or ‘driver’) associated Internacional de Agricultura Tropical (CIAT). Gridded Population of the World, with the EID event according to the classes listed in refs 2, 13. We re-classified Version 3 (GPWv3): Population Grids (SEDAC, Columbia University, New York, 2005); available at Æhttp://sedac.ciesin.columbia.edu/gpwæ. ‘Economic development and land use’ and ‘Technology and industry’ to form 26. International Institute for Applied Systems Analysis (IIASA) & Food and more descriptive categories: ‘Agricultural industry changes’, ‘Medical industry Agricultural Organization (FAO). Global Agro-Ecological Zones (GAEZ) changes’, ‘Food industry changes’, ‘Land use changes’ and ‘Bushmeat’. (9) (FAO/IIASA, Rome, 2000); available at Æhttp://www.fao.org/ag/agl/agll/gaez/ ‘Location’. Description of where the first cluster of cases representing each index.htmæ. EID event was reported to have occurred. For these descriptions, accurate 27. Sechrest, W. Global Diversity, Endemism and Conservation of Mammals. Thesis, spatial coordinates (point location data) were found for 51.8% of EID events Univ. Virginia (2003). (n5 220) using Global Gazetteer v.2.1 (http://www.fallingrain.com/world/) 28. SPSS. SPSS for Windows, Version 12.0 (SPSS Inc., Chicago, 2006). and these were assigned to corresponding one degree terrestrial spatial grids. 29. R Development Core Team. R: A language and environment for statistical Some EID event locations were lesser known and only described sub-regionally computing, reference index, Version 2.2.1 (R Foundation for Statistical or regionally (for example, SARS in ‘‘Guangdong Province, China’’ or Computing, Vienna, Austria, 2005). © 2008Nature PublishingGroup

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