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Shearer FM, Longbottom J, Browne AJ, Pigott DM, Brady OJ, Kraemer MUG, Marinho F, Yactayo S, de Araújo VEM, da Nóbrega AA, Fullman N, Ray SE, Mosser JF, Stanaway JD, Lim SS, Reiner RC, Moyes CL, Hay SI, Golding N. Existing and potential infection risk zones of yellow fever worldwide: a modelling analysis. LANCET GLOBAL HEALTH 2018; 6:e270-e278. [PMID: 29398634 PMCID: PMC5809716 DOI: 10.1016/s2214-109x(18)30024-x] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Background Yellow fever cases are under-reported and the exact distribution of the disease is unknown. An effective vaccine is available but more information is needed about which populations within risk zones should be targeted to implement interventions. Substantial outbreaks of yellow fever in Angola, Democratic Republic of the Congo, and Brazil, coupled with the global expansion of the range of its main urban vector, Aedes aegypti, suggest that yellow fever has the propensity to spread further internationally. The aim of this study was to estimate the disease's contemporary distribution and potential for spread into new areas to help inform optimal control and prevention strategies. Methods We assembled 1155 geographical records of yellow fever virus infection in people from 1970 to 2016. We used a Poisson point process boosted regression tree model that explicitly incorporated environmental and biological explanatory covariates, vaccination coverage, and spatial variability in disease reporting rates to predict the relative risk of apparent yellow fever virus infection at a 5 × 5 km resolution across all risk zones (47 countries across the Americas and Africa). We also used the fitted model to predict the receptivity of areas outside at-risk zones to the introduction or reintroduction of yellow fever transmission. By use of previously published estimates of annual national case numbers, we used the model to map subnational variation in incidence of yellow fever across at-risk countries and to estimate the number of cases averted by vaccination worldwide. Findings Substantial international and subnational spatial variation exists in relative risk and incidence of yellow fever as well as varied success of vaccination in reducing incidence in several high-risk regions, including Brazil, Cameroon, and Togo. Areas with the highest predicted average annual case numbers include large parts of Nigeria, the Democratic Republic of the Congo, and South Sudan, where vaccination coverage in 2016 was estimated to be substantially less than the recommended threshold to prevent outbreaks. Overall, we estimated that vaccination coverage levels achieved by 2016 avert between 94 336 and 118 500 cases of yellow fever annually within risk zones, on the basis of conservative and optimistic vaccination scenarios. The areas outside at-risk regions with predicted high receptivity to yellow fever transmission (eg, parts of Malaysia, Indonesia, and Thailand) were less extensive than the distribution of the main urban vector, A aegypti, with low receptivity to yellow fever transmission in southern China, where A aegypti is known to occur. Interpretation Our results provide the evidence base for targeting vaccination campaigns within risk zones, as well as emphasising their high effectiveness. Our study highlights areas where public health authorities should be most vigilant for potential spread or importation events. Funding Bill & Melinda Gates Foundation.
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Affiliation(s)
- Freya M Shearer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Joshua Longbottom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Annie J Browne
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Oliver J Brady
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Moritz U G Kraemer
- Department of Zoology, University of Oxford, Oxford, UK; Harvard Medical School, Boston, MA, USA; Boston Children's Hospital, Boston, MA, USA
| | - Fatima Marinho
- University of State of Rio de Janeiro, Maracana, Rio de Janeiro, Brazil
| | - Sergio Yactayo
- World Health Organization, Infectious Hazard Management, Geneva, Switzerland
| | | | - Aglaêr A da Nóbrega
- Secretariat of Health Surveillance of the Ministry of Health of Brazil, Brazil
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Sarah E Ray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jonathan F Mosser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Division of Pediatric Infectious Diseases, Seattle Children's Hospital/University of Washington, Seattle, WA, USA
| | - Jeffrey D Stanaway
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Catherine L Moyes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Simon I Hay
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
| | - Nick Golding
- Quantitative & Applied Ecology Group, School of BioSciences, University of Melbourne, Parkville, VIC, Australia
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Golding N, Burstein R, Longbottom J, Browne AJ, Fullman N, Osgood-Zimmerman A, Earl L, Bhatt S, Cameron E, Casey DC, Dwyer-Lindgren L, Farag TH, Flaxman AD, Fraser MS, Gething PW, Gibson HS, Graetz N, Krause LK, Kulikoff XR, Lim SS, Mappin B, Morozoff C, Reiner RC, Sligar A, Smith DL, Wang H, Weiss DJ, Murray CJL, Moyes CL, Hay SI. Mapping under-5 and neonatal mortality in Africa, 2000-15: a baseline analysis for the Sustainable Development Goals. Lancet 2017; 390:2171-2182. [PMID: 28958464 PMCID: PMC5687451 DOI: 10.1016/s0140-6736(17)31758-0] [Citation(s) in RCA: 177] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 06/03/2017] [Accepted: 06/26/2017] [Indexed: 01/29/2023]
Abstract
BACKGROUND During the Millennium Development Goal (MDG) era, many countries in Africa achieved marked reductions in under-5 and neonatal mortality. Yet the pace of progress toward these goals substantially varied at the national level, demonstrating an essential need for tracking even more local trends in child mortality. With the adoption of the Sustainable Development Goals (SDGs) in 2015, which established ambitious targets for improving child survival by 2030, optimal intervention planning and targeting will require understanding of trends and rates of progress at a higher spatial resolution. In this study, we aimed to generate high-resolution estimates of under-5 and neonatal all-cause mortality across 46 countries in Africa. METHODS We assembled 235 geographically resolved household survey and census data sources on child deaths to produce estimates of under-5 and neonatal mortality at a resolution of 5 × 5 km grid cells across 46 African countries for 2000, 2005, 2010, and 2015. We used a Bayesian geostatistical analytical framework to generate these estimates, and implemented predictive validity tests. In addition to reporting 5 × 5 km estimates, we also aggregated results obtained from these estimates into three different levels-national, and subnational administrative levels 1 and 2-to provide the full range of geospatial resolution that local, national, and global decision makers might require. FINDINGS Amid improving child survival in Africa, there was substantial heterogeneity in absolute levels of under-5 and neonatal mortality in 2015, as well as the annualised rates of decline achieved from 2000 to 2015. Subnational areas in countries such as Botswana, Rwanda, and Ethiopia recorded some of the largest decreases in child mortality rates since 2000, positioning them well to achieve SDG targets by 2030 or earlier. Yet these places were the exception for Africa, since many areas, particularly in central and western Africa, must reduce under-5 mortality rates by at least 8·8% per year, between 2015 and 2030, to achieve the SDG 3.2 target for under-5 mortality by 2030. INTERPRETATION In the absence of unprecedented political commitment, financial support, and medical advances, the viability of SDG 3.2 achievement in Africa is precarious at best. By producing under-5 and neonatal mortality rates at multiple levels of geospatial resolution over time, this study provides key information for decision makers to target interventions at populations in the greatest need. In an era when precision public health increasingly has the potential to transform the design, implementation, and impact of health programmes, our 5 × 5 km estimates of child mortality in Africa provide a baseline against which local, national, and global stakeholders can map the pathways for ending preventable child deaths by 2030. FUNDING Bill & Melinda Gates Foundation.
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Affiliation(s)
- Nick Golding
- School of BioSciences, University of Melbourne, Parkville, VIC, Australia
| | - Roy Burstein
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Joshua Longbottom
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Annie J Browne
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Lucas Earl
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Samir Bhatt
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Ewan Cameron
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Daniel C Casey
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Laura Dwyer-Lindgren
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Tamer H Farag
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Abraham D Flaxman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Maya S Fraser
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Peter W Gething
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Harry S Gibson
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Nicholas Graetz
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Xie Rachel Kulikoff
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Stephen S Lim
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Bonnie Mappin
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Chloe Morozoff
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Amber Sligar
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Haidong Wang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Daniel J Weiss
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - Catherine L Moyes
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Simon I Hay
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA.
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Bhatt S, Cameron E, Flaxman SR, Weiss DJ, Smith DL, Gething PW. Improved prediction accuracy for disease risk mapping using Gaussian process stacked generalization. J R Soc Interface 2017; 14:20170520. [PMID: 28931634 PMCID: PMC5636278 DOI: 10.1098/rsif.2017.0520] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 08/30/2017] [Indexed: 11/12/2022] Open
Abstract
Maps of infectious disease-charting spatial variations in the force of infection, degree of endemicity and the burden on human health-provide an essential evidence base to support planning towards global health targets. Contemporary disease mapping efforts have embraced statistical modelling approaches to properly acknowledge uncertainties in both the available measurements and their spatial interpolation. The most common such approach is Gaussian process regression, a mathematical framework composed of two components: a mean function harnessing the predictive power of multiple independent variables, and a covariance function yielding spatio-temporal shrinkage against residual variation from the mean. Though many techniques have been developed to improve the flexibility and fitting of the covariance function, models for the mean function have typically been restricted to simple linear terms. For infectious diseases, known to be driven by complex interactions between environmental and socio-economic factors, improved modelling of the mean function can greatly boost predictive power. Here, we present an ensemble approach based on stacked generalization that allows for multiple nonlinear algorithmic mean functions to be jointly embedded within the Gaussian process framework. We apply this method to mapping Plasmodium falciparum prevalence data in sub-Saharan Africa and show that the generalized ensemble approach markedly outperforms any individual method.
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Affiliation(s)
- Samir Bhatt
- Department of Infectious Disease Epidemiology, Imperial College London, London W2 1PG, UK
| | - Ewan Cameron
- Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Seth R Flaxman
- Department of Statistics, University of Oxford, 24-29 St Giles, Oxford OX1 3LB, UK
| | - Daniel J Weiss
- Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98121, USA
| | - Peter W Gething
- Oxford Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
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Tsalyuk M, Kelly M, Getz WM. Improving the Prediction of African Savanna Vegetation Variables Using Time Series of MODIS Products. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING : OFFICIAL PUBLICATION OF THE INTERNATIONAL SOCIETY FOR PHOTOGRAMMETRY AND REMOTE SENSING (ISPRS) 2017; 131:77-91. [PMID: 30739997 PMCID: PMC6368348 DOI: 10.1016/j.isprsjprs.2017.07.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
African savanna vegetation is subject to extensive degradation as a result of rapid climate and land use change. To better understand these changes detailed assessment of vegetation structure is needed across an extensive spatial scale and at a fine temporal resolution. Applying remote sensing techniques to savanna vegetation is challenging due to sparse cover, high background soil signal, and difficulty to differentiate between spectral signals of bare soil and dry vegetation. In this paper, we attempt to resolve these challenges by analyzing time series of four MODIS Vegetation Products (VPs): Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fraction of Photosynthetically Active Radiation (FPAR) for Etosha National Park, a semiarid savanna in north-central Namibia. We create models to predict the density, cover, and biomass of the main savanna vegetation forms: grass, shrubs, and trees. To calibrate remote sensing data we developed an extensive and relatively rapid field methodology and measured herbaceous and woody vegetation during both the dry and wet seasons. We compared the efficacy of the four MODIS-derived VPs in predicting vegetation field measured variables. We then compared the optimal time span of VP time series to predict ground-measured vegetation. We found that Multiyear Partial Least Square Regression (PLSR) models were superior to single year or single date models. Our results show that NDVI-based PLSR models yield robust prediction of tree density (R2 =0.79, relative Root Mean Square Error, rRMSE=1.9%) and tree cover (R2 =0.78, rRMSE=0.3%). EVI provided the best model for shrub density (R2 =0.82) and shrub cover (R2 =0.83), but was only marginally superior over models based on other VPs. FPAR was the best predictor of vegetation biomass of trees (R2 =0.76), shrubs (R2 =0.83), and grass (R2 =0.91). Finally, we addressed an enduring challenge in the remote sensing of semiarid vegetation by examining the transferability of predictive models through space and time. Our results show that models created in the wetter part of Etosha could accurately predict trees' and shrubs' variables in the drier part of the reserve and vice versa. Moreover, our results demonstrate that models created for vegetation variables in the dry season of 2011 could be successfully applied to predict vegetation in the wet season of 2012. We conclude that extensive field data combined with multiyear time series of MODIS vegetation products can produce robust predictive models for multiple vegetation forms in the African savanna. These methods advance the monitoring of savanna vegetation dynamics and contribute to improved management and conservation of these valuable ecosystems.
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Affiliation(s)
- Miriam Tsalyuk
- Department of Environmental Sciences, Policy & Management, 130 Mulford Hall #3114, University of California Berkeley, CA 94720-3114, USA
| | - Maggi Kelly
- Department of Environmental Sciences, Policy & Management, 130 Mulford Hall #3114, University of California Berkeley, CA 94720-3114, USA
| | - Wayne M. Getz
- Department of Environmental Sciences, Policy & Management, 130 Mulford Hall #3114, University of California Berkeley, CA 94720-3114, USA
- School of Mathematical Sciences, University of KwaZulu-Natal, Private Bag X54001, Durban 4000, South Africa
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55
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Evapotranspiration Mapping in a Heterogeneous Landscape Using Remote Sensing and Global Weather Datasets: Application to the Mara Basin, East Africa. REMOTE SENSING 2017. [DOI: 10.3390/rs9040390] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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56
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Longbottom J, Browne AJ, Pigott DM, Sinka ME, Golding N, Hay SI, Moyes CL, Shearer FM. Mapping the spatial distribution of the Japanese encephalitis vector, Culex tritaeniorhynchus Giles, 1901 (Diptera: Culicidae) within areas of Japanese encephalitis risk. Parasit Vectors 2017; 10:148. [PMID: 28302156 PMCID: PMC5356256 DOI: 10.1186/s13071-017-2086-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 03/10/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Japanese encephalitis (JE) is one of the most significant aetiological agents of viral encephalitis in Asia. This medically important arbovirus is primarily spread from vertebrate hosts to humans by the mosquito vector Culex tritaeniorhynchus. Knowledge of the contemporary distribution of this vector species is lacking, and efforts to define areas of disease risk greatly depend on a thorough understanding of the variation in this mosquito's geographical distribution. RESULTS We assembled a contemporary database of Cx. tritaeniorhynchus presence records within Japanese encephalitis risk areas from formal literature and other relevant resources, resulting in 1,045 geo-referenced, spatially and temporally unique presence records spanning from 1928 to 2014 (71.9% of records obtained between 2001 and 2014). These presence data were combined with a background dataset capturing sample bias in our presence dataset, along with environmental and socio-economic covariates, to inform a boosted regression tree model predicting environmental suitability for Cx. tritaeniorhynchus at each 5 × 5 km gridded cell within areas of JE risk. The resulting fine-scale map highlights areas of high environmental suitability for this species across India, Nepal and China that coincide with areas of high JE incidence, emphasising the role of this vector in disease transmission and the utility of the map generated. CONCLUSIONS Our map contributes towards efforts determining the spatial heterogeneity in Cx. tritaeniorhynchus distribution within the limits of JE transmission. Specifically, this map can be used to inform vector control programs and can be used to identify key areas where the prevention of Cx. tritaeniorhynchus establishment should be a priority.
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Affiliation(s)
- Joshua Longbottom
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Annie J. Browne
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - David M. Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA USA
| | - Marianne E. Sinka
- Oxford Long Term Ecology Laboratory, Department of Zoology, University of Oxford, Oxford, UK
| | - Nick Golding
- Quantitative & Applied Ecology Group, School of BioSciences, University of Melbourne, Parkville, VIC Australia
| | - Simon I. Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA USA
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Catherine L. Moyes
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Freya M. Shearer
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
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Nsoesie EO, Kraemer MU, Golding N, Pigott DM, Brady OJ, Moyes CL, Johansson MA, Gething PW, Velayudhan R, Khan K, Hay SI, Brownstein JS. Global distribution and environmental suitability for chikungunya virus, 1952 to 2015. ACTA ACUST UNITED AC 2017; 21. [PMID: 27239817 DOI: 10.2807/1560-7917.es.2016.21.20.30234] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Accepted: 03/20/2016] [Indexed: 01/19/2023]
Abstract
Chikungunya fever is an acute febrile illness caused by the chikungunya virus (CHIKV), which is transmitted to humans by Aedes mosquitoes. Although chikungunya fever is rarely fatal, patients can experience debilitating symptoms that last from months to years. Here we comprehensively assess the global distribution of chikungunya and produce high-resolution maps, using an established modelling framework that combines a comprehensive occurrence database with bespoke environmental correlates, including up-to-date Aedes distribution maps. This enables estimation of the current total population-at-risk of CHIKV transmission and identification of areas where the virus may spread to in the future. We identified 94 countries with good evidence for current CHIKV presence and a set of countries in the New and Old World with potential for future CHIKV establishment, demonstrated by high environmental suitability for transmission and in some cases previous sporadic reports. Aedes aegypti presence was identified as one of the major contributing factors to CHIKV transmission but significant geographical heterogeneity exists. We estimated 1.3 billion people are living in areas at-risk of CHIKV transmission. These maps provide a baseline for identifying areas where prevention and control efforts should be prioritised and can be used to guide estimation of the global burden of CHIKV.
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Affiliation(s)
- E O Nsoesie
- Children's Hospital Informatics Program, Boston Children's Hospital, Boston, United States.,Department of Pediatrics, Harvard Medical School, Boston, United States.,Institute of Health Metrics and Evaluation, University of Washington, Seattle, United States
| | - M U Kraemer
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, United Kingdom
| | - N Golding
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom.,Department of BioScience, University of Melbourne, Australia
| | - D M Pigott
- Institute of Health Metrics and Evaluation, University of Washington, Seattle, United States.,Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom
| | - O J Brady
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom
| | - C L Moyes
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom
| | - M A Johansson
- Centers for Disease Control and Prevention, San Juan, Puerto Rico.,Center for Communicable Disease Dynamics, Harvard T. H. Chan School of Public Health, Boston, United States
| | - P W Gething
- Malaria Atlas Project, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom
| | | | - K Khan
- Li Ka Shing Knowledge Institute, Division of Infectious Diseases, St Michael's Hospital, Toronto, Canada
| | - S I Hay
- Institute of Health Metrics and Evaluation, University of Washington, Seattle, United States.,Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom
| | - J S Brownstein
- Children's Hospital Informatics Program, Boston Children's Hospital, Boston, United States.,Department of Pediatrics, Harvard Medical School, Boston, United States.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
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Jay S, Potter C, Crabtree R, Genovese V, Weiss DJ, Kraft M. Evaluation of modelled net primary production using MODIS and landsat satellite data fusion. CARBON BALANCE AND MANAGEMENT 2016; 11:8. [PMID: 27330549 PMCID: PMC4889621 DOI: 10.1186/s13021-016-0049-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 05/16/2016] [Indexed: 05/10/2023]
Abstract
BACKGROUND To improve estimates of net primary production for terrestrial ecosystems of the continental United States, we evaluated a new image fusion technique to incorporate high resolution Landsat land cover data into a modified version of the CASA ecosystem model. The proportion of each Landsat land cover type within each 0.004 degree resolution CASA pixel was used to influence the ecosystem model result by a pure-pixel interpolation method. RESULTS Seventeen Ameriflux tower flux records spread across the country were combined to evaluate monthly NPP estimates from the modified CASA model. Monthly measured NPP data values plotted against the revised CASA model outputs resulted in an overall R2 of 0.72, mainly due to cropland locations where irrigation and crop rotation were not accounted for by the CASA model. When managed and disturbed locations are removed from the validation, the R2 increases to 0.82. CONCLUSIONS The revised CASA model with pure-pixel interpolated vegetation index performed well at tower sites where vegetation was not manipulated or managed and had not been recently disturbed. Tower locations that showed relatively low correlations with CASA-estimated NPP were regularly disturbed by either human or natural forces.
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Affiliation(s)
- Steven Jay
- Yellowstone Ecological Research Center, 2048 Analysis Dr. Ste. B, Bozeman, MT 59718 USA
| | | | - Robert Crabtree
- Yellowstone Ecological Research Center, 2048 Analysis Dr. Ste. B, Bozeman, MT 59718 USA
| | - Vanessa Genovese
- Science and Environmental Policy, California State University, Monterey Bay, 100 Campus Center, Seaside, CA 93955 USA
| | - Daniel J. Weiss
- Yellowstone Ecological Research Center, 2048 Analysis Dr. Ste. B, Bozeman, MT 59718 USA
- Department of Zoology, University of Oxford, The Tinbergen Building, S Parks Rd, Oxford, OX1 3PS UK
| | - Maggi Kraft
- Yellowstone Ecological Research Center, 2048 Analysis Dr. Ste. B, Bozeman, MT 59718 USA
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Ribeiro AI, Krainski ET, Autran R, Teixeira H, Carvalho MS, de Pina MDF. The influence of socioeconomic, biogeophysical and built environment on old-age survival in a Southern European city. Health Place 2016; 41:100-109. [PMID: 27583526 DOI: 10.1016/j.healthplace.2016.08.008] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Revised: 07/15/2016] [Accepted: 08/09/2016] [Indexed: 10/21/2022]
Abstract
Old-age survival is a good indicator of population health and regional development. We evaluated the spatial distribution of old-age survival across Porto neighbourhoods and its relation with physical (biogeophysical and built) and socioeconomic factors (deprivation). Smoothed survival rates and odds ratio (OR) were estimated using Bayesian spatial models. There were important geographical differentials in the chances of survival after 75 years of age. Socioeconomic deprivation strongly impacted old-age survival (Men: least deprived areas OR=1.31(1.05-1.63); Women OR=1.53(1.24-1.89)), explaining over 40% of the spatial variance. Walkability and biogeophysical environment were unrelated to old-age survival and also unrelated to socioeconomic deprivation, being fairly evenly distributed through the city.
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Affiliation(s)
- Ana Isabel Ribeiro
- EPIUnit-Instituto de Saúde Pública, Universidade do Porto, Portugal; i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Portugal; INEB-Instituto de Engenharia Biomédica, Universidade do Porto, Portugal; Departamento de Epidemiologia Clínica, Medicina Preditiva e Saúde Pública, Faculdade de Medicina, Universidade do Porto, Portugal.
| | - Elias Teixeira Krainski
- Departamento de Estatística, Universidade Federal do Paraná, Curitiba, Brazil; The Norwegian University for Science and Technology, Trondheim, Norway.
| | - Roseanne Autran
- Centro de Investigação em Atividade Física, Saúde e Lazer-Faculdade de Desporto da Universidade do Porto, Portugal.
| | - Hugo Teixeira
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Portugal; INEB-Instituto de Engenharia Biomédica, Universidade do Porto, Portugal.
| | - Marilia Sá Carvalho
- PROCC-Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
| | - Maria de Fátima de Pina
- i3S-Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Portugal; ICICT/FIOCRUZ-Instituto de Comunicação e Informação Científica e Tecnológica em Saúde/Fundação Oswaldo Cruz, Rio de Janeiro, Brazil; CARTO-FEN/UERJ-Departamento de Engenharia Cartográfica, Faculdade de Engenharia da Universidade do Estado do Rio de Janeiro, Brazil.
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Kraemer MUG, Perkins TA, Cummings DAT, Zakar R, Hay SI, Smith DL, Reiner RC. Big city, small world: density, contact rates, and transmission of dengue across Pakistan. J R Soc Interface 2016; 12:20150468. [PMID: 26468065 PMCID: PMC4614486 DOI: 10.1098/rsif.2015.0468] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Macroscopic descriptions of populations commonly assume that encounters between individuals are well mixed; i.e. each individual has an equal chance of coming into contact with any other individual. Relaxing this assumption can be challenging though, due to the difficulty of acquiring detailed knowledge about the non-random nature of encounters. Here, we fitted a mathematical model of dengue virus transmission to spatial time-series data from Pakistan and compared maximum-likelihood estimates of 'mixing parameters' when disaggregating data across an urban-rural gradient. We show that dynamics across this gradient are subject not only to differing transmission intensities but also to differing strengths of nonlinearity due to differences in mixing. Accounting for differences in mobility by incorporating two fine-scale, density-dependent covariate layers eliminates differences in mixing but results in a doubling of the estimated transmission potential of the large urban district of Lahore. We furthermore show that neglecting spatial variation in mixing can lead to substantial underestimates of the level of effort needed to control a pathogen with vaccines or other interventions. We complement this analysis with estimates of the relationships between dengue transmission intensity and other putative environmental drivers thereof.
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Affiliation(s)
- M U G Kraemer
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK
| | - T A Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - D A T Cummings
- Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - R Zakar
- Department of Public Health, University of Punjab, Lahore 54590, Pakistan
| | - S I Hay
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA 98121, USA
| | - D L Smith
- Department of Zoology, University of Oxford, Oxford OX1 3PS, UK Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA Sanaria Institute for Global Health and Tropical Medicine, Rockville, MD 20850, USA
| | - R C Reiner
- Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN 47405, USA
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Shearer FM, Huang Z, Weiss DJ, Wiebe A, Gibson HS, Battle KE, Pigott DM, Brady OJ, Putaporntip C, Jongwutiwes S, Lau YL, Manske M, Amato R, Elyazar IRF, Vythilingam I, Bhatt S, Gething PW, Singh B, Golding N, Hay SI, Moyes CL. Estimating Geographical Variation in the Risk of Zoonotic Plasmodium knowlesi Infection in Countries Eliminating Malaria. PLoS Negl Trop Dis 2016; 10:e0004915. [PMID: 27494405 PMCID: PMC4975412 DOI: 10.1371/journal.pntd.0004915] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 07/20/2016] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Infection by the simian malaria parasite, Plasmodium knowlesi, can lead to severe and fatal disease in humans, and is the most common cause of malaria in parts of Malaysia. Despite being a serious public health concern, the geographical distribution of P. knowlesi malaria risk is poorly understood because the parasite is often misidentified as one of the human malarias. Human cases have been confirmed in at least nine Southeast Asian countries, many of which are making progress towards eliminating the human malarias. Understanding the geographical distribution of P. knowlesi is important for identifying areas where malaria transmission will continue after the human malarias have been eliminated. METHODOLOGY/PRINCIPAL FINDINGS A total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. Predictions were then made throughout Southeast Asia, including regions where infection data are sparse. The resulting map predicts areas of high risk for P. knowlesi infection in a number of countries that are forecast to be malaria-free by 2025 (Malaysia, Cambodia, Thailand and Vietnam) as well as countries projected to be eliminating malaria (Myanmar, Laos, Indonesia and the Philippines). CONCLUSIONS/SIGNIFICANCE We have produced the first map of P. knowlesi malaria risk, at a fine-scale resolution, to identify priority areas for surveillance based on regions with sparse data and high estimated risk. Our map provides an initial evidence base to better understand the spatial distribution of this disease and its potential wider contribution to malaria incidence. Considering malaria elimination goals, areas for prioritised surveillance are identified.
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Affiliation(s)
- Freya M. Shearer
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Zhi Huang
- Malaria Atlas Project, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Daniel J. Weiss
- Malaria Atlas Project, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Antoinette Wiebe
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Harry S. Gibson
- Malaria Atlas Project, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Katherine E. Battle
- Malaria Atlas Project, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - David M. Pigott
- Institute of Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Oliver J. Brady
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Chaturong Putaporntip
- Molecular Biology of Malaria and Opportunistic Parasites Research Unit, Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Somchai Jongwutiwes
- Molecular Biology of Malaria and Opportunistic Parasites Research Unit, Department of Parasitology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Yee Ling Lau
- Department of Parasitology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Magnus Manske
- Wellcome Trust Sanger Institute, Hinxton, United Kingdom
- Medical Research Council (MRC) Centre for Genomics and Global Health, University of Oxford, Oxford, United Kingdom
| | - Roberto Amato
- Wellcome Trust Sanger Institute, Hinxton, United Kingdom
- Medical Research Council (MRC) Centre for Genomics and Global Health, University of Oxford, Oxford, United Kingdom
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | | | - Indra Vythilingam
- Department of Parasitology, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Samir Bhatt
- Malaria Atlas Project, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
| | - Peter W. Gething
- Malaria Atlas Project, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Balbir Singh
- Malaria Research Centre, Universiti Malaysia Sarawak, Kuching, Sarawak, Malaysia
| | - Nick Golding
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Department of BioSciences, University of Melbourne, Parkville, Victoria, Australia
| | - Simon I. Hay
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
- Institute of Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
| | - Catherine L. Moyes
- Spatial Ecology & Epidemiology Group, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
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Perkins TA, Siraj AS, Ruktanonchai CW, Kraemer MUG, Tatem AJ. Model-based projections of Zika virus infections in childbearing women in the Americas. Nat Microbiol 2016; 1:16126. [DOI: 10.1038/nmicrobiol.2016.126] [Citation(s) in RCA: 110] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 06/28/2016] [Indexed: 01/22/2023]
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63
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Evaluating Biosphere Model Estimates of the Start of the Vegetation Active Season in Boreal Forests by Satellite Observations. REMOTE SENSING 2016. [DOI: 10.3390/rs8070580] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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64
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Messina JP, Kraemer MU, Brady OJ, Pigott DM, Shearer FM, Weiss DJ, Golding N, Ruktanonchai CW, Gething PW, Cohn E, Brownstein JS, Khan K, Tatem AJ, Jaenisch T, Murray CJ, Marinho F, Scott TW, Hay SI. Mapping global environmental suitability for Zika virus. eLife 2016; 5. [PMID: 27090089 PMCID: PMC4889326 DOI: 10.7554/elife.15272] [Citation(s) in RCA: 237] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2016] [Accepted: 04/10/2016] [Indexed: 01/07/2023] Open
Abstract
Zika virus was discovered in Uganda in 1947 and is transmitted by Aedes mosquitoes, which also act as vectors for dengue and chikungunya viruses throughout much of the tropical world. In 2007, an outbreak in the Federated States of Micronesia sparked public health concern. In 2013, the virus began to spread across other parts of Oceania and in 2015, a large outbreak in Latin America began in Brazil. Possible associations with microcephaly and Guillain-Barré syndrome observed in this outbreak have raised concerns about continued global spread of Zika virus, prompting its declaration as a Public Health Emergency of International Concern by the World Health Organization. We conducted species distribution modelling to map environmental suitability for Zika. We show a large portion of tropical and sub-tropical regions globally have suitable environmental conditions with over 2.17 billion people inhabiting these areas. DOI:http://dx.doi.org/10.7554/eLife.15272.001 Zika virus is transmitted between humans by mosquitoes. The majority of infections cause mild flu-like symptoms, but neurological complications in adults and infants have been found in recent outbreaks. Although it was discovered in Uganda in 1947, Zika only caused sporadic infections in humans until 2007, when it caused a large outbreak in the Federated States of Micronesia. The virus later spread across Oceania, was first reported in Brazil in 2015 and has since rapidly spread across Latin America. This has led many people to question how far it will continue to spread. There was therefore a need to define the areas where the virus could be transmitted, including the human populations that might be risk in these areas. Messina et al. have now mapped the areas that provide conditions that are highly suitable for the spread of the Zika virus. These areas occur in many tropical and sub-tropical regions around the globe. The largest areas of risk in the Americas lie in Brazil, Colombia and Venezuela. Although Zika has yet to be reported in the USA, a large portion of the southeast region from Texas through to Florida is highly suitable for transmission. Much of sub-Saharan Africa (where several sporadic cases have been reported since the 1950s) also presents an environment that is highly suitable for the Zika virus. While no cases have yet been reported in India, a large portion of the subcontinent is also suitable for Zika transmission. Over 2 billion people live in Zika-suitable areas globally, and in the Americas alone, over 5.4 million births occurred in 2015 within such areas. It is important, however, to recognize that not all individuals living in suitable areas will necessarily be exposed to Zika. We still lack a great deal of basic epidemiological information about Zika. More needs to be known about the species of mosquito that spreads the disease and how the Zika virus interacts with related viruses such as dengue. As such information becomes available and clinical cases become routinely diagnosed, the global evidence base will be strengthened, which will improve the accuracy of future maps. DOI:http://dx.doi.org/10.7554/eLife.15272.002
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Affiliation(s)
- Jane P Messina
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | | | - Oliver J Brady
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - David M Pigott
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.,Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
| | - Freya M Shearer
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Daniel J Weiss
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Nick Golding
- Department of BioSciences, University of Melbourne, Parkville, United Kingdom
| | - Corrine W Ruktanonchai
- WorldPop project, Department of Geography and Environment, University of Southampton, Southampton, United Kingdom
| | - Peter W Gething
- Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Emily Cohn
- Boston Children's Hospital, Harvard Medical School, Boston, United Kingdom
| | - John S Brownstein
- Boston Children's Hospital, Harvard Medical School, Boston, United Kingdom
| | - Kamran Khan
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada.,Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Canada
| | - Andrew J Tatem
- WorldPop project, Department of Geography and Environment, University of Southampton, Southampton, United Kingdom.,Flowminder Foundation, Stockholm, Sweden
| | - Thomas Jaenisch
- Section Clinical Tropical Medicine, Department for Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany.,German Centre for Infection Research (DZIF), Heidelberg partner site, Heidelberg, Germany
| | - Christopher Jl Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
| | - Fatima Marinho
- Secretariat of Health Surveillance, Ministry of Health Brazil, Brasilia, Brazil
| | - Thomas W Scott
- Department of Entomology and Nematology, University of California Davis, Davis, United States
| | - Simon I Hay
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.,Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
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Messina JP, Pigott DM, Golding N, Duda KA, Brownstein JS, Weiss DJ, Gibson H, Robinson TP, Gilbert M, William Wint GR, Nuttall PA, Gething PW, Myers MF, George DB, Hay SI. The global distribution of Crimean-Congo hemorrhagic fever. Trans R Soc Trop Med Hyg 2015; 109:503-13. [PMID: 26142451 PMCID: PMC4501401 DOI: 10.1093/trstmh/trv050] [Citation(s) in RCA: 148] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 05/19/2015] [Accepted: 05/20/2015] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne infection caused by a virus (CCHFV) from the Bunyaviridae family. Domestic and wild vertebrates are asymptomatic reservoirs for the virus, putting animal handlers, slaughter-house workers and agricultural labourers at highest risk in endemic areas, with secondary transmission possible through contact with infected blood and other bodily fluids. Human infection is characterized by severe symptoms that often result in death. While it is known that CCHFV transmission is limited to Africa, Asia and Europe, definitive global extents and risk patterns within these limits have not been well described. METHODS We used an exhaustive database of human CCHF occurrence records and a niche modeling framework to map the global distribution of risk for human CCHF occurrence. RESULTS A greater proportion of shrub or grass land cover was the most important contributor to our model, which predicts highest levels of risk around the Black Sea, Turkey, and some parts of central Asia. Sub-Saharan Africa shows more focalized areas of risk throughout the Sahel and the Cape region. CONCLUSIONS These new risk maps provide a valuable starting point for understanding the zoonotic niche of CCHF, its extent and the risk it poses to humans.
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Affiliation(s)
| | | | - Nick Golding
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - John S Brownstein
- Department of Pediatrics, Harvard Medical School and Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | | | - Harry Gibson
- Department of Zoology, University of Oxford, Oxford, UK
| | - Timothy P Robinson
- Livestock Systems and Environment (LSE), International Livestock Research Institute (ILRI),Nairobi, Kenya
| | - Marius Gilbert
- Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium Fonds National de la Recherche Scientifique, Brussels, Belgium
| | | | | | | | | | - Dylan B George
- Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
| | - Simon I Hay
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
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Kraemer MUG, Sinka ME, Duda KA, Mylne AQN, Shearer FM, Barker CM, Moore CG, Carvalho RG, Coelho GE, Van Bortel W, Hendrickx G, Schaffner F, Elyazar IRF, Teng HJ, Brady OJ, Messina JP, Pigott DM, Scott TW, Smith DL, Wint GRW, Golding N, Hay SI. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. eLife 2015; 4:e08347. [PMID: 26126267 PMCID: PMC4493616 DOI: 10.7554/elife.08347] [Citation(s) in RCA: 1169] [Impact Index Per Article: 129.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2015] [Accepted: 06/18/2015] [Indexed: 02/06/2023] Open
Abstract
Dengue and chikungunya are increasing global public health concerns due to their rapid geographical spread and increasing disease burden. Knowledge of the contemporary distribution of their shared vectors, Aedes aegypti and Aedes albopictus remains incomplete and is complicated by an ongoing range expansion fuelled by increased global trade and travel. Mapping the global distribution of these vectors and the geographical determinants of their ranges is essential for public health planning. Here we compile the largest contemporary database for both species and pair it with relevant environmental variables predicting their global distribution. We show Aedes distributions to be the widest ever recorded; now extensive in all continents, including North America and Europe. These maps will help define the spatial limits of current autochthonous transmission of dengue and chikungunya viruses. It is only with this kind of rigorous entomological baseline that we can hope to project future health impacts of these viruses.
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Affiliation(s)
- Moritz UG Kraemer
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Marianne E Sinka
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Kirsten A Duda
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Adrian QN Mylne
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Freya M Shearer
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Christopher M Barker
- Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, Davis, United States
| | - Chester G Moore
- Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, United States
| | | | | | - Wim Van Bortel
- European Centre for Disease Prevention and Control, Stockholm, Sweden
| | | | | | | | - Hwa-Jen Teng
- Center for Research, Diagnostics and Vaccine Development, Centers for Disease Control, Taipei, Taiwan
| | - Oliver J Brady
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Jane P Messina
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - David M Pigott
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Thomas W Scott
- Fogarty International Center, National Institutes of Health, Bethesda, United States
- Department of Entomology and Nematology, University of California, Davis, Davis, United States
| | - David L Smith
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethesda, United States
- Sanaria Institute for Global Health and Tropical Medicine, Rockville, United States
| | - GR William Wint
- Environmental Research Group Oxford, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Nick Golding
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Simon I Hay
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethesda, United States
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, United States
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Mylne AQN, Pigott DM, Longbottom J, Shearer F, Duda KA, Messina JP, Weiss DJ, Moyes CL, Golding N, Hay SI. Mapping the zoonotic niche of Lassa fever in Africa. Trans R Soc Trop Med Hyg 2015; 109:483-92. [PMID: 26085474 PMCID: PMC4501400 DOI: 10.1093/trstmh/trv047] [Citation(s) in RCA: 93] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Accepted: 05/29/2015] [Indexed: 02/05/2023] Open
Abstract
Background Lassa fever is a viral haemorrhagic illness responsible for disease outbreaks across West Africa. It is a zoonosis, with the primary reservoir species identified as the Natal multimammate mouse, Mastomys natalensis. The host is distributed across sub-Saharan Africa while the virus' range appears to be restricted to West Africa. The majority of infections result from interactions between the animal reservoir and human populations, although secondary transmission between humans can occur, particularly in hospital settings. Methods Using a species distribution model, the locations of confirmed human and animal infections with Lassa virus (LASV) were used to generate a probabilistic surface of zoonotic transmission potential across sub-Saharan Africa. Results Our results predict that 37.7 million people in 14 countries, across much of West Africa, live in areas where conditions are suitable for zoonotic transmission of LASV. Four of these countries, where at-risk populations are predicted, have yet to report any cases of Lassa fever. Conclusions These maps act as a spatial guide for future surveillance activities to better characterise the geographical distribution of the disease and understand the anthropological, virological and zoological interactions necessary for viral transmission. Combining this zoonotic niche map with detailed patient travel histories can aid differential diagnoses of febrile illnesses, enabling a more rapid response in providing care and reducing the risk of onward transmission.
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Affiliation(s)
- Adrian Q N Mylne
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | - Joshua Longbottom
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Freya Shearer
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | | | | | | | - Catherine L Moyes
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Nick Golding
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Simon I Hay
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK Institute for Health Metrics and Evaluation, University of Washington, Seattle, USA Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA
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Pigott DM, Golding N, Mylne A, Huang Z, Weiss DJ, Brady OJ, Kraemer MUG, Hay SI. Mapping the zoonotic niche of Marburg virus disease in Africa. Trans R Soc Trop Med Hyg 2015; 109:366-78. [PMID: 25820266 PMCID: PMC4447827 DOI: 10.1093/trstmh/trv024] [Citation(s) in RCA: 78] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2014] [Accepted: 02/23/2015] [Indexed: 11/12/2022] Open
Abstract
Background Marburg virus disease (MVD) describes a viral haemorrhagic fever responsible for a number of outbreaks across eastern and southern Africa. It is a zoonotic disease, with the Egyptian rousette (Rousettus aegyptiacus) identified as a reservoir host. Infection is suspected to result from contact between this reservoir and human populations, with occasional secondary human-to-human transmission. Methods Index cases of previous human outbreaks were identified and reports of infection in animals recorded. These data were modelled within a species distribution modelling framework in order to generate a probabilistic surface of zoonotic transmission potential of MVD across sub-Saharan Africa. Results Areas suitable for zoonotic transmission of MVD are predicted in 27 countries inhabited by 105 million people. Regions are suggested for exploratory surveys to better characterise the geographical distribution of the disease, as well as for directing efforts to communicate the risk of practices enhancing zoonotic contact. Conclusions These maps can inform future contingency and preparedness strategies for MVD control, especially where secondary transmission is a risk. Coupling this risk map with patient travel histories could be used to guide the differential diagnosis of highly transmissible pathogens, enabling more rapid response to outbreaks of haemorrhagic fever.
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Affiliation(s)
- David M Pigott
- Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK
| | - Nick Golding
- Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK
| | - Adrian Mylne
- Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK
| | - Zhi Huang
- Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK
| | - Daniel J Weiss
- Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK
| | - Oliver J Brady
- Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK
| | - Moritz U G Kraemer
- Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK
| | - Simon I Hay
- Spatial Ecology & Epidemiology Group, Department of Zoology, University of Oxford, Oxford, UK Fogarty International Center, National Institutes of Health, Bethesda, Maryland, USA
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Weiss DJ, Mappin B, Dalrymple U, Bhatt S, Cameron E, Hay SI, Gething PW. Re-examining environmental correlates of Plasmodium falciparum malaria endemicity: a data-intensive variable selection approach. Malar J 2015; 14:68. [PMID: 25890035 PMCID: PMC4333887 DOI: 10.1186/s12936-015-0574-x] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 01/18/2015] [Indexed: 11/14/2022] Open
Abstract
Background Malaria risk maps play an increasingly important role in disease control planning, implementation, and evaluation. The construction of these maps using modern geospatial techniques relies on covariate grids: continuous surfaces quantifying environmental factors that partially explain spatial heterogeneity in malaria endemicity. Although crucial, past variable selection processes for this purpose have often been subjective and ad-hoc, with many covariates used in modeling with little quantitative justification. Methods This research consists of an extensive covariate construction and selection process for predicting Plasmodium falciparum parasite rates (PfPR) in Africa for years 2000-2012. First, a literature review was conducted to establish a comprehensive list of covariates used for malaria mapping. Second, a library of covariate data was assembled to reflect this list, a process that included the construction of multiple, temporally dynamic datasets. Third, the resulting set of covariates was leveraged to create more than 50 million possible covariate terms via factorial combinations of different spatial and temporal aggregations, transformations, and pairwise interactions. Fourth, the expanded set of covariates was reduced via successive selection criteria to yield a robust covariate subset that was assessed using an out-of-sample validation approach. Results The final covariate subset included predominately dynamic covariates and it substantially out-performed earlier sets used by the Malaria Atlas Project (MAP) for creating global malaria risk maps, with the pseudo-R2 value for the out-of-sample validation increasing from 0.43 to 0.52. Dynamic covariates improved the model, with 17 of the 20 new covariates consisting of monthly or annual products, but the selected covariates were typically interaction terms that included both dynamic and synoptic datasets. Thus the interplay between normal (i.e., long-term averages) and immediate conditions may be key for characterizing environmental controls on parasite rate. Conclusions This analysis represents the first effort to systematically audit covariate utility for malaria mapping and then derive an objective, empirically based set of environmental covariates for modeling PfPR. The new covariates produce more reliable representations of malaria risk patterns and how they are changing through time, and these covariates will be used to characterize spatially and temporally varying environmental conditions affecting PfPR within a geostatistical-modeling framework, thus building upon previous research by MAP that produced global malaria maps for 2007 and 2010. Electronic supplementary material The online version of this article (doi:10.1186/s12936-015-0574-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Daniel J Weiss
- Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, Oxford, UK.
| | - Bonnie Mappin
- Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, Oxford, UK.
| | - Ursula Dalrymple
- Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, Oxford, UK.
| | - Samir Bhatt
- Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, Oxford, UK.
| | - Ewan Cameron
- Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, Oxford, UK.
| | - Simon I Hay
- Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, Oxford, UK. .,Fogarty International Center, National Institutes of Health, Bethesda, MD, USA.
| | - Peter W Gething
- Spatial Ecology and Epidemiology Group, Tinbergen Building, Department of Zoology, University of Oxford, Oxford, UK.
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70
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Hollm-Delgado MG, Piel FB, Weiss DJ, Howes RE, Stuart EA, Hay SI, Black RE. Vitamin A supplements, routine immunization, and the subsequent risk of Plasmodium infection among children under 5 years in sub-Saharan Africa. eLife 2015; 4:e03925. [PMID: 25647726 PMCID: PMC4383226 DOI: 10.7554/elife.03925] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Accepted: 01/08/2015] [Indexed: 12/23/2022] Open
Abstract
Recent studies, partly based on murine models, suggest childhood immunization and
vitamin A supplements may confer protection against malaria infection, although
strong evidence to support these theories in humans has so far been lacking. We
analyzed national survey data from children aged 6–59 months in four
sub-Saharan African countries over an 18-month time period, to determine the risk of
Plasmodium spp. parasitemia (n=8390) and Plasmodium
falciparum HRP-2 (PfHRP-2)-related antigenemia
(n=6121) following vitamin A supplementation and standard vaccination. Bacille
Calmette Guerin-vaccinated children were more likely to be PfHRP-2
positive (relative risk [RR]=4.06, 95% confidence interval
[CI]=2.00–8.28). No association was identified with parasitemia. Measles
and polio vaccination were not associated with malaria. Children receiving vitamin A
were less likely to present with parasitemia (RR=0.46, 95%
CI=0.39–0.54) and antigenemia (RR=0.23, 95%
CI=0.17–0.29). Future studies focusing on climate seasonality, placental
malaria and HIV are needed to characterize better the association between vitamin A
and malaria infection in different settings. DOI:http://dx.doi.org/10.7554/eLife.03925.001 More than half of the world's population is at risk of malaria, with an estimated 198
million clinical cases each year. A vaccine that fully prevents it has not yet been
discovered. Most cases of malaria occur among children living in sub-Saharan Africa,
a region where many receive routine vaccinations designed to prevent other diseases;
for example, 75% of children in sub-Saharan Africa receive measles vaccines. Many
also receive vitamin A supplements, which have been linked not only to the protection
of a child's vision, but also to a lower risk of death and an improved ability to
fight off infections. Some researchers have suggested that vitamin A supplements and routine childhood
vaccinations for other diseases may also provide some protection against malaria. For
example, some studies performed in mice have shown that a commonly used tuberculosis
vaccine may eliminate Plasmodium parasites that cause malaria
infections. However, this effect depended on several factors, including how the
vaccine was administered and whether the vaccination was given before or after the
mouse developed malaria. It is less clear whether vaccines or vitamin A have antimalarial effects in humans.
To address this, Hollm-Delgado et al. analyzed national survey data collected from
thousands of children aged between 6 months and 5 years old who lived in four
different countries in sub-Saharan Africa. The surveys contained information about
the vaccines and supplements the children received, and whether their blood showed
signs of infection with malaria-causing Plasmodium parasites. Hollm-Delgado et al. found that routine vaccinations did not affect the likelihood of
malaria parasites being detected in the child's blood. However, children vaccinated
against tuberculosis were more likely to have a specific type of protein released
when malaria infects the blood. Hollm-Delgado et al. suspect that the tests may
actually have inadvertently detected other parasitic infections in the children, such
as Schistosoma, producing false-positive results for malaria. In contrast, Hollm-Delgado et al. found that children who received vitamin A
supplements were less likely to become infected with malaria. The benefits of the
supplements appeared to be affected by several conditions, including the time of year
when the children received their supplements or when they were tested for malaria,
and whether their mother had malaria when pregnant. Clinical trials are now needed to
confirm these results and investigate how effectively vitamin A prevents malaria. DOI:http://dx.doi.org/10.7554/eLife.03925.002
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Affiliation(s)
- Maria-Graciela Hollm-Delgado
- Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States
| | - Frédéric B Piel
- Evolutionary Ecology of Infectious Disease Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Daniel J Weiss
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Rosalind E Howes
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Elizabeth A Stuart
- Departments of Mental Health and Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States
| | - Simon I Hay
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Robert E Black
- Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, United States
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71
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Pigott DM, Golding N, Mylne A, Huang Z, Henry AJ, Weiss DJ, Brady OJ, Kraemer MUG, Smith DL, Moyes CL, Bhatt S, Gething PW, Horby PW, Bogoch II, Brownstein JS, Mekaru SR, Tatem AJ, Khan K, Hay SI. Mapping the zoonotic niche of Ebola virus disease in Africa. eLife 2014; 3:e04395. [PMID: 25201877 PMCID: PMC4166725 DOI: 10.7554/elife.04395] [Citation(s) in RCA: 243] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2014] [Accepted: 08/31/2014] [Indexed: 11/17/2022] Open
Abstract
Ebola virus disease (EVD) is a complex zoonosis that is highly virulent in humans. The largest recorded outbreak of EVD is ongoing in West Africa, outside of its previously reported and predicted niche. We assembled location data on all recorded zoonotic transmission to humans and Ebola virus infection in bats and primates (1976-2014). Using species distribution models, these occurrence data were paired with environmental covariates to predict a zoonotic transmission niche covering 22 countries across Central and West Africa. Vegetation, elevation, temperature, evapotranspiration, and suspected reservoir bat distributions define this relationship. At-risk areas are inhabited by 22 million people; however, the rarity of human outbreaks emphasises the very low probability of transmission to humans. Increasing population sizes and international connectivity by air since the first detection of EVD in 1976 suggest that the dynamics of human-to-human secondary transmission in contemporary outbreaks will be very different to those of the past.
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Affiliation(s)
- David M Pigott
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Nick Golding
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Adrian Mylne
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Zhi Huang
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Andrew J Henry
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Daniel J Weiss
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Oliver J Brady
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Moritz UG Kraemer
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - David L Smith
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
- Sanaria Institute for Global Health and Tropical Medicine, Rockville, United States
| | - Catherine L Moyes
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Samir Bhatt
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Peter W Gething
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
| | - Peter W Horby
- Epidemic Diseases Research Group, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
| | - Isaac I Bogoch
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
- Divisions of Internal Medicine and Infectious Diseases, University Health Network, Toronto, Toronto, Canada
| | - John S Brownstein
- Department of Pediatrics, Harvard Medical School, Boston, United States
- Children's Hospital Informatics Program, Boston Children's Hospital, Boston, United States
| | - Sumiko R Mekaru
- Children's Hospital Informatics Program, Boston Children's Hospital, Boston, United States
| | - Andrew J Tatem
- Department of Geography and Environment, University of Southampton, Southampton, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethesda, United States
- Flowminder Foundation, Stockholm, Sweden
| | - Kamran Khan
- Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Simon I Hay
- Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom
- Fogarty International Center, National Institutes of Health, Bethseda, United States
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