Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia

被引:0
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作者
Amir S. Siraj
Isabel Rodriguez-Barraquer
Christopher M. Barker
Natalia Tejedor-Garavito
Dennis Harding
Christopher Lorton
Dejan Lukacevic
Gene Oates
Guido Espana
Moritz U.G. Kraemer
Carrie Manore
Michael A. Johansson
Andrew J. Tatem
Robert C. Reiner
T. Alex Perkins
机构
[1] University of Notre Dame,Department of Biological Sciences and Eck Institute for Global Health
[2] Johns Hopkins Bloomberg School of Public Health,Department of Epidemiology
[3] Microbiology and Immunology,Department of Pathology
[4] University of California,Department of Geography and Environment
[5] WorldPop,Department of Zoology
[6] University of Southampton,Department of Global Health and Institute for Health Metrics and Evaluation
[7] Flowminder Foundation,undefined
[8] Institute for Disease Modeling,undefined
[9] Spatial Ecology and Epidemiology Group,undefined
[10] University of Oxford,undefined
[11] Harvard Medical School,undefined
[12] Boston Children’s Hospital,undefined
[13] Theoretical Biology and Biophysics Group,undefined
[14] Los Alamos National Laboratory,undefined
[15] Centers for Disease Control and Prevention,undefined
[16] Center for Communicable Disease Dynamics,undefined
[17] Harvard T.H. Chan School of Public Health,undefined
[18] University of Washington,undefined
来源
Scientific Data | / 5卷
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摘要
Despite a long history of mosquito-borne virus epidemics in the Americas, the impact of the Zika virus (ZIKV) epidemic of 2015–2016 was unexpected. The need for scientifically informed decision-making is driving research to understand the emergence and spread of ZIKV. To support that research, we assembled a data set of key covariates for modeling ZIKV transmission dynamics in Colombia, where ZIKV transmission was widespread and the government made incidence data publically available. On a weekly basis between January 1, 2014 and October 1, 2016 at three administrative levels, we collated spatiotemporal Zika incidence data, nine environmental variables, and demographic data into a single downloadable database. These new datasets and those we identified, processed, and assembled at comparable spatial and temporal resolutions will save future researchers considerable time and effort in performing these data processing steps, enabling them to focus instead on extracting epidemiological insights from this important data set. Similar approaches could prove useful for filling data gaps to enable epidemiological analyses of future disease emergence events.
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