The inevitability of global climate change has led to speculation regarding its effects on the incidence and distribution of infectious disease risk. This important topic has received well-deserved attention in the form of hypothesis-driven research aimed at untangling the relationships between climate and infectious disease, usually focusing on one disease in a specific population or geographic region. However, no study has systematically examined the impact of climate change on the global burden of infectious diseases. Though a comprehensive approach is ambitious, informatics tools, statistical methods and computing capacity now exist to begin to address this challenge quickly and efficiently. We propose to model global relationships between climate and disease risk and to use these models to project future effects of climate change on infectious disease risk. By coupling new technologies for automated, global disease outbreak detection with existing climate data we will comprehensively assess climate-disease relationships on a global scale. Leveraging our previous experience in climate-based disease modeling and our development of HealthMap.org, a leading global infectious disease surveillance system, we will explore an informatics approach to investigating the predictive relationships between climate and a number of infectious diseases. The goals of our proposed research are to broadly characterize the global effects of climate on infectious disease risk and burden and to evaluate the resulting models under future climate change scenarios to project changes in global disease risk due to climate change. First, we plan to validate the use of event-based infectious disease surveillance data sources for tracking spatiotemporal trends in global infectious disease risk and burden. Building on our previous efforts in identifying informal sources for outbreak surveillance, we will assess the reliability of these sources for analyses of large-scale, long-term epidemiological patterns. Second, using this validated data, we will identify the diseases most sensitive to climate and build disease-specific predictive spatiotemporal models of relationships between climate and infectious disease risk and burden. Finally, we will leverage existing climate change forecasts to evaluate our models under various climate change scenarios. Coupled with global population growth projections, these models can be used to predict changes in populations at risk as a consequence of climate change. Guided by our advisory group including partners at the WHO, Fogarty International, Red Cross/Red Crescent Climate Center, and NASA, we plan to frame our results in a policy- relevant manner that will inform ongoing international surveillance and impact assessment efforts.
While the inevitability of global climate change has led to speculation regarding its effects on the incidence and distribution of infectious disease risk, no study has systematically examined the impact of climate change on the global burden of infectious diseases. We propose to couple new disease surveillance technologies and advanced statistical methodologies with global climate data in order to broadly characterize and analyze the global effects of climate on infectious disease risk and burden. Our goal is to project the likely changes in global disease risk due to climate change and to inform ongoing surveillance and impact assessments efforts of international organizations, such as the World Health Organization and the Red Cross.
|Kraemer, Moritz U G; Faria, Nuno R; Reiner Jr, Robert C et al. (2017) Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015-16: a modelling study. Lancet Infect Dis 17:330-338|
|Yang, Shihao; Santillana, Mauricio; Brownstein, John S et al. (2017) Using electronic health records and Internet search information for accurate influenza forecasting. BMC Infect Dis 17:332|
|Shearer, Freya M; Moyes, Catherine L; Pigott, David M et al. (2017) Global yellow fever vaccination coverage from 1970 to 2016: an adjusted retrospective analysis. Lancet Infect Dis 17:1209-1217|
|Majumder, Maimuna S; Santillana, Mauricio; Mekaru, Sumiko R et al. (2016) Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak. JMIR Public Health Surveill 2:e30|
|Nsoesie, E O; Kraemer, M U; Golding, N et al. (2016) Global distribution and environmental suitability for chikungunya virus, 1952 to 2015. Euro Surveill 21:|
|Kluberg, Sheryl A; Mekaru, Sumiko R; McIver, David J et al. (2016) Global Capacity for Emerging Infectious Disease Detection, 1996-2014. Emerg Infect Dis 22:E1-6|
|Bogoch, Isaac I; Brady, Oliver J; Kraemer, Moritz U G et al. (2016) Anticipating the international spread of Zika virus from Brazil. Lancet 387:335-336|
|Hawkins, Jared B; Brownstein, John S; Tuli, Gaurav et al. (2016) Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Qual Saf 25:404-13|
|Johansson, Michael A; Reich, Nicholas G; Hota, Aditi et al. (2016) Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Sci Rep 6:33707|
|Messina, Jane P; Kraemer, Moritz Ug; Brady, Oliver J et al. (2016) Mapping global environmental suitability for Zika virus. Elife 5:|
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