Schistosomiasis, like many other neglected tropical diseases, has strong associations with dynamic climactic, ecological, hydrological and other environmental phenomena, raising an important opportunity for public health decision-making. Because disease persistence and establishment are highly dependent on environmental phenomena, spatial and temporal environmental datasets have the potential to inform public health actions, such as where and when to focus surveillance efforts. This application provides multiple modes of training to the applicant in advanced numerical and statistical methods, applied to the optimization of schistosomiasis surveillance in the presence of environmental heterogeneity in Sichuan Province, China. Surveillance for Schistosoma parasites in China is currently guided by analytical models with key limitations, including simplistic, isotropic spatial functions that perform poorly for environmentally-mediated organisms, and crude phenomenological representations of environmental processes.
The specific aims of this proposal are: 1) to assemble a world-class Schistosoma japonicum epidemiological dataset, combining surveillance and research data into a cohesive, longitudinal database;2) to quantitatively attribute the effects of multiple environmental drivers at varying scales on dynamic Schistosoma outcomes using novel statistical and mathematical approaches, including spatially explicit, graph-theoretic models and time-series approaches allowing for transient coupling;and 3) to optimize Schistosoma surveillance campaigns in Sichuan using models developed in Aim 2, evaluating predictions using historical and contemporary data. The career development and research activities proposed in this application will lead to a more rigorous quantification of environmental drivers of schistosomiasis, more accurate modeling of the spatial and temporal dimensions of risk, and improved selection of surveillance sites and survey timing. The resulting techniques will be generalized for use in other systems where they can be applied to decision-making support in the face of environmental change. The research builds on the candidate's foundation of skills, leveraging existing data and knowledge to support his transition to a productive independent investigator. PUBLIC HEALTH RELEVENCE: Human parasites like schistosomes are known to be highly sensitive to environmental factors. Understanding how these parasites respond to changes in temperature, rainfall and vegetation can be used to inform public health decision-making, such as where and when to focus surveillance for disease outbreaks. The proposed study will be the first to investigate how environmental information can be used to improve public health activities to prevent new parasite infections.

Public Health Relevance

Human parasites like schistosomes are known to be highly sensitive to environmental factors. Understanding how these parasites respond to changes in temperature, rainfall and vegetation can be used to inform public health decision-making, such as where and when to focus surveillance for disease outbreaks. The proposed study will be the first to investigate how environmental information can be used to improve public health activities that to prevent new parasite infections.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Scientist Development Award - Research & Training (K01)
Project #
5K01AI091864-04
Application #
8604361
Study Section
Microbiology and Infectious Diseases Research Committee (MID)
Program Officer
Rao, Malla R
Project Start
2011-02-01
Project End
2016-01-31
Budget Start
2014-02-01
Budget End
2015-01-31
Support Year
4
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Emory University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
City
Atlanta
State
GA
Country
United States
Zip Code
30322
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Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H et al. (2014) Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling. PLoS One 9:e103163

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