Statistical and agent-based modeling of complex microbial systems: a means for understanding enteric disease transmission among children in urban neighborhoods of Kenya

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Climate change models predict an increasing frequency of droughts, extreme rainfall, and floods globally, which will increase global morbidity and mortality from infectious diarrheal diseases. Urban, high-poverty neighborhoods in low-income countries (LICs) that lack wastewater infrastructure may experience the worst impacts of climate-driven diarrhea outbreaks. Slums and informal settlements experience frequent and pro- longed seasonal flooding each year that overflows drains, open defecation sites, and septage pits, spreading fecal waste with diarrheal pathogens across communities. Climate change is poised to increase the frequency and severity of these floods, and the number of people in a population vulnerable to waterborne disease in LIC cities. The science of predicting flood location, severity, and duration in urban landscapes is still limited, in part because these floods are driven more by manmade landscape modification and small bodies of water, rather than meteorological systems associated with large lakes, rivers, and oceans. Additionally, urban landscapes are extraordinarily heterogeneous in extent and quality of infrastructure development, green space, and popu- lation density. Multiple slums and middle-class neighborhoods with very different levels of vulnerability to flood- ing may be interwoven within one small 10 kilometer squared space, suggesting aggregated measures like daily municipal precipitation may not be consistent indicators of floodwater exposure across urban communi- ties. The effectiveness of diarrheal disease transmission models, like the PATHOME Study (TW011795) in Kenya, in recommending disease prevention interventions would be improved if models accounted for variance in floodwater exposure risk at small spatial scales. Multiple satellites now in orbit around the earth generate fine-resolution spatial climate data that can be analyzed by open source hydrometeorological models to map flood events at small scales. The goal of this supplemental proposal is to generate neighborhood-specific indi- cators of precipitation and flood hazards during one year of time in a slum and middle-class neighborhood in Nairobi, Kenya participating in the parent PATHOME Study. We aim to generate localized estimates of precipi- tation and the occurrence, intensity, and duration of flooding in our study neighborhoods, using web-based open source flood modeling platforms and fine-resolution (<1 kilometer) satellite data. Then we will test whether precipitation and flooding differs between neighborhoods. These data will be integrated into our broader datasets for modeling the role of flood events on observed enteric pathogen transmission dynamics in each neighborhood. We hypothesize that satellite data will reveal differences in frequency of exposure to flood- ing between urban neighborhoods. This supplement will be implemented by the PATHOME PIs and an expert in climate and urban flood hydrometeorological modeling, and will contribute to the training of an international graduate student from a LIC. Broader impacts of this study include demonstrating new uses for earth data to investigate climate-driven infectious disease in urban LIC settings.