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

气候变化模型预测，全球干旱、极端降雨和洪水的频率越来越高， 这将增加全球传染病的发病率和死亡率。城市，高度贫困 低收入国家（LIC）缺乏废水基础设施的社区可能会经历最糟糕的情况 气候驱动的腹泻爆发的影响。贫民窟和非正规住区经常发生亲 每年长时间的季节性洪水，溢出排水沟，露天排便场所和化粪池，蔓延 粪便中携带的病原体。气候变化可能会增加 这些洪水的严重程度，以及LIC易受水传播疾病影响的人口数量 城市预测城市景观中洪水位置、严重程度和持续时间的科学仍然有限，部分原因是 因为这些洪水更多地是由人为的景观改造和小水体造成的， 与大型湖泊、河流和海洋相关的气象系统。此外，城市景观 在基础设施发展、绿色空间和人口方面， 密度。多个贫民窟和中产阶级社区，对洪水的脆弱程度各不相同- ing可能交织在一个小的10平方公里的空间内，这表明聚合的措施，如 每日城市降水量可能不是整个城市社区洪水暴露的一致指标， 关系的研究表明，疟疾传播模型的有效性，如PATHOME研究（TW 011795）， 肯尼亚，在建议疾病预防干预措施时，如果模型考虑到方差， 在小空间尺度上的洪水暴露风险。现在在地球轨道上的多颗卫星产生 高分辨率的空间气候数据，可以通过开源水文气象模型进行分析， 小规模的洪水事件。这一补充提案的目标是产生社区特定的indi- 在一个贫民窟和中产阶级社区的一年时间里， 内罗毕，肯尼亚，参与母PATHOME研究。我们的目标是产生局部的降水估计- 站和洪水的发生，强度和持续时间在我们的研究社区，使用基于网络的 开源洪水建模平台和高分辨率（<1公里）卫星数据。然后我们将测试 降水和洪水是否在社区之间存在差异。这些数据将被整合到我们的 更广泛的数据集，用于模拟洪水事件对观察到的肠道病原体传播动力学的作用， 每个邻居。我们假设卫星数据将揭示洪水暴露频率的差异- 在城市街区之间。该补充将由病理学PI和一名专家实施 在气候和城市洪水水文气象建模，并将有助于培训一个国际 毕业于LIC。这项研究的更广泛影响包括展示地球数据的新用途， 调查城市低收入国家环境中气候驱动的传染病。

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.