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将越来越高， 这将增加全球感染性腹泻疾病的发病率和死亡率。城市高度贫困 缺乏污水基础设施的低收入国家(LIC)社区可能会经历最糟糕的情况 气候驱动的腹泻暴发的影响。贫民窟和非正式住区经常出现，而且有利于 人们期待已久的季节性洪水每年都会泛滥，使排水沟、露天排泄点和化粪池泛滥，蔓延开来 跨社区的带有腹泻病原体的排泄物。气候变化可能会增加这种频率 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的位置、严重程度和持续时间的科学在一定程度上仍然有限。 因为这些洪水更多地是由人为的景观改造和小水体驱动的，而不是 而不是与大型湖泊、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、绿地和人口方面的范围和质量都非常不同- 蒸气密度。多个贫民窟和中产阶级社区，对洪水的脆弱性程度非常不同- ING可能交织在一个10平方公里的小空间内，这意味着像这样的综合衡量标准 城市日降雨量可能不是整个城市社区洪水暴露的一致指标- 领带。腹泻疾病传播模型的有效性，如PATHOME研究(TW011795) 如果模型考虑到差异，肯尼亚在建议疾病预防干预措施方面将得到改进 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了 可由开源水文气象模型分析以绘制地图的高分辨率空间气候数据 小范围的洪水事件。这项补充建议的目标是生成特定于社区的指标- 中国贫民窟和中产阶级居民区一年内降水和洪水灾害的影响因素 肯尼亚内罗毕，参加母公司PATHOME研究。我们的目标是生成对沉淀的本地化估计- 以及我们研究社区洪水的发生、强度和持续时间，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 不同社区的降水和洪水是否有所不同。这些数据将整合到我们的 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的更广泛的数据集 每一个社区。我们假设卫星数据将揭示洪水暴露频率的差异- 在城市社区之间穿梭。本补编将由PATHOME PIS和一名专家执行 在气候和城市洪水水文气象模拟方面，将有助于培训一名国际 LIC的研究生。这项研究的更广泛影响包括展示地球数据的新用途 在城市LIC环境中调查气候驱动的传染病。

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.