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

复杂微生物系统的统计和基于主体的建模：了解肯尼亚城市社区儿童肠道疾病传播的一种手段

• 批准号: 10671983
• 负责人: Kelly K Baker
• 金额: $ 10.95万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10671983
• 关键词: Administrative Supplement; Area; Bangladesh; Behavior; Cessation of life; Child; Childhood; Cities; Climate; Code; Communicable Diseases; Communities; Complex; Data; Data Collection; Data Set; Data Sources; Defecation; Development; Diarrhea; Disasters; Disease; Disease Outbreaks; Drainage procedure; Droughts; Dysentery; Effectiveness; Enteral; Environmental Pollution; Event; Exposure to; Felis catus; Floods; Frequencies; Future; Goals; Green space; Health; Hour; Human; Income; Infectious Diseases Research; Infrastructure; International; Intervention; Kenya; Location; Maps; Measures; Meteorology; Microclimate; Modeling; Modification; Morbidity - disease rate; Municipalities; Nature; Neighborhoods; Nigerian; Oceans; Ocular orbit; Online Systems; Parents; Pattern; Persons; Planet Earth; Population Density; Poverty; Precipitation; Public Health; Rain; Research; Resolution; Risk; Rivers; Running; Science; Seasons; Severities; Site; Slum; Standardization; Sum; Surface; System; Testing; Time; TimeLine; Training; Unit of Measure; Urban Community; Urban Developments; Vector-transmitted infectious disease; Vulnerable Populations; Water; Work; base; burden of illness; climate change; climate data; climate impact; climate-related health; diarrheal disease; disease transmission; disorder prevention; enteric infection; enteric pathogen; experience; graduate student; hazard; improved; infrastructure development; low income country; microbial; mortality; non-Gaussian model; open source; pathogen; predictive modeling; prevent; preventive intervention; role model; student training; transmission process; wasting; waterborne; waterborne illness; web site

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.

气候变化模型预测全球干旱、极端降雨和洪水的频率不断增加， 这将增加全球感染性腹泻病的发病率和死亡率。城市、高度贫困 缺乏废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。贫民窟和非正规住区经常发生和亲 每年长期发生的季节性洪水会溢出排水沟、露天排便场和化粪池，蔓延开来 社区内含有腹泻病原体的粪便废物。气候变化将增加频率 这些洪水的严重程度，以及低收入国家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造景观改造和小水体驱动的，而不是 与大型湖泊、河流和海洋相关的气象系统相比。此外，城市景观 基础设施发展、绿色空间和人口的范围和质量极其参差不齐 关系密度。多个贫民窟和中产阶级社区对洪水的脆弱程度差异很大 可能会交织在一个 10 平方公里的小空间内，建议采取聚合措施，例如 每日城市降水量可能并不能作为整个城市社区洪水暴露的一致指标。 关系。腹泻病传播模型的有效性，例如 PATHOME 研究 (TW011795) 肯尼亚建议，如果模型考虑到方差，疾病预防干预措施将会得到改善 小空间尺度的洪水暴露风险。目前，多颗卫星在绕地球轨道运行，产生 精细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以绘制地图 小规模的洪水事件。该补充提案的目标是生成针对特定社区的个人信息 一年内贫民窟和中产阶级社区降水和洪水灾害的因素 肯尼亚内罗毕参加家长 PATHOME 研究。我们的目标是生成局部的降水量估计 使用基于网络的数据来了解我们研究社区的洪水发生情况、强度和持续时间 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 社区之间的降水量和洪水是否不同。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态的作用 每个街区。我们假设卫星数据将揭示遭受洪水的频率差异 城市街区之间。该补充将由 PATHOME PI 和专家实施 气候和城市洪水水文气象建模，并将有助于培训国际 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 调查城市低收入国家环境中气候驱动的传染病。