Statistical and agent-based modeling of complex microbial systems: a means for..

复杂微生物系统的统计和基于代理的建模：一种手段......

• 批准号: 10399592
• 负责人: Kelly K Baker
• 金额: $ 50万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10399592
• 关键词: Accounting; Address; Affect; Animals; Area; Behavior; Behavioral; Biological Assay; Caregivers; Cattle; Characteristics; Child; Communicable Diseases; Communities; Complex; Computer Models; Computer software; Computing Methodologies; Country; Data; Defecation; Demographer; Detection; Developing Countries; Development; Diarrhea; Diffusion; Disease; Disease Pathway; Domestic Animals; Economic Development; Economics; Ecosystem; Enteral; Environment; Epidemiologist; Evolution; Exposure to; Failure; Feces; Floor; Frequencies; Geography; Household; Human; Incidence; Income; Individual; Infant; Infection; Ingestion; Intervention; Joints; Kenya; Latrine; Lead; Long-Term Effects; Low income; Mathematics; Measures; Methods; Microbiology; Milk; Modeling; Morbidity - disease rate; Movement; Neighborhoods; Pathogenicity; Pathway interactions; Pattern; Periodicity; Policies; Population Attributable Risks; Positioning Attribute; Prevalence; Process; Research; Research Personnel; Risk; Sanitation; Scientist; Seasons; Site; Social Development; Source; Statistical Methods; Statistical Models; Structure; Surveys; System; Taxonomy; Testing; Training; Urban Community; Urban Developments; Viral; Water; Work; base; burden of illness; design; disease transmission; dynamic system; effective intervention; enteric infection; enteric pathogen; experimental study; exposure pathway; gastrointestinal; infection risk; large datasets; microbial; microbial community; model design; mortality; multi-scale modeling; open source; pathogen; pediatrician; predictive modeling; prevent; simulation environment; social; sociologist; spatiotemporal; stool sample; therapy development; tool; transmission process; urban area; urban setting; virtual laboratory; wasting

Enteric infections remain the second leading cause of diarrheal morbidity and mortality globally in children, despite significant improvements in access to latrines and safe water sources in high disease burden countries. Our prior research has demonstrated that the “enteric pathome” - i.e. the communities of viral, bacterial, and protozoan pathogens transmitted by human and animal feces in the environment - ingested by children living in low-income urban neighborhoods of Kenya is taxonomically complex and varies by exposure pathway. Our preliminary data indicates that the risk of multi-pathogen infection is elevated among those 6- month old Kenyan infants fed cow’s milk and living in urban households with dirt floors, shared latrines, and cohabitating domestic animals. This suggests multiple aspects of societal development beyond latrines and water sources are required to reduce complexity in pathogen transmission in high burden countries. We hypothesize that joint modeling of enteric pathome agents across households and neighborhoods that represent contrasts in urban societal development will show that development leads to pathome evolution from complex to simple community structures, and thus lower detection frequencies for individual pathogen taxa. Understanding the evolution in pathome complexity induced by societal development and subsequently identifying effective intervention strategies is challenging since field experiments are expensive to implement, difficult to generalize to other settings, are only informed by existing, limited observational data, and sufficiently sophisticated statistical and mathematical pathome modeling tools are not currently available. Our proposal aims to (1) develop spatiotemporal and trajectory statistical models to understand the complex exposure risks for infants from the enteric pathome; (2) collect environmental, behavioral, spatial, economic, and microbial data to characterize the enteric pathome along pathways for disease diffusion and the intersection of humans and animals with these pathways; and (3) develop and validate agent-based models (ABMs) for predicting which social and environmental urban developmental interventions (e.g. animal penning, building latrines or drains, concrete floors) best prevent multipathogen transmission to infants in high disease burden countries using established Kenyan study sites as a model. Our interdisciplinary team includes a biostatistician, infectious disease epidemiologists, microbiologist, computational scientist, behavioral researcher, and urban development geographer.

肠道感染仍然是儿童腹泻发病率和死亡率的第二大主要原因，在高疾病烧伤国家中，press的儿童在儿童中获得了显着改善和安全的水源。我们先前的研究表明，“肠道乡亲” - 即人类和动物粪便在环境中传播的病毒，细菌和原生动物病原体的群落 - 被肯尼亚低收入城市社区中的儿童摄入，这是分类法的复杂性和暴露途径。我们的初步数据表明，在饲养牛奶的6个月大的肯尼亚婴儿中，多病原体感染的风险升高，生活在具有污垢地板，共用厕所和同居家畜的城市家庭中。这表明，除了厕所以外，社会发展的多个方面需要降低高负担国家病原体传播的复杂性。我们假设，在城市社会发展中代表对比的家庭和邻里的肠道肠道体代理的联合建模将表明，发展会导致路径从复杂的社区结构发展到简单的社区结构，从而降低了单个病原体分类群的检测频率。了解社会发展引起的路径复杂性的演变以及随后确定有效的干预策略的挑战，因为实施实施价格昂贵，很难推广到其他环境，只有现有的，有限的观察数据以及足够复杂的统计数据和数学的路径建模工具才能提供。我们的建议旨在（1）开发时空和轨迹统计模型，以了解来自促进pathome的婴儿的复杂暴露风险； （2）收集环境，行为，空间，经济和微生物数据，以表征沿疾病扩散途径的启动子途径以及人类和动物与这些途径的相交； （3）开发和验证基于剂的模型（ABM），以预测哪些社会和环境城市发展干预措施（例如动物笔，建造厕所或排水管，混凝土地板）最好地防止使用既定的肯尼亚研究地点作为模型，以最佳的方式防止高病伯恩斯国家的多痛传播向伯恩斯国家的婴儿传播。我们的跨学科团队包括一个生物统计学家，传染病流行病学家，微生物学家，计算科学家，行为研究人员和城市发展地理学家。