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: 10227256
• 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/10227256
• 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.

尽管高疾病负担国家的厕所和安全水源的使用情况显着改善，但肠道感染仍然是全球儿童腹泻发病和死亡的第二大原因。生活在肯尼亚低收入城市社区的儿童摄入的环境中人类和动物粪便传播的细菌和原生动物病原体在分类学上很复杂，并且因暴露途径而异。在那些喝牛奶、生活在地板肮脏、共用厕所和家畜同居的城市家庭中的 6 个月大肯尼亚婴儿中，多种病原体感染的比例较高，这表明除了厕所和水源之外，还需要社会发展的多个方面来解决这一问题。我们探索了代表城市社会发展对比的家庭和社区肠道病原体传播的联合模型，这将表明发展导致病原体从复杂的社区结构演变为简单的社区结构，从而降低检测频率。了解社会发展引起的病理复杂性的演变并随后确定有效的干预策略具有挑战性，因为要实施现场实验，难以推广到其他环境，只能通过现有的、有限的观察数据来了解，并且足够复杂。目前尚无统计和数学病理模型工具，目的是（1）开发时空和轨迹统计模型，以了解婴儿因肠道病理而面临的复杂暴露风险；（2）收集环境、行为、空间、经济和风险。微生物数据描述疾病传播途径的肠道病理特征以及人类和动物与这些途径的交叉；(3) 开发和验证基于主体的模型 (ABM)，用于预测哪些社会和环境城市发展干预措施（例如动物围栏、修建厕所）使用已建立的肯尼亚研究中心作为模型，最好地防止多病原体传播给高疾病负担国家的婴儿。我们的跨学科团队包括生物统计学家、传染病流行病学家、微生物学家、计算科学家、行为学家。研究员、城市发展地理学家。