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: 10615095
• 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/10615095
• 关键词: Accounting; 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; Policies; Pollution; 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; Urbanization; Viral; Water; Work; burden of illness; design; disease transmission; dynamic system; effective intervention; enteric infection; enteric pathogen; experimental study; exposure pathway; gastrointestinal; improved; 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.

肠道感染仍然是全球儿童腹泻发病率和死亡率的第二大原因，尽管在疾病负担高的国家，获得厕所和安全水源的情况有了很大改善。我们之前的研究已经证明，生活在肯尼亚低收入城市社区的儿童摄入的“肠道病原体”--即通过人和动物的粪便在环境中传播的病毒、细菌和原生动物病原体群落--在分类上是复杂的，并因接触途径而异。我们的初步数据表明，那些6个月大的肯尼亚婴儿中，多病原体感染的风险增加，这些婴儿喂食牛奶，生活在地板肮脏、共用厕所和与家畜同居的城市家庭中。这表明，要降低高负担国家病原体传播的复杂性，需要在厕所和水源之外进行多方面的社会发展。我们假设，对代表城市社会发展差异的家庭和社区的肠道病原体进行联合建模将表明，发展导致病原体从复杂的群落结构演变为简单的群落结构，从而降低了对单个病原体分类群的检测频率。理解社会发展引起的病理组复杂性的演变并随后确定有效的干预策略是具有挑战性的，因为现场实验实施起来成本高昂，难以推广到其他环境，仅由现有的有限观测数据提供信息，并且目前还没有足够复杂的统计和数学病理组建模工具。我们的建议旨在(1)开发时空和轨迹统计模型，以了解婴儿接触肠道病原体的复杂风险；(2)收集环境、行为、空间、经济和微生物数据，以表征疾病传播路径上的肠道病原体特征，以及这些路径与人和动物的交集；以及(3)开发和验证基于试剂的模型(ABM)，以肯尼亚已建立的研究地点为模型，预测哪些社会和环境城市发展干预措施(例如，动物圈养、修建厕所或下水道、混凝土地板)最能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、行为研究员和城市发展地理学家。