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

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，尽管在高疾病负担国家，获得厕所和安全水源的情况有了重大改善。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“肠道病原菌”——即通过环境中人类和动物粪便传播的病毒、细菌和原生动物病原体群落——在分类上是复杂的，并因暴露途径而异。我们的初步数据表明，在6个月大的肯尼亚婴儿中，那些以牛奶为食、生活在肮脏的地板、共用厕所和同居家畜的城市家庭中的婴儿，多病原体感染的风险升高。这表明，要在高负担国家减少病原体传播的复杂性，需要在厕所和水源之外的多个社会发展方面做出努力。我们假设，跨家庭和社区的肠道病原体联合建模代表了城市社会发展的差异，将表明发展导致病原体从复杂到简单的社区结构进化，从而降低单个病原体分类群的检测频率。了解由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，因为现场实验实施成本高昂，难以推广到其他环境，只能通过现有的有限观察数据提供信息，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)建立时空和轨迹统计模型，以了解肠道病原菌对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，以表征疾病传播途径和人类与动物在这些途径上的交集；(3)开发并验证基于主体的模型（ABMs），以使用已建立的肯尼亚研究地点作为模型，预测哪些社会和环境城市发展干预措施（例如动物围栏、建造厕所或排水沟、混凝土地板）最能防止多病原体向高疾病负担国家的婴儿传播。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。

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.