Modeling and control of environmentally transmitted pathogens

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DESCRIPTION (provided by applicant): Many pathogens are able to infect hosts via the environment without direct contact between hosts. There is a critical need to better characterize environmental transmission in mathematical and computational models used for disease control. Our long term research goal is to develop a quantitative framework to advance disease transmission theory and control of pathogens with environmental pathways and reservoirs. Our central hypothesis is that the suitability of different model formulations for environmental transmission depends on the pathogen life history, the characterization of the environmental reservoir, and the processes associated with pathogen exposure. Using as a case study the transmission of Clostridium difficile in health-care settings, the following specific aims will be carried out: 1) Develop and analyze models that mechanistically address environmental transmission. Explicit functional forms that represent exposure pathways will be developed using queueing theory, and will be linked to the pathogen dynamics outside the host, and implications in disease dynamics will be investigated. 2) Develop and analyze spatially explicit models to address the role of spatial heterogeneity in environmental transmission. Agent-based models (ABM) will be developed to include spatial features of environmental transmission. Their aggregated behavior will be approximated and compared with partial differential equations models. 3) Assess the implications of environmental transmission in disease control and surveillance using optimal control theory. Optimal control will be applied to ABMs to identify the preferred environmental strategies. The project will deliver a comprehensive understanding of the scaling up of environmental transmission, and the suitability of different model representations of environmental reservoirs, transmission, and control strategies that reduce environmental exposure. From a mathematical perspective, the project will address significant mathematical challenges associated to the analysis of ABMs, which are used across all biological disciplines, from molecular biology to ecology, namely how to reduce spatially explicit ABMs to mean-field dynamics, and how to transfer optimal control strategies from the mean-field models to the ABMs.