Deterministic Models for Waterborne Infections

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This project will conduct mathematical modeling, analysis and simulation for waterborne infections, which remain a significant public health burden despite tremendous efforts on prevention and intervention programs and a large body of theoretical, experimental, and clinical studies. A particular challenge associated with waterborne infections is to accurately predict the epidemic development and effectively manage the disease outbreaks in populations and environments with strong heterogeneity. The project will develop innovative mathematical models for a deep investigation into the complex, heterogeneous dynamics of waterborne diseases. The project outcome will not only build a solid knowledge base to better understand the transmission and spread of waterborne infections, but also provide useful public health guidelines for disease management and policy-making. Graduate and undergraduate students are involved in the funded research.The project will establish a new mathematical framework for waterborne infections based on deterministic differential equations. A novel, adaptive cluster modeling approach will be introduced that will integrate and extend the classical Lagrangian and Eulerian approaches. This cluster modeling technique will optimize the balance between the homogeneity and heterogeneity in the quantification of disease dynamics as well as the balance between the individual needs and available resources in the management of disease outbreaks. Using this technique, the project will perform an accurate and efficient study on the dynamics of waterborne infections across a wide range of spatial and temporal scales. The project will also employ realistic epidemic and demographic data, particularly those related to cholera outbreaks, to validate model predictions and inform model refinement. Successful completion of this project will lead to a potentially transformative progress in the quantitative study of waterborne infections and related disease control strategies.This award is being funded jointly by the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program and BIO/IOS through the Behavioral Systems Cluster.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.