(456d) A Nonlinear Programming Framework for Estimating Spatial Coupling and Seasonal Transmission Parameters in Disease Transmission
AIChE Annual Meeting
Wednesday, October 31, 2018 - 8:57am to 9:16am
We address the issue of estimating disease transmission parameters using a flexible, scalable modeling framework. Spatial coupling parameters are a measure of the level of mixing of individuals between regional subpopulations . Seasonal transmission parameters describe the impact of seasonal variation due to effects like degree of interaction . This work focuses on estimating both spatial coupling and seasonal transmission parameters with a nonlinear programming approach for a network of subpopulations.
A framework is presented for efficient estimation of city-to-city spatial transmission rates by inferring transport information from localized disease case data using a statistical, hazard-based SIR model . The estimation is demonstrated using records of measles outbreaks in 954 cities across England and Wales between 1944 and 1964. First, a stochastic model is constructed to predict spatio-temporal disease dynamics and accurately match existing datasets. A statistical hazard-based approach focusing on disease fade-out periods provides the basis for the estimation. Then, the model is extended to simultaneously estimate the city-to-city spatial transmission parameters, as well as seasonal transmission parameters. The proposed approach for this large-scale estimation accurately reproduces existing parameter estimates from [1,4], is readily scalable to larger problem sizes, and substantially reduces solution times.
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