Networked Epidemic Modeling of Infectious Disease
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During the past two decades, network scientists have demonstrated that interaction among population members can dramatically influence spreading dynamics. In networked epidemic models, interactions among individuals are explicitly modeled using a contact network where individuals are the nodes, and possible interactions are the edges of the graph. This talk introduces the generalized epidemic modeling framework (GEMF) that facilitates systematic implementation of a broad spectrum of stochastic spreading processes over complex networks. GEMF is flexible and scalable to incorporate multiple nodal states and multiple types of interactions between nodes. A simple-to-use tool, GEMFsim, can numerically simulate any stochastic GEMF-based model. GEMFsim is also highly flexible and scalable due to its optimized data structure, and is capable of simulating spreading processes on networks with millions of nodes. The procedure required for setting up a simulation is simple and systematic and is available in popular programming platforms such as C, Python, R, and MATLAB. Finally, the talk concludes with implications of this network-based framework for studying the spatio-temporal spread of COVID-19. Zoom: https://arizona.zoom.us/j/183045568