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Monitoring spatiotemporal trends in COVID-19 using state space SEIR models

Lartey, Laura
The fight against COVID-19 has put a strain on national human and economic resources, which has led to the need that a thorough investigation into the virus’s global dissemination be carried out. The purpose of this study is to develop a model that can predict the dynamics of the virus’ transmission and recovery rates while also taking into account the spatial and temporal trends present in the data. After conducting preliminary research with the help of preexisting spatio-temporal models found in the CARBayesST package, it was discovered that these models were not adequate for modeling the virus. This paper’s objective is to present a compartmental modeling framework in the form of the Susceptible-Exposed-Infectious-Recovered (SEIR) model that incorporates spatially variable transmission and recovery rates.