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Title: Spatiotemporal transmission dynamics of COVID-19 in Nigeria Authors:  Ashok Krishnamurthy - Mount Royal University (Canada) [presenting]
Bedrich Sousedik - University of Maryland Baltimore County (United States)
Maya Mueller - University of Maryland Baltimore County (United States)
Agatha Ojimelukwe - University of Port Harcourt (Nigeria)
Brittany Millis - Mount Royal University (Canada)
Jocelyn Boegelsack - Mount Royal University (Canada)
Loren Cobb - University of Colorado Denver (United States)
Abstract: The global coronavirus pandemic (COVID-19) reached Lagos, Nigeria on February 27, 2020. Since then, COVID-19 infections have been reported in the majority of Nigerian states. We present a spatial Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) compartmental model of epidemiology to capture the transmission dynamics of the spread of COVID-19 and provide insight that would support public health officials towards informed, data-driven decision making. Data assimilation is a general category of statistical tracking techniques that incorporate and adapt to real-time data as they arrive by sequential statistical estimation. Data assimilation applied to the SEIRD model receives daily aggregated epidemiological data from the Nigeria Centre for Disease Control (NCDC) and uses this data to perform corrections to the current state vector of the epidemic. In other words, it enhances the operation of the SEIRD model by periodically executing a Bayesian correction to the state vector, in a way that is, at least arguably, statistically optimal. We observe that the prediction improves as data is assimilated over time. It is essential to understand what future epidemic trends will be, as well as the effectiveness and potential impact of government disease intervention measures. Predictions for disease prevalence with and without mitigation efforts are presented via spatiotemporal disease maps.