Geospatial Analysis of The Spreading of COVID-19 In The United States

The COVID-19 pandemic has been a big threat to public health and there is an increasing need for efficient modelling of pathogens, predicting the daily infection rates to reduce the spread of COVID-19. The Moran’s and Geary’s statistics showed significant spatial autocorrelation in the infection counts for the US COVID-19 data. Spatial regression using the simultaneous autoregression (SAR) and conditional autoregression (CAR) models indicate clear association between the confirmed cases and the number of population and the population density in both national county and state specific analyses. The SAR model provided a better model fit with the low AIC value, leaving no significant autocorrelation for the residuals. The approximate Bayesian computation (ABC) methods were used to provide a flexible posterior distribution of the infection rate for COVID-19 based on the first 100 days of the pandemic. Three different simulation methods such as ABC-Rejection, ABC- Markov Chain Monte Carlo (MCMC) and ABC- Sequential Monte Carlo (SMC) were employed and compared. These algorithms seem to give reasonable posterior estimates for the average daily infections when the likelihood calculations for the spread of a harmful pathogen become complex, or intractable entirely. The posterior distributions of ABC-MCMC and ABC-SMC provided plausible estimations covering all of the observed infection rates at different time points