Regional Prediction of COVID-19 in the United States Based on the Difference Equation Model (Journal of Nonlinear Modeling and Analysis, 2024)
The novel coronavirus pneumonia 2019 (COVID-19) has swept theglobe in just a few months with negative social and psychological consequencesfor public health. So far, the United States has been one of the countries mostaffected by the epidemic. In this study, 51 states in the United States aredivided into 10 state clusters according to relevant factors, and a differenceequation model with spatio-temporal dynamic characteristics is established topredict the transmission dynamics of COVID-19 in the 10 state clusters andobtain data on regional aggregation levels (the United States). The studyshowed that the Pearson Correlation Coefficient between the actual data andthe predicted data in the 10 state clusters is between 0.6 and 0.96 (mean $R^2$=0.8448), and the mean absolute error (MAE) of the newly confirmed casesin each cluster is between 300 and 1650 (mean MAE=878) and the averageforecasting error rate (AFER) of the total confirmed cases in each cluster isbetween 0.9% and 3% (mean AFER=1.57%). These results show that thedifference equation model can well predict the changes in the recent confirmedcases of infectious diseases such as COVID-19.