Comparison of the inhomogeneous SEPIR model and data from the COVID-19 outbreak in South Carolina

During the COVID-19 pandemic authorities have been striving to obtain reliable predictions for the spreading dynamics of disease. We recently developed an in-homogeneous multi-"sub-populations" (multi-compartments: susceptible, exposed, pre-symptomatic, infectious, recovered) model, that accounts for the spatial in-homogeneous spreading of the infection and shown, for a variety of examples, how the epidemic curves are highly sensitive to location of epicenters, non-uniform population density, and local restrictions. In the present work we tested our model against real-life data from South Carolina during the period May 22 to July 22 (2020), that was available in the form of infection heat-maps and conventional epidemic curves. During this period, minimal restrictions have been employed, which allowed us to assume that the local reproduction number is constant in time. We accounted for the non-uniform population density in South Carolina using data from NASA, and predicted the evolution of infection heat-maps during the studied period. Comparing the predicted heat-maps with those observed, we find high qualitative resemblance. Moreover, the Pearsons correlation coefficient is relatively high and does not get lower than 0.8, thus validating our model against real-world data. We conclude that our model accounts for the major effects controlling spatial in-homogeneous spreading of the disease. Inclusion of additional sub-populations (compartments), in the spirit of several recently developed models for COVID-19, can be easily performed within our mathematical framework.