Compartmental models in epidemiology describe the flow of individuals between relevant population categories (such as the Infected, the Susceptible to infection, the Recovered, etc.) during the course of the epidemic. Having in mind the conditions in which the COVID-19 was spreading in the early phases, characterized by widely implemented social distancing measures after the initial exponential growth, we constructed a non-linear compartmental model which describes the SARS-CoV-2 virus transmission and the epidemic progression in a population. The examination of the cumulative case and death dynamic curves for various populations (countries/regions) reveals several distinct, general phases of the COVID-19 epidemic growth. Focusing on the initial exponential phase of the uncontrolled virus transmission, one can use the model to infer the basic reproduction number (R0, the standard epidemiological measure of the inherent virus transmissibility, dependent on the virus and the population/environment characteristics) for each country. As the first part of our study of the influence of demographic and climatic factors on the SARS-CoV-2 virus transmission, we analyzed correlations between R0 values and 42 different factors for ~118 countries. Furthermore, we applied the model to the data for 30 provinces of China and determined the values of the main parameters of the epidemic growth in each of them. The model provides a reasonable interpretation of the prominent disproportion between the intensive spread of the infection in Wuhan (Hubei) and the much smaller case counts in other Chinese provinces, which may have occurred due to a significantly higher inherent virus transmission in Wuhan and more efficient epidemic control measures in other provinces. In conclusion, the results of these analyzes indicate that the dynamics of the epidemic spread may significantly depend on potentially highly heterogeneous and seemingly random factors, such as variations in demographic and meteorological conditions, as well as their complex interaction with introduced control measures. Understanding these factors is crucial, not only for risk estimation during a pandemic but also for long-term prediction of virus behavior in a population if the COVID-19 disease becomes endemic.
Keywords:
compartmental model, bioinformatics, COVID-19, basic reproduction number, environmental effects in nonlinear metasurfaces.