Determinants of the infection rate of the COVID-19 in the U.S. using ANFIS and virus optimization algorithm (VOA)

Abstract

Recently, anovel coronavirus disease (COVID-19) has become a serious concern for global public health. Infectious disease outbreaks such as COVID-19 can also significantly affect the sustainable development of urban areas. Several factors such as population density and climatology parameters could potentially affect the spread of the COVID-19. In this study, a combination of the virus optimization algorithm (VOA) and adaptive network-based fuzzy inference system (ANFIS) was used to investigate the effects of various climate-related factors and population density on the spread of the COVID-19. For this purpose, data on the climate-related factors and the confirmed infected cases by the COVID-19 across the U.S counties was used. The results show that the variable defined for the population density had the most significant impact on the performance of the developed models, which is an indication of the importance of social distancing in reducing the infection rate and spread rate of the COVID-19. Among the climatology parameters, an increase in the maximum temperature was found to slightly reduce the infection rate. Average temperature, minimum temperature, precipitation, and average wind speed were not found to significantly affect the spread of the COVID-19 while an increase in the relative humidity was found to slightly increase the infection rate. The findings of this research show that it could be expected to have slightly reduced infection rate over the summer season. However, it should be noted that the models developed in this study were based on limited one-month data. Future investigation can benefit from using more comprehensive data covering a wider range for the input variables.