Model-based machine learning to explore the nexus between COVID-19 and environmental factors in the United States

The aim of this study is to demonstrate the applicability of machine learning methods to understand the transmission of the viral flow of COVID-19 with respect to various environmental factors. Daily update data of new COVID-19 related reported cases from six states of the United State (US), dated from 1 March 2020 to 30 Nov 2020, across 6 US states - New York, New Jersey, Illinois, Massachusetts, Georgia and Michigan are examined. The daily COVID-19 update data are assembled from the US health department and Weather Underground Company (WUC) official websites. A diverse set of environmental factors, including temperature, humidity, dew point, wind speed, atmospheric pressure and precipitation are used to express possible environmental determinants. Asymmetric distributions of daily reported new cases of COVID-19 with respect to all states is evident. The average numbers of new reported cases of COVID-19 patients remains highest in Illinois. Whereas maximum numbers of affected cases in a single day were reported in Georgia. The lowest of the average new cases is found in Massachusetts. Finally, based on the outcomes of this research, we believe that a more rigorous study targeting other variables, such as population density, mobility, air quality, nature of travel bans, race, and the degree of health interventions, is required. Furthermore, understanding the potential for seasonality and the association with weather is particularly relevant for further work given the longer time series of COVID-19 information now available in 2021, as is modelling new cases, transmission, along with deaths, reproduction number and severity levels of COVID-19. Given the skewed nature of the distribution of number of reported cases in each state, future work could likewise employ the quintile regression approach.