Machine learning for investigation of climatic effects on the prevalence of Coronavirus

Environmental factors believe that they guarantee human health, and this increases the pressure on health systems. According to studies, infectious diseases and corona loading originate from the environment and wildlife. Therefore, human pressure in the natural environment may cause the emergence of other diseases. Thus, strengthening health systems, improving infectious diseases in the wild, and protecting natural resources and the environment increase the risk of new diseases in the market in the future. This study reviews the results of articles that computationally identified the impact of climatic characteristics on the coronavirus. Finally, although it can not be said with certainty, it can be noted that the two factors of temperature and humidity have a significant impact on the prevalence of coronavirus. In this study, we reached this conclusion by examining twelve cities in Albania. Using the random forest machine learning method, we investigated the effect of climatic parameters on the mortality rate.

Keywords:

Covid-۱۹ , Computational method , Environment , Machine learning , Temperature , Virus