Count Data Models and Machine Learning Methods with Applications to New COVID-19 Cases using Air Pollution Data in Bangkok, Thailand

The novel coronavirus causing the COVID-19 pandemic and morbidity and mortality around the world has left unprecedented repercussions on all aspects of life. Estimation of the incidence of coronavirus patients will reimburse planning and preparation for new waves. The link between COVID-19 and air pollution data can allow us to estimate COVID cases. In this study, count data models; Poisson regression and negative binomial regressions models and machine learning methods; support vector regression and random forest are used to investigate the link between new COVID-19 cases and the air pollution data in Bangkok, Thailand. The root means square error and means absolute percentage error are used as the criterions to compare the efficiency from each method. The results from the real data showed that the support vector regression outperforms random forest and also Poisson regression and negative binomial regression models.