Cubic B-spline and Generalised Linear Models for COVID-19 Patients in Thailand

Abstract

Thailand is one of the countries which has been affected by the coronavirus disease (COVID-19) pandemic as it is known a severe acute respiratory disease. COVID-19 was first emerged in Thailand in January and the number of infected people on a daily basis has increased significantly over a year. A valid data set of COVID-19 cases in Thailand is collected by the Department of Disease Control, Ministry of Public Health. This paper, therefore, was subjected to the daily reported of cases to the estimation of statistical models using count autoregressive regression based on Poisson distribution and cubic B-spline regression analysis to quantify patterns in the incidence for different groups of COVID-19 patients in Thailand. The findings in Phase I show that male patients with specific age groups influence the spread of infected disease. In addition, the cubic B-spline with optimal tuning equal to 8 is the best-fitting model in Phase II.