Forecasting modeling of the number of cumulative COVID-19 cases with deaths and recoveries removal in Thailand
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Abstract
Coronavirus disease 2019 (COVID-19) could become one of the problems for the healthcare system in Thailand, thus forecasting the number of cumulative cases could be helpful to mitigate this. The present research was conducted to forecast the number of cumulative COVID-19 cases in Thailand and analyze any trends in the data. The predictive modeling was based on logistic models for cumulative COVID-19 cases with and without removing the deaths and recoveries data. Confidence intervals for and validation of the forecasting models were also given. The results showed that the logistic model performed better than the logistic model after deaths and recoveries removal but analysis of the behavior of the number of cumulative COVID-19 cases in Thailand by using the latter model was still pertinent. This research provided a predictive modeling tool to help the authorities developing policies for controlling the spread of COVID-19 in Thailand.
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