Challenging the spread of COVID-19 in Thailand

Kraichat Tantrakarnapa; Bhophkrit Bhopdhornangkul

doi:10.1016/j.onehlt.2020.100173

Challenging the spread of COVID-19 in Thailand

One Health. 2020 Dec 20:11:100173. doi: 10.1016/j.onehlt.2020.100173. Epub 2020 Oct 7.

Authors

Kraichat Tantrakarnapa¹, Bhophkrit Bhopdhornangkul²

Affiliations

¹ Department of Social and Environmental Medicine, Faculty of Tropical Medicine, Mahidol University, Ratchathewi, Bangkok, Thailand.
² Infectious Disease Control and Entomology Section, Division of Health Promotion and Preventive Medicine, Royal Thai Army Medical Crops, THailand.

Abstract

Coronavirus disease (COVID-19) has been identified as a pandemic by the World Health Organization (WHO). It was initially detected in Wuhan, China and spread to other cities of China and all countries. It has caused many deaths and the number of infections became greater than 18 million as of 5 August 2020. This study aimed to analyze the situation of COVID-19 in Thailand and the challenging disease control by employing a dynamic model to determine prevention approaches. We employed a statistical technique to analyze the ambient temperature influencing the cases. We found that temperature was significantly associated with daily infected cases (p-value <0.01). The SEIR (Susceptible Exposed Infectious and Recovered) dynamic approach and moving average estimation were used to forecast the daily infected and cumulative cases until 16 June as a base run analysis using STELLA dynamic software and statistical techniques. The movement of people, both in relation to local (Thai people) and foreign travel (both Thai and tourists), played a significant role in the spread of COVID-19 in Thailand. Enforcing a state of emergency and regulating social distancing were the key factors in reducing the growth rate of the disease. The SEIR model reliably predicted the actual infected cases, with a root mean square error (RMSE) of 12.8. In case of moving average approach, RMSE values were 0.21, 0.21, and 0.35 for two, three and five days, respectively. The previous records were used as input for prediction that caused lower values of RMSE. Two-days and three-days moving averages gave the better results than SEIR model. The SEIR model is suitable for longer period prediction, whereas the moving average approach is suitable for short term prediction. The implementation of interventions, such as governmental regulation and restrictions, through collaboration among various sectors was the key factor for controlling the spreading of COVID-19 in Thailand.

Keywords: COVID-19; Disease control; Moving average model; SEIR model; Spread of disease; Thailand.