Publication: Challenging the spread of COVID-19 in Thailand
Issued Date
2020-01-01
Resource Type
ISSN
23527714
Other identifier(s)
2-s2.0-85092622392
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Mahidol University
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SCOPUS
Bibliographic Citation
One Health. (2020)
Suggested Citation
Kraichat Tantrakarnapa, Bhophkrit Bhopdhornangkul Challenging the spread of COVID-19 in Thailand. One Health. (2020). doi:10.1016/j.onehlt.2020.100173 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/60110
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Title
Challenging the spread of COVID-19 in Thailand
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Abstract
© 2020 The Author(s) 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.