tetano
Editor, Senior Moderator
Biology (Basel)
. 2021 Jan 22;10(2):80.
doi: 10.3390/biology10020080.
Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study
Wiriya Mahikul[SUP] 1 2 [/SUP], Palang Chotsiri[SUP] 3 [/SUP], Kritchavat Ploddi[SUP] 4 [/SUP], Wirichada Pan-Ngum[SUP] 3 5 [/SUP]
Affiliations
Abstract
Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. This study aimed to assess and predict the incidence of COVID-19 in Thailand, including the preparation and evaluation of intervention strategies. An SEIR (susceptible, exposed, infected, recovered) model was implemented with model parameters estimated using the Bayesian approach. The model's projections showed that the highest daily reported incidence of COVID-19 would be approximately 140 cases (95% credible interval, CrI: 83-170 cases) by the end of March 2020. After Thailand declared an emergency decree, the numbers of new cases and case fatalities decreased, with no new imported cases. According to the model's predictions, the incidence would be zero at the end of June if non-pharmaceutical interventions (NPIs) were strictly and widely implemented. These stringent NPIs reduced the effective reproductive number (Rt) to 0.73 per day (95% CrI: 0.53-0.93) during April and May. Sensitivity analysis showed that contact rate, hand washing, and face mask wearing effectiveness were the parameters that most influenced the number of reported daily new cases. Our evaluation shows that Thailand's intervention strategies have been highly effective in mitigating disease propagation. Continuing with these strict disease prevention behaviors could minimize the risk of a new COVID-19 outbreak in Thailand.
Keywords: Bayesian approach; coronavirus disease 2019 (COVID-19); intervention; mathematical modelling; prediction.
. 2021 Jan 22;10(2):80.
doi: 10.3390/biology10020080.
Evaluating the Impact of Intervention Strategies on the First Wave and Predicting the Second Wave of COVID-19 in Thailand: A Mathematical Modeling Study
Wiriya Mahikul[SUP] 1 2 [/SUP], Palang Chotsiri[SUP] 3 [/SUP], Kritchavat Ploddi[SUP] 4 [/SUP], Wirichada Pan-Ngum[SUP] 3 5 [/SUP]
Affiliations
- PMID: 33499138
- DOI: 10.3390/biology10020080
Abstract
Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide. This study aimed to assess and predict the incidence of COVID-19 in Thailand, including the preparation and evaluation of intervention strategies. An SEIR (susceptible, exposed, infected, recovered) model was implemented with model parameters estimated using the Bayesian approach. The model's projections showed that the highest daily reported incidence of COVID-19 would be approximately 140 cases (95% credible interval, CrI: 83-170 cases) by the end of March 2020. After Thailand declared an emergency decree, the numbers of new cases and case fatalities decreased, with no new imported cases. According to the model's predictions, the incidence would be zero at the end of June if non-pharmaceutical interventions (NPIs) were strictly and widely implemented. These stringent NPIs reduced the effective reproductive number (Rt) to 0.73 per day (95% CrI: 0.53-0.93) during April and May. Sensitivity analysis showed that contact rate, hand washing, and face mask wearing effectiveness were the parameters that most influenced the number of reported daily new cases. Our evaluation shows that Thailand's intervention strategies have been highly effective in mitigating disease propagation. Continuing with these strict disease prevention behaviors could minimize the risk of a new COVID-19 outbreak in Thailand.
Keywords: Bayesian approach; coronavirus disease 2019 (COVID-19); intervention; mathematical modelling; prediction.