Modeling COVID-19 Dynamics with a Medical Treatment Strategy: A Case Study of Thailand - Chen, Yinghui; Modnak, Chairat (Communication in Biomathematical Sciences, 2025)
Since 2020, Thailand has been impacted by the COVID-19 pandemic, which continues to persist into 2025. In response, the country has implemented various disease control measures, including public health campaigns and vaccination programs. While these strategies are still in place, they are now applied with less intensity, allowing people to return to a more normal way of life. However, this relaxed approach can contribute to continued disease transmission. In this study, we shift focus from conventional control measures-such as vaccination, mask-wearing, and social distancing-to strategies aimed at coexisting with the disease while minimizing its spread. Specifically, we investigate the impact of treating symptomatic and severe patients to reduce their infectiousness and thereby lower the risk of transmission to others. To achieve this, we develop a mathematical model of COVID-19 transmission dynamics and apply it using Thailand's 2025 data. We analyze the stability of both the disease-free and endemic equilibrium points and explore an optimal control problem related to medical treatment strategies. Our findings suggest that reducing the infectiousness of symptomatic and severe cases through effective treatment can help slow down the spread of COVID-19, supporting safer coexistence in a society returning to normalcy.