ID modelling is a mathematical description of the spread of infection (1). Infectious disease (ID) in this study refers to common infectious diseases (whether in humans or animals), including COVID-19. ID models help provide the understanding of the disease natural history, as well as the impact of interventions (e.g., vaccination, social distancing, facemask wearing, or locking down etc.) depending on the choice of mathematical model (2). Many countries have adopted this type of modelling to help inform their current and future decision making in terms of understanding, predicting, controlling, and preventing the situation (3, 4). Examples include prediction of disease trends, evaluation of safety and control measures imposed, and exploring uncertainty when certain decisions were to change. During the COVID-19 pandemic, they were used to inform policies to prevent the spread of the disease around the world (5).
The knowledge on current advancement, use, and situation of ID modelling in the Southeast Asian region is relatively limited, regardless of its history being epicentre of several outbreaks (6-8). In Thailand, studies using ID modelling have been conducted (9-11), however, there is limited documented literature on its use. Therefore, this study is conceptualised to provide an overview of ID modelling, using Thailand as a primary case study.
This study is expected to benefit the Thai research and policy making community by enhancing their understanding of the current capacity for infectious disease modelling in Thailand and plan for capacity building or networking activities to strengthen this field in the country, to address policy on infectious diseases, both, for routine care as well as for pandemic response. Building such a community of researchers, with links to policy makers, will also strengthen the quality of research that is generated and their use in the future.
This study is supported by the Rockefeller Foundation.
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