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. References 1. McLean AR. Infectious Disease Modeling. 2012. 2. Grassly NC, Fraser C. Mathematical models of infectious disease transmission. Nature Reviews Microbiology. 2008;6(6):477-87. 3. Wei Y, Sha F, Zhao Y, Jiang Q, Hao Y, Chen F. Better modelling of infectious diseases: lessons from covid-19 in China. BMJ 2021;375:n2365. 4. Kimani TN, Nyamai M, Owino L, Makori A, Ombajo LA, Maritim M, et al. Infectious disease modelling for SARS-CoV-2 in Africa to guide policy: A systematic review. Epidemics. 2022;40:100610. 5. CMCC, Group. Guidance on Use of Modelling for Policy Responses to COVID-19. 2020. 6. Laxminarayan R, Kakkar M, Horby P, Malavige GN, Basnyat B. Emerging and re-emerging infectious disease threats in South Asia: status, vulnerability, preparedness, and outlook. Bmj. 2017;357:j1447. 7. Skowronski DM, Astell C, Brunham RC, Low DE, Petric M, Roper RL, et al. Severe Acute Respiratory Syndrome (SARS): A Year in Review. Annual Review. 2005;56(1):357-81. 8. Eagles D, Siregar ES, Dung DH, Weaver J, Wong F, Daniels P. H5N1 highly pathogenic avian influenza in Southeast Asia. Revue scientifique et technique (International Office of Epizootics). 2009;28(1):341-8. 9. เหลืองวิไล เ, บัวภิบาล ฐ, หมุดและ ส, วิลามาศ ว, มีชาวนา ส, ลีลายุทธ ส. Estimating the Covid-19 epidemic in Thailand by using SIR/D model. NKRAFA JOURNAL OF SCIENCE AND TECHNOLOGY. 2020;16(2):ุึ67-73. 10. Patanarapeelert K, Songprasert W, Patanarapeelert N. Modeling Dynamic Responses to COVID-19 Epidemics: A Case Study in Thailand. Tropical Medicine and Infectious Disease. 2022;7(10):303. 11. Dechsupa S, Assawakosri S, Phakham S, Honsawek S. Positive impact of lockdown on COVID-19 outbreak in Thailand. Travel medicine and infectious disease. 2020;36:101802.