Abstract
Background: Mathematical models are not integrated into the policy‑making process in low‑ and middle‑income countries, including India, primarily due to limited capacity in building mathematical models, lack of trust in the model findings and the reluctance of policy‑makers to apply the model findings to formulate policies. There is a perceived need to create a critical mass of trained infectious disease experts and modelers within the public health and clinical domain. Thus, with the support of the Department of Health Research (DHR), we developed a 3‑month post‑graduate (PG) certificate course on infectious disease modelling, the first of such a course in India. The first cycle of the course was delivered during July to September 2024, which produced the first cohort of 20 infectious disease modellers in the country. Methods: This paper describes the structure, content and key components of the first course along with the experiences, strengths, challenges and way forward from the participants' perspective using a mixed methods approach. Findings: Most of the participants felt that the learning objectives were clear (n = 18, 90%), course content was well organised and delivered (n = 19, 95%) and the course structure allowed all participants to fully participate (n = 19, 95%) in the learning process. The strengths of the course were: hybrid mode of delivery, well‑designed course content, theory lectures followed by practical sessions, regular trainer-trainee communication, interactive discussion forums and the 3‑day contact workshop. The key challenges were non‑availability of recorded videos, evening timings of the sessions and difficulty of some topics. Conclusions: The challenges and recommendations will feed into the subsequent course cycles. Future courses are planned to be hosted on an online platform to facilitate participant completion of the course at their own pace. More collaboration with various stakeholders, nationally and internationally, will be sought to improve the content, delivery and robustness of the program.