In this episode, we dive into the use of the SIR model to predict the trajectory of the COVID-19 pandemic. Our speakers break down the basics of the model, explaining how it helps forecast infection spread, and why accurate data is crucial for reliable predictions. We also explore the limitations of using the SIR model in developing countries, where data collection can be inconsistent, leading to less accurate outcomes. To address these challenges, the conversation introduces the SPE approach, which uses fixed recovery rates to improve model precision, with real-world examples from Norway. We wrap up by discussing the importance of understanding the assumptions behind pandemic models and why a healthy dose of skepticism is essential when interpreting their predictions.

This podcast is based on the following article:

Senel, K., Ozdinc, M., & Ozturkcan, S. (2021). SPE approach for robust estimation of SIR model with limited and noisy data: The case for COVID-19. Disaster Medicine and Public Health Preparedness, 15(3), e8-e22. https://doi.org/10.1017/dmp.2020.220