Editorial: Improving pandemic and epidemic responses - novel methods and lessons learned from previous infectious disease outbreaks

Journal article : Editorial

Recent outbreaks, including the coronavirus disease 2019 (COVID-19) pandemic, have highlighted the importance of mathematical and statistical modelling in understanding the drivers of transmission and how to tailor responses. Improving future responses requires lessons from these previous outbreaks to be learned and the remaining challenges overcome. This requires developing appropriate mathematical, statistical and computational frameworks that accurately capture the studied mechanisms, leading to a better understanding of how different data can affect these formulations or the simulated interventions. Furthermore, improving model validation and quantifying uncertainty in the model parameter estimates and projections when applied responsively across settings and diseases is also required. Via a collection of 22 papers, this special issue brings together both theoretical advances and applied modelling innovations aimed at improving epidemic and pandemic preparedness and response. It includes articles that develop novel statistical inference approaches, as well as sophisticated and data-informed mathematical models, enhanced simulation techniques, exploration of heterogeneities in disease transmission both across ages and settings and the use of modelling techniques to evaluate intervention strategies across different diseases and settings to improve public health outcomes.

Authors: Swallow, B; Panovska-Griffiths, J

• Publication status: Published
• Publisher: Elsevier
• Journal: Journal of Theoretical Biology
• Volume: 625
• Article number: 112424
• Publication date: 2026-03-05
• DOI: 10.1016/j.jtbi.2026.112424
• ISSN: 0022-5193
• Pmid: 41794357
• Language: English
• Keywords: infectious disease modelling; pandemic science; epidemics; network models
• Subtype: Editorial