Unraveling the Impact of Non-Pharmaceutical Interventions on Pathogen Mutation

Journal

Authors

Jiaqi Chen, Dong Wang, Peng Wu, Benjamin J. Cowling, Sheikh Taslim Ali

Abstract

This work develops a novel Bayesian machine learning framework to rigorously examine how macroscopic non-pharmaceutical interventions (NPIs)—such as lockdowns, mask mandates, and social distancing policies—influence microscopic pathogen mutation dynamics. By bridging population-level policy analysis with viral evolutionary modeling, this study sheds new light on the co-evolution of public health responses and infectious disease.

Key Contributions

• Developed a new Bayesian machine learning framework integrating ODE-based epidemic models with statistical inference
• Quantified the causal impact of macroscopic NPIs on microscopic pathogen mutation rates
• Applied bootstrap resampling to rigorously assess estimation uncertainty

Technologies

R   Python   Mathematical Modeling (ODE & Statistics)   Bayesian Inference   Machine Learning   Bootstrap   Linux

Recommended citation: Chen, J., Wang, D., Wu, P., Cowling, B. J., & Ali, S. T. (2024). "Unraveling the Impact of Non-Pharmaceutical Interventions on Pathogen Mutation." Nature Communications (Under Review).