Mathematics of Public Health

lt;b>Jummy David is currently a public health analytics and modeling fellow at the Centers for Disease Control and Prevention (CDC), USA. Prior to CDC, she was a postdoctoral fellow at York University, and obtained her PhD (2020) and Master's degree from the University of British Columbia, Canada. She is an applied mathematician, a computational and research-driven scientist with in-depth, hands-on advanced mathematical modeling and simulation methods. She is great at leveraging on analytics and quantitative experiences to develop and analyze mechanistically relevant mathematical models, PK/PD models, and health economic models. Results of her modeling and simulation analysis are used to evaluate, forecast, predict, optimize and accelerate policy designs toward improving decision making in public health, medicine, computational biology and science.

Preface.- Mathematical models: perspectives of mathematical modelers and public health professionals.- Discovering first-principle of behavioural change in disease transmission dynamics by deep learning.- Understanding Epidemic Multi-Wave Patterns via Machine Learning Clustering and the Epidemic Renormalization Group.- Contact Matrices in Compartmental Disease Transmission Models.- An optimal control approach for public health interventions on an Epidemic-Viral model in deterministic and stochastic environments.- Modeling airborne disease dynamics: progress and questions.- Modelling mutation-driven emergence of drug-resistance: a case study of SARS-CoV-2.- A Categorical Framework for Modeling with Stock and Flow Diagrams.- Agent-Based Modeling and its Tradeoffs: An Introduction & Examples.- Mathematical assessment of the role of interventions against SARS-CoV-2.- Long term dynamics of COVID-19 in a multi-strain model.