Long-standing neglected diseases continue to challenge our global health infrastructure, and emerging pathogens pose new threats worldwide. To inform prevention and response efforts, mathematical models of infectious disease dynamics are being increasingly applied. Here we explain how models can be developed to enhance our understanding and predictive power over population-level disease trends, by capturing both fundamental aspects of transmission and also the effects of medical and behavioral interventions. We review advances in transdisciplinary approaches of disease modeling and illustrate these advances with applications including community-based initiatives undertaken during the Ebola epidemic in West Africa and age-targeting of influenza vaccination in the USA. We further discuss how modern statistical inference facilitates the incorporation of data from behavioral sciences and epidemiology into models, highlighting how data-driven models can constitute powerful tools to inform and improve public health strategies.
Modeling Approaches Toward Understanding Infectious Disease Transmission