Challenges in modeling the emergence of novel pathogens
Emma E. Glennon,
Marjolein Bruijning,
Justin Lessler,
Ian F. Miller,
Benjamin L. Rice,
Robin N. Thompson,
Konstans Wells,
C. Jessica E. Metcalf
Affiliations
Emma E. Glennon
Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK; Corresponding author.
Marjolein Bruijning
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA
Justin Lessler
Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
Ian F. Miller
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA; Rocky Mountain Biological Laboratory, Crested Butte, CO 81224, USA
Benjamin L. Rice
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544, USA; Madagascar Health and Environmental Research (MAHERY), Maroantsetra, Madagascar
Robin N. Thompson
Mathematics Institute, University of Warwick, Warwick CV4 7AL, UK; The Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Warwick CV4 7AL, UK
Konstans Wells
Department of Biosciences, Swansea University, Swansea SA28PP, UK
C. Jessica E. Metcalf
Disease Dynamics Unit, Department of Veterinary Medicine, University of Cambridge, Cambridge CB3 0ES, UK; Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
The emergence of infectious agents with pandemic potential present scientific challenges from detection to data interpretation to understanding determinants of risk and forecasts. Mathematical models could play an essential role in how we prepare for future emergent pathogens. Here, we describe core directions for expansion of the existing tools and knowledge base, including: using mathematical models to identify critical directions and paths for strengthening data collection to detect and respond to outbreaks of novel pathogens; expanding basic theory to identify infectious agents and contexts that present the greatest risks, over both the short and longer term; by strengthening estimation tools that make the most use of the likely range and uncertainties in existing data; and by ensuring modelling applications are carefully communicated and developed within diverse and equitable collaborations for increased public health benefit.
