Data‐Driven Models of Foot‐and‐Mouth Disease Dynamics: A Review. Issue 3 (18th November 2015)

• Record Type: Journal Article
• Authors: Pomeroy, L. W.; Bansal, S.; Tildesley, M.; Moreno‐Torres, K. I.; Moritz, M.; Xiao, N.; Carpenter, T. E.; Garabed, R. B.
• Is Part Of: Transboundary and emerging diseases. Volume 64:Issue 3(2017)
• Page Start: 716
• Page End: 728
• Publication Date: 2015-11-18
• Subjects: foot‐and‐mouth disease virus -- dynamical models -- transmission -- targeted control; Veterinary medicine -- Periodicals; 636.089
• DOI: 10.1111/tbed.12437
• Languages: English
• ISSNs: 1865-1674

Abstract

Summary: Foot‐and‐mouth disease virus (FMDV) threatens animal health and leads to considerable economic losses worldwide. Progress towards minimizing both veterinary and financial impact of the disease will be made with targeted disease control policies. To move towards targeted control, specific targets and detailed control strategies must be defined. One approach for identifying targets is to use mathematical and simulation models quantified with accurate and fine‐scale data to design and evaluate alternative control policies. Nevertheless, published models of FMDV vary in modelling techniques and resolution of data incorporated. In order to determine which models and data sources contain enough detail to represent realistic control policy alternatives, we performed a systematic literature review of all FMDV dynamical models that use host data, disease data or both data types. For the purpose of evaluating modelling methodology, we classified models by control strategy represented, resolution of models and data, and location modelled. We found that modelling methodology has been well developed to the point where multiple methods are available to represent detailed and contact‐specific transmission and targeted control. However, detailed host and disease data needed to quantify these models are only available from a few outbreaks. To address existing challenges in data collection, novel data sources should be considered and integrated into models of FMDV transmission and control. We suggest modelling multiple endemic areas to advance local control and global control and better understand FMDV transmission dynamics. With incorporation of additional data, models can assist with both the design of targeted control and identification of transmission drivers across geographic boundaries.