Effects of regional differences and demography in modelling foot-and-mouth disease in cattle at the national scale

Abstract

Foot-and-mouth disease (FMD) is a fast-spreading viral infection that can produce large and costly outbreaks in livestock populations. Transmission occurs at multiple spatial scales, as can the actions used to control outbreaks. The US cattle industry is spatially expansive, with heterogeneous distributions of animals and infrastructure. We have developed a model that incorporates the effects of scale for both disease transmission and control actions, applied here in simulating FMD outbreaks in US cattle. We simulated infection initiating in each of the 3049 counties in the contiguous US, 100 times per county. When initial infection was located in specific regions, large outbreaks were more likely to occur, driven by infrastructure and other demographic attributes such as premises clustering and number of cattle on premises. Sensitivity analyses suggest these attributes had more impact on outbreak metrics than the ranges of estimated disease parameter values. Additionally, although shipping accounted for a small percentage of overall transmission, areas receiving the most animal shipments tended to have other attributes that increase the probability of large outbreaks. The importance of including spatial and demographic heterogeneity in modelling outbreak trajectories and control actions is illustrated by specific regions consistently producing larger outbreaks than others.

Integration of computational tools, data analysis and social science into food safety risk assessment

“…Sensitivity analysis (SA) can be used to evaluate how robust risk assessment and management strategies are, by assessing how variation of a model output can be attributed to variations in the different input factors (Tsao et al., 2019, Feyissa et al., 2012, Pianosi et al., 2016, Carlucci et al., 1999). This can be used for the ranking of the input factors, screening the input factors in order to identify which have a negligible influence on the output and for mapping the region of the input variability space that produces significant output values.…”

Multi-scale dynamics of infectious diseases

“…Currently, the field is most rich in the molecular (RNA) and meta-population scales depicted in figure 1. As research begins to link scales starting from the smallest as in Orton et al [31] and largest as in Tsao et al [16], we expect the two research processes to overlap and will be intrigued to see if they produce similar findings.…”

“…This cross-protection has some counterintuitive population-level implications: control aiming at reducing exposure to L. monocytogenes may decrease population immunity level against L. monocytogenes, increasing the incidence of listeriosis, the clinical manifestation of L. monocytogenes infection. By using mechanistic models, Stout et al [42] explore the implications of pathogen-host interactions at the population scale much like Tsao et al [16] and Garira & Chirove [35].…”

“…These studies typically use data from past FMD outbreaks and laboratory experiments to parametrize models that predict disease spread at farm to regional and national scales, and evaluate interventions aimed at limiting transmission and the risk of major epidemics [7,[12][13][14][15]. The paper by Tsao et al [16] exemplifies this type of work, using simulation models to examine how size and spatial characteristics of cattle farms in the USA (location, clustering, connectivity with other farms via shipments) affect the likelihood of spread of FMDV from a given index farm. They find that the likelihood of a large outbreak is far more sensitive to spatial attributes of index farms and counties than to epidemiological parameters describing disease transmission.…”

“…Concordant with the importance of spatial parameters, they find that the most effective control is achieved through interventions that identify and target farms that are tightly connected with infected farms (owing to traffic between premises, rather than mere spatial proximity). As such, Tsao et al's study [16] highlights the importance of network structure, including node characteristics (e.g. farm size, clustering) and edge distribution (e.g.…”