Title:	LivEpiMod
DNr:	NAISS 2024/5-74
Project Type:	NAISS Medium Compute
Principal Investigator:	Tom Lindström <tom.lindstrom@liu.se>
Affiliation:	Linköpings universitet
Duration:	2024-03-27 – 2025-04-01
Classification:	10699
Keywords:

Abstract

This project for Livestock Epidemiological Modeling (LivEpiMod) focuses on transboundary animal diseases (TADs), which are a major threat to agricultural system potentially affecting food security and economy. Many tools used to understand potential disease spread and the effect of response actions inherently require an underlying model. We have developed three models that we currently work with: the Animal Movement Model (AMM); the Disease Outbreak Simulation (DOS); and a specialized model for exploring bovine tuberculosis (bTB) in Michigan, U.S. Together, AMM and DOS provide rigorous, quantitative predictions about livestock shipment and the spread, size, duration, and spatial risk of TAD outbreaks at the national level for planning and response purposes. Our team has significant expertise and previous successes with livestock shipment and TAD modeling. AMM is a Bayesian hierarchical model and uses incomplete samples of cattle shipments in a Markov Chain Monte Carlo (MCMC) algorithm to predict the complete cattle shipment networks in space and time at the national scale. DOS is a model for simulating TADs in nation-wide agricultural systems on the level of the individual premises or herd. It takes two transmission routes into account: long range due to shipments informed by AMM and local spread using a spatially implicit, density-dependent kernel parameterized from published disease outbreak data. Our bTB model is a Bayesian hierarchical state-space model that fits parameters of a within-herd model of the spread of bTB to multi-year test data while taking long-term population dynamics of the herds into account. The model is informed using data from a certain subset of the cattle farm population of Michigan where bTB is a large problem. We continuously work on refining and expanding the models to include new features, specific scenarios, additional data and the like in order to facilitate more detailed risk assessment, application to a wider range of diseases, a wider range of disease models. We propose to demonstrate the utility of AMM by using its predictions to determine the geographic areas that ultimately feed into slaughter surveillance and to recommend allocation of slaughter surveillance for improved geographic coverage. This information can inform surveillance plans to maximize early or first detection as well as improving surveillance for endemic diseases. We use sensitivity analysis to address likely variation from published parameters. We extend DOS to take inputs from AMM and further to incorporate within-premises dynamics in order to test their importance at the national scale. With the bTB model we will determine test sensitivity and specificity for the diagnostics procedures used in the bTB eradication program in the U.S. bTB is notoriously difficult to diagnose, as there is no gold standard for determining infection. This also means that accurately determining the sensitivity and specificity of diagnostic tests is nontrivial and requires a sophisticated modeling approach.