Does Size Matter to Models? Exploring the Effect of Herd Size on Outputs of a Herd-Level Disease Spread Simulator

Patterns of foot-and-mouth disease virus detection in environmental samples in an endemic setting

Foot-and-Mouth Disease virus (FMDV) is endemic in several regions and is a virus that can persist in the environment dependent on pH, relative humidity (RH), temperature, and matrix (i.e., soil, water, or air). Our previously published analysis of available viral persistence data showed that persistence is likely affected by interactions between RH, temperature, and matrix. Understanding these relationships will aid efforts to eliminate FMD, which has significant impacts on economies and food security. In Cameroon, West Africa, the livestock system consists of mobile (transhumant), transboundary trade and sedentary herds. Studying this system can provide information about the patterns of environmental detection of FMDV RNA that may influence approaches to virus elimination on premises during an outbreak. To improve our understanding of these patterns, we collected samples from individuals, vehicles, and along cattle pathways at three sedentary herds beginning on day one of owner-reported outbreaks, ending by day 30, and tested for the presence of FMD viral RNA using rRT-PCR. Our analysis suggests that detection decreases in soil surface samples with increased distance from herd and time from the first report of disease. Whereas time but not distance decreases detection in air samples. Interaction of RH and temperature suggests increased detection at high temperatures (>24°C) and RH (>75%), providing us with new information about the patterns of FMD viral RNA detection in and around cattle herds that could help to inform targeted virus elimination strategies, such as location and application of disinfectants.

Development of a transboundary model of livestock disease in Europe

Epidemiological models of notifiable livestock disease are typically framed at a national level and targeted for specific diseases. There are inherent difficulties in extending models beyond national borders as details of the livestock population, production systems and marketing systems of neighbouring countries are not always readily available. It can also be a challenge to capture heterogeneities in production systems, control policies, and response resourcing across multiple countries, in a single transboundary model.

In this paper, we describe EuFMDiS, a continental‐scale modelling framework for transboundary animal disease, specifically designed to support emergency animal disease planning in Europe. EuFMDiS simulates the spread of livestock disease within and between countries and allows control policies to be enacted and resourced on a per‐country basis. It provides a sophisticated decision support tool that can be used to look at the risk of disease introduction, establishment and spread; control approaches in terms of effectiveness and costs; resource management; and post‐outbreak management issues.

Modelling the domestic poultry population in the United States: A novel approach leveraging remote sensing and synthetic data methods

Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.

Challenges to the Application of Spatially Explicit Stochastic Simulation Models for Foot-and-Mouth Disease Control in Endemic Settings: A Systematic Review

Simulation modeling has become common for estimating the spread of highly contagious animal diseases. Several models have been developed to mimic the spread of foot-and-mouth disease (FMD) in specific regions or countries, conduct risk assessment, analyze outbreaks using historical data or hypothetical scenarios, assist in policy decisions during epidemics, formulate preparedness plans, and evaluate economic impacts. Majority of the available FMD simulation models were designed for and applied in disease-free countries, while there has been limited use of such models in FMD endemic countries. This paper's objective was to report the findings from a study conducted to review the existing published original research literature on spatially explicit stochastic simulation (SESS) models of FMD spread, focusing on assessing these models for their potential use in endemic settings. The goal was to identify the specific components of endemic FMD needed to adapt these SESS models for their potential application in FMD endemic settings. This systematic review followed the PRISMA guidelines, and three databases were searched, which resulted in 1176 citations. Eighty citations finally met the inclusion criteria and were included in the qualitative synthesis, identifying nine unique SESS models. These SESS models were assessed for their potential application in endemic settings. The assessed SESS models can be adapted for use in FMD endemic countries by modifying the underlying code to include multiple cocirculating serotypes, routine prophylactic vaccination (RPV), and livestock population dynamics to more realistically mimic the endemic characteristics of FMD. The application of SESS models in endemic settings will help evaluate strategies for FMD control, which will improve livestock health, provide economic gains for producers, help alleviate poverty and hunger, and will complement efforts to achieve the Sustainable Development Goals.

Environmental persistence of foot-and-mouth disease virus applied to endemic regions

The consequences of foot-and-mouth disease impact regional economies and food security through animal mortality and morbidity, trade restrictions and burdens to veterinary infrastructure. Despite efforts to control the disease, some regions, mostly in warmer climates, persistently report disease outbreaks. Consequently, it is necessary to understand how environmental factors influence transmission, of this economically devastating disease. Extensive research covers basic aetiology and transmission potential of livestock and livestock products for foot-and-mouth disease virus (FMDV), with a subset evaluating environmental survival. However, this subset, completed in the early to mid-20th century in Northern Europe and the United States, is not easily generalized to today's endemic locations. This review uncovered 20 studies, to assess current knowledge and analyse the effects of environmental variables on FMDV survival, using a Cox proportional hazards (Coxph) model. However, the dataset is limited, for example pH was included in three studies and only five studies reported both relative humidity (RH) and temperature. After dropping pH from the analysis, our results suggest that temperature alone does not describe FMDV survival; instead, interactions between RH and temperature have broader impacts across various conditions. For instance, FMDV is expected to survive longer during the wet season (survival at day 50 is ~90% at 16°C and 86% RH) versus the dry season (survival at day 50 approaches 0% at 16°C and 37.5% RH) or comparatively in the UK versus the Southwestern United States. Additionally, survival on vegetation topped 70% on day 75 when conditions exceeded 20°C with high RH (86%), drastically higher than the survival on inanimate surfaces at the same temperature and RH (~0%). This is important in tropical regions, where high temperatures can persist throughout the year, but RH varies. Therefore, parameter estimates, for disease modelling and control in endemic areas, require environmental survival data from a wider range of conditions.

Why sold, not culled? Analysing farm and animal characteristics associated with livestock selling practices

Livestock disease simulation models that incorporate animal movements often assume (1) that farmers' livestock trading practices remain consistent over time in future, (2) that animals sold to other farms are chosen randomly from a herd, and (3) that the animals' fate on the destination farm is not influenced by their past production and movement histories. The objective of this study was to assess the extent to which these assumptions are violated in the real world using records from a national database in New Zealand that captures both milk production and movement data for individual dairy cattle. All individual animal milk test records from 2006 through 2010 were extracted from the database and processed to generate different animal and herd level variables including cow demographics, previous movement history, milk volume, and milk composition (somatic cell counts (SCC), protein percentage, and fat percentage). Various statistical models were used to explore factors associated with farms' selling practice and characteristics of animals being sold. The results showed farms' livestock selling practices were highly influenced by both external factors such as market milk price and internal factors such as previous year's cow mortality and how long farms had been in business. Higher milk price increased both the number of cows being sold and the number of farms selling cows. Compared with cows that remained in the herd at the end of lactation, cows sold to other farms had lower fat and protein percentages, but similar milk volumes and SCCs. Cows that had been sold more often in the past were more likely to be sold after controlling for the effects of age. Overall, these findings highlight the potential need for disease simulation models to account for dynamics in selling practices and animal characteristics when determining which animals will be sold to other herds.