Abstract
Human behavioral change around biosecurity in response to increased awareness of disease risks is a critical factor in modeling animal disease dynamics. Here, biosecurity is referred to as implementing control measures to decrease the chance of animal disease spreading. However, social dynamics are largely ignored in traditional livestock disease models. Not accounting for these dynamics may lead to substantial bias in the predicted epidemic trajectory. In this research, an agent-based model is developed by integrating the human decision-making process into epidemiological processes. We simulate human behavioral change on biosecurity practices following an increase in the regional disease incidence. We apply the model to beef cattle production systems in southwest Kansas, United States, to examine the impact of human behavior factors on a hypothetical foot-and-mouth disease outbreak. The simulation results indicate that heterogeneity of individuals regarding risk attitudes significantly affects the epidemic dynamics, and human-behavior factors need to be considered for improved epidemic forecasting. With the same initial biosecurity status, increasing the percentage of risk-averse producers in the total population using a targeted strategy can more effectively reduce the number of infected producer locations and cattle losses compared to a random strategy. In addition, the reduction in epidemic size caused by the shifting of producers’ risk attitudes towards risk-aversion is heavily dependent on the initial biosecurity level. A comprehensive investigation of the initial biosecurity status is recommended to inform risk communication strategy design.
Highlights
Industrial livestock production is characterized by intensive and high-throughput systems, with all parts of the chain from birth to slaughter always operating at full capacity
We present and compares the simulation results among the three simulation sets to evaluate the impact of risk attitudes on epidemic dynamics
An agent-based model was developed to simulate the human decision-making process during epidemics, a factor which is often ignored in conventional livestock disease models
Summary
Industrial livestock production is characterized by intensive and high-throughput systems, with all parts of the chain from birth to slaughter always operating at full capacity. Disruptions that initially occur at one part of the chain can immediately impact both upstream and downstream aspects due to entities’ demand-supply relationships. Such impacts could be amplified and propagated throughout the chain [1, 2]. The ongoing COVID-19 pandemic has dramatically impacted people’s normal activities and left significant economic consequences to industries, among which the livestock production industries are one the most negatively impacted sectors [3,4,5]. Peel et al [6] suggested that COVID-19 could result in $13.6 billion economic losses to the US beef industry, highlighting the fragility of today’s intensive livestock production industries against unexpected events
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