Risk Attitudes Affect Livestock Biosecurity Decisions With Ramifications for Disease Control in a Simulated Production System

Abstract

Hog producers' operational decisions can be informed by an awareness of risks associated with emergent and endemic diseases. Outbreaks of porcine epidemic diarrhea virus (PEDv) have been re-occurring every year since the first onset in 2013 with substantial losses across the hog production supply chain. Interestingly, a decreasing trend in PEDv incidence is visible. We assert that changes in human behaviors may underlie this trend. Disease prevention using biosecurity practices is used to minimize risk of infection but its efficacy is conditional on human behavior and risk attitude. Standard epidemiological models bring important insights into disease dynamics but have limited predictive ability. Since research shows that human behavior plays a driving role in the disease spread process, the explicit inclusion of human behavior into models adds an important dimension to understanding disease spread. Here we analyze PEDv incidence emerging from an agent-based model (ABM) that uses both epidemiological dynamics and algorithms that incorporate heterogeneous human decisions. We investigate the effects of shifting fractions of hog producers between risk tolerant and risk averse positions. These shifts affect the dynamics describing willingness to increase biosecurity as a response to disease threats and, indirectly, change infection probabilities and the resultant intensity and impact of the disease outbreak. Our ABM generates empirically verifiable patterns of PEDv transmission. Scenario results show that relatively small shifts (10% of the producer agents) toward a risk averse position can lead to a significant decrease in total incidence. For significantly steeper decreases in disease incidence, the model's hog producer population needed at least 37.5% of risk averse. Our study provides insight into the link between risk attitude, decisions related to biosecurity, and consequent spread of disease within a livestock production system. We suggest that it is possible to create positive, lasting changes in animal health by nudging the population of livestock producers toward more risk averse behaviors. We make a case for integrating social and epidemiological aspects in disease spread models to test intervention strategies intended to improve biosecurity and animal health at the system scale.

Keywords: agent-based models; biosecurity; disease transmission; hog production; human behavior; porcine epidemic diarrhea virus (PEDv); risk attitude.

Figure 1

Time series of the number of confirmed new PEDv positive premises by week. Gray bars report data for the U.S. and green bars for the state of North Carolina (NC). The data are available for the period 06/01/2014 to 02/25/2018. Issued on June 5, 2014, a Federal Order required the reporting of swine enteric coronavirus diseases including PEDv (https://www.aasv.org/aasv%20website/Resources/Diseases/PorcineEpidemicDiarrhea.php). On March 6, 2018, USDA rescinded the Federal Order (https://www.aphis.usda.gov/aphis/newsroom/news/sa_by_date/sa-2018/secd-reporting). The dark green line traces the decreasing trend in incidence in NC with a slope m = −0.02. This is equivalent to an average decrease from about 20 new cases in the month June, 2014 down to eight new cases in the same month in 2017.

Figure 2

Agent-based model (ABM) process flow. It highlights the ABM's main components and processes of how the Porcine Epidemic Diarrhea virus (PEDv) can spread through the network structure of the swine industry and is influenced by human behavior. The ABM structural component mimics the swine industry with three types of agents: P, producer; FM, feed mill; and SP, slaughter plant. Agents interact via networks of hog and feed movement. The ABM epidemiological component simulates the risk of PEDv transmission associated with movement through these network connections disease spreads. Human decisions on biosecurity also influence infection risk. Disease spread depends on the probability of disease transmission on the networks and influences the biosecurity level on farms.

Figure 3

Box-plot of the distributions of total PEDv incidence (sum of new infection cases over the simulated time period) for each scenario. Each scenario represents a different distribution of risk attitudes within the population of producer agents in our ABM. The baseline-scenario population has equal proportions of producer agents in the all four groups (risk averse, risk opportunistic, risk neutral, risk tolerant). Three scenarios (12.5, 17.5, and 22.5% averse) tested the effect of reducing the number of risk averse producers by shifting a fraction (10, 30, or 50%) of producer agents from the risk averse to the risk tolerant category and are color coded with red shades. The other three scenarios (27.5, 32.5, and 37.5% averse) tested the effect of increasing the number of risk averse producers by shifting a fraction (10, 30, or 50%) of producer agents from the risk tolerant to the risk averse category and are color coded with blue shades. Each scenario distribution is drawn from a Monte Carlo experiment with 800 replicates. The compact letter display indicates significance from pairwise comparison. For the scenarios sharing a letter there is no evidence of a difference for that pair of distributions at adjusted α* = 0.002 level (Bonferroni adjustment for 21 comparisons). The black dashed line marks the total incidence in the observed data.

Figure 4

Box-plot of the intercept distributions derived from the PEDv incidence trends for each scenario. Description details as in Figure 3.

Figure 5

Box-plot of the slope distributions derived from the PEDv incidence trends for each scenario. Description details as in Figure 3.

Figure 6

Model results for PEDv incidence for the seven risk attitude scenarios (Table 1). Observed PEDv Incidence and its linear trend are overlaid in green. (A) Time series of averaged simulated PEDv incidence (lines) and one-standard-deviation bands derived from the 800 Monte Carlo runs for each scenario. (B) Zoom on simulated outputs with overlaid trends obtained from averaged linear regressions on each simulation run. The green line represents the linear trend of the observed data. The other colors are the same as described in the legend of the top panel.