Modeling Dynamic Human Behavioral Changes in Animal Disease Models: Challenges and Opportunities for Addressing Bias

Abstract

Over the past several decades, infectious disease modeling has become an essential tool for creating counterfactual scenarios that allow the effectiveness of different disease control policies to be evaluated prior to implementation in the real world. For livestock diseases, these models have become increasingly sophisticated as researchers have gained access to rich national livestock traceability databases, which enables inclusion of explicit spatial and temporal patterns in animal movements through network-based approaches. However, there are still many limitations in how we currently model animal disease dynamics. Critical among these is that many models make the assumption that human behaviors remain constant over time. As many studies have shown, livestock owners change their behaviors around trading, on-farm biosecurity, and disease management in response to complex factors such as increased awareness of disease risks, pressure to conform with social expectations, and the direct imposition of new national animal health regulations; all of which may significantly influence how a disease spreads within and between farms. Failing to account for these dynamics may produce a substantial layer of bias in infectious disease models, yet surprisingly little is currently known about the effects on model inferences. Here, we review the growing evidence on why these assumptions matter. We summarize the current knowledge about farmers' behavioral change in on-farm biosecurity and livestock trading practices and highlight the knowledge gaps that prohibit these behavioral changes from being incorporated into disease modeling frameworks. We suggest this knowledge gap can be filled only by more empirical longitudinal studies on farmers' behavioral change as well as theoretical modeling studies that can help to identify human behavioral changes that are important in disease transmission dynamics. Moreover, we contend it is time to shift our research approach: from modeling a single disease to modeling interactions between multiple diseases and from modeling a single farmer behavior to modeling interdependencies between multiple behaviors. In order to solve these challenges, there is a strong need for interdisciplinary collaboration across a wide range of fields including animal health, epidemiology, sociology, and animal welfare.

Poultry farmer response to disease outbreaks in smallholder farming systems in southern Vietnam

“…Additionally, it is unknown whether poultry farmers increase application of disinfection practices or vaccination rates against avian influenza in response to disease outbreaks occurring in their flocks. Changes in farm management caused by variations in epidemiological risk have not been quantified for any livestock system that we are aware of, primarily because of the lack of combined epidemiological and behavioural data in longitudinal studies of livestock disease ( Hidano et al, 2018 ). Ifft et al, 2011 compared the evolution of chicken farm sizes and disease prevention in administrative areas with different levels of HPAI prevalence in Vietnam, and Hidano and Gates, 2019 modelled the effect of cattle mortality and production performance on the frequency of sales and culling in New Zealand dairy farms.…”

Effects of Social Cues on Biosecurity Compliance in Livestock Facilities: Evidence From Experimental Simulations

“…[6] Human behavioral factors that can influence biosecurity implementation have been identified as crucial to mitigating the risk of outbreaks; although limited knowledge exists on the actual relationship between these factors and behavior. [7,8] Analysis of human-behavior can be undertaken in contexts of varying scope: strategic, tactical, and operational. [9] Taking a broad view, big picture strategic decisions are guided by long-term objectives.…”

“…These factors have been identified as important within the farmer biosecurity decision-making process. [7,31] Research has suggested that farmers in the United States exhibit risk tolerance [32], but they may be more likely to implement or comply with biosecurity as their perceived risk of infection increases [6,28,33]. Indeed, Merrill et al [30] found that as the actual infection risk within the experimental simulation increased, so did compliance with a biosecurity practice.…”

Poultry farmer response to disease outbreaks in smallholder farming systems

“…Additionally, it is unknown whether poultry farmers increase application of disinfection practices or vaccination rates against avian influenza in response to disease outbreaks occurring in their flocks. Changes in farm management caused by variations in epidemiological risk have not been quantified for any livestock system that we are aware of, primarily because of the lack of combined epidemiological and behavioural data in longitudinal studies of livestock disease (22). Ifft et al compared the evolution of chicken farm sizes and disease prevention in administrative areas with different levels of HPAI prevalence in Vietnam (23), and Hidano et al…”