AUTHOR=Hidano Arata , Enticott Gareth , Christley Robert M. , Gates M. Carolyn 

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

JOURNAL=Frontiers in Veterinary Science

VOLUME=Volume 5 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/veterinary-science/articles/10.3389/fvets.2018.00137

DOI=10.3389/fvets.2018.00137

ISSN=2297-1769

ABSTRACT=Over the past several decades, infectious disease modelling 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 behaviours remain constant over time. As many studies have shown, livestock owners change their behaviours 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 summarise the current knowledge about farmers’ behavioural change in on-farm biosecurity and livestock trading practices and highlight the knowledge gaps that prohibit these behavioural changes from being incorporated into disease modelling frameworks. We suggest this knowledge gap can be filled only by more empirical longitudinal studies on farmers’ behavioural change as well as theoretical modelling studies that can help to identify human behavioural changes that are important in disease transmission dynamics. Moreover, we contend it is time to shift our research approach: from modelling a single disease to modelling interactions between multiple diseases and from modelling a single farmer behaviour to modelling interdependencies between multiple behaviours.  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.