Hidano Arata, Enticott Gareth, Christley Robert M, Gates M Carolyn
EpiCentre, School of Veterinary Science, Massey University, Palmerston North, New Zealand.
Cardiff School of Geography and Planning, Cardiff University, Cardiff, United Kingdom.
Front Vet Sci. 2018 Jun 21;5:137. doi: 10.3389/fvets.2018.00137. eCollection 2018.
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.
在过去几十年中，传染病建模已成为创建反事实情景的重要工具，通过这些情景可以在现实世界中实施不同疾病控制政策之前评估其有效性。对于家畜疾病而言，随着研究人员能够获取丰富的国家家畜可追溯性数据库，这些模型变得越来越复杂，这使得通过基于网络的方法在动物移动中纳入明确的空间和时间模式成为可能。然而，我们目前对动物疾病动态的建模仍存在许多局限性。其中关键的一点是，许多模型假设人类行为随时间保持不变。正如许多研究所表明的，家畜养殖户会根据疾病风险意识提高、符合社会期望的压力以及新的国家动物卫生法规的直接实施等复杂因素，改变他们在交易、农场生物安全和疾病管理方面的行为；所有这些因素都可能显著影响疾病在农场内部和农场之间的传播方式。未能考虑这些动态变化可能会在传染病模型中产生大量偏差，但令人惊讶的是，目前对于其对模型推断的影响知之甚少。在此，我们回顾关于这些假设为何重要的越来越多的证据。我们总结了当前关于农民在农场生物安全和家畜交易行为方面行为变化的知识，并强调了阻碍将这些行为变化纳入疾病建模框架的知识空白。我们认为，只有通过对农民行为变化进行更多的实证纵向研究以及有助于识别在疾病传播动态中重要的人类行为变化的理论建模研究，才能填补这一知识空白。此外，我们认为现在是时候转变我们的研究方法了：从对单一疾病进行建模转向对多种疾病之间的相互作用进行建模，从对单一农民行为进行建模转向对多种行为之间的相互依存关系进行建模。为了解决这些挑战，迫切需要跨动物健康、流行病学、社会学和动物福利等广泛领域的跨学科合作。
