Climate change affects biophysical processes related to the transmission of many infectious diseases, with potentially adverse consequences for the health of communities. While our knowledge of biophysical associations between meteorological factors and disease is steadily improving, our understanding of the social processes that shape adaptation to environmental perturbations lags behind. Using computational modeling methods, we explore the ways in which social cohesion can affect adaptation of disease prevention strategies when communities are exposed to different environmental scenarios that influence transmission pathways for diseases such as diarrhea. We developed an agent-based model in which household agents can choose between two behavioral strategies that offer different levels of protection against environmentally mediated disease transmission. One behavioral strategy is initially set as more protective, leading households to adopt it widely, but its efficacy is sensitive to variable weather conditions and stressors such as floods or droughts that modify the disease transmission system. The efficacy of the second strategy is initially moderate relative to the first and is insensitive to environmental changes. We examined how social cohesion (defined as average number of household social network connections) influences health outcomes when households attempt to identify an optimal strategy by copying the behaviors of socially connected neighbors who seem to have adapted successfully in the past. Our simulation experiments suggest that high-cohesion communities are able to rapidly disseminate the initially optimal behavioral strategy compared to low-cohesion communities. This rapid and pervasive change, however, decreases behavioral diversity; i.e., once a high cohesion community settles on a strategy, most or all households adopt that behavior. Following environmental changes that reduce the efficacy of the initially optimal strategy, rendering it suboptimal relative to the alternative strategy, high-cohesion communities can fail to adapt. As a result, despite faring better early in the course of computational experiments, high-cohesion communities may ultimately experience worse outcomes. In the face of uncertainty in predicting future environmental stressors due to climate change, strategies to improve effective adaptation to optimal disease prevention strategies should balance between intervention efforts that promote protective behaviors based on current scientific understanding and the need to guard against the crystallization of inflexible norms. Developing generalizable models allows us to integrate a wide range of theories and multiple datasets pertaining to the relationship between social mechanisms and adaptation, which can provide further understanding of future climate change impacts. Models such as the one we present can generate hypotheses about the mechanisms that underlie the dynamics of adaptation events and suggest specific points of measurement to assess the impact of these mechanisms. They can be incorporated as modules within predictive simulations for specific socio-ecological contexts.

气候变化影响与许多传染病传播相关的生物物理过程，可能对社区健康造成不利后果。虽然我们对气象因素与疾病之间的生物物理关联的了解正在稳步提高，但我们对影响适应环境扰动的社会过程的理解却滞后。使用计算建模方法，我们探索当社区暴露于影响腹泻等疾病传播途径的不同环境情景时，社会凝聚力如何影响疾病预防策略的适应。我们开发了一种基于代理的模型，其中家庭代理可以在两种行为策略之间进行选择，这两种策略可以提供不同程度的保护，防止环境介导的疾病传播。一种行为策略最初被设定为更具保护性，导致家庭广泛采用它，但其功效对多变的天气条件和压力源（例如改变疾病传播系统的洪水或干旱）敏感。相对于第一种策略，第二种策略的功效最初是中等的，并且对环境变化不敏感。我们研究了当家庭试图通过复制过去似乎成功适应的社会联系邻居的行为来确定最佳策略时，社会凝聚力（定义为家庭社交网络连接的平均数量）如何影响健康结果。我们的模拟实验表明，与低凝聚力社区相比，高凝聚力社区能够快速传播最初的最佳行为策略。然而，这种快速而普遍的变化降低了行为多样性； IE，一旦一个高凝聚力的社区确定了一项策略，大多数或所有家庭都会采取这种行为。随着环境变化降低了最初最优策略的功效，使其相对于替代策略而言不是最优的，高凝聚力社区可能无法适应。因此，尽管在计算实验过程的早期表现较好，但高凝聚力社区最终可能会经历更糟糕的结果。面对预测气候变化造成的未来环境压力的不确定性，提高对最佳疾病预防策略的有效适应的策略应在基于当前科学认识促进保护行为的干预措施与防止僵化规范结晶的需要之间取得平衡。开发可推广的模型使我们能够整合与社会机制和适应之间的关系有关的广泛理论和多个数据集，这可以进一步了解未来气候变化的影响。我们提出的模型可以生成关于适应事件动态机制的假设，并提出具体的测量点来评估这些机制的影响。它们可以作为模块纳入特定社会生态环境的预测模拟中。