Vermont Complex Systems Center, University of Vermont , Burlington, VT, USA.
Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili , Tarragona 43007, Spain.
Proc Biol Sci. 2024 Aug;291(2028):20241117. doi: 10.1098/rspb.2024.1117. Epub 2024 Aug 14.
Epidemic models study the spread of undesired agents through populations, be it infectious diseases through a country, misinformation in social media or pests infesting a region. In combating these epidemics, we rely neither on global top-down interventions, nor solely on individual adaptations. Instead, interventions commonly come from local institutions such as public health departments, moderation teams on social media platforms or other forms of group governance. Classic models, which are often individual or agent-based, are ill-suited to capture local adaptations. We leverage developments of institutional dynamics based on cultural group selection to study how groups attempt local control of an epidemic by taking inspiration from the successes and failures of other groups. Incorporating institutional changes into epidemic dynamics reveals paradoxes: a higher transmission rate can result in smaller outbreaks as does decreasing the speed of institutional adaptation. When groups perceive a contagion as more worrisome, they can invest in improved policies and, if they maintain these policies long enough to have impact, lead to a reduction in endemicity. By looking at the interplay between the speed of institutions and the transmission rate of the contagions, we find rich coevolutionary dynamics that reflect the complexity of known biological and social contagions.
疫情模型研究不受欢迎的因素在人群中的传播,无论是传染病在一个国家的传播、社交媒体上的错误信息还是害虫在一个地区的滋生。在应对这些疫情时,我们既不依赖于全球自上而下的干预,也不单纯依赖于个人的适应。相反,干预措施通常来自于地方机构,如公共卫生部门、社交媒体平台上的 moderation 团队或其他形式的团体治理。经典模型通常是基于个体或代理的,不适合捕捉地方适应性。我们利用基于文化群体选择的机构动态发展来研究群体如何通过借鉴其他群体的成功和失败来尝试对疫情进行局部控制。将机构变化纳入疫情动态揭示了悖论:更高的传播率会导致较小的疫情爆发,降低机构适应速度也会导致较小的疫情爆发。当群体认为传染病更令人担忧时,他们可以投资改进政策,如果他们能够长期坚持这些政策并产生影响,就会导致地方性疾病的减少。通过观察机构速度和传染病传播率之间的相互作用,我们发现了丰富的共同进化动态,反映了已知生物和社会传染病的复杂性。