Seibel Rachel L, Tildesley Michael J, Hill Edward M
EPSRC & MRC Centre for Doctoral Training in Mathematics for Real-World Systems, Mathematics Institute, University of Warwick, Coventry, UK.
Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, Mathematics Institute and School of Life Sciences, University of Warwick, Coventry, UK.
R Soc Open Sci. 2025 Jun 18;12(6):241964. doi: 10.1098/rsos.241964. eCollection 2025 Jun.
During infectious disease outbreaks, humans often base their decision to adhere to an intervention strategy on individual choices and opinions. However, due to data limitations and inference challenges, infectious disease models usually omit these variables. We constructed a compartmental, deterministic Susceptible-Exposed-Infectious-Recovered (SEIR) disease model that includes a behavioural function with parameters influencing intervention uptake. The behavioural function accounted for an initial subpopulation opinion towards an intervention, their outbreak information awareness sensitivity and the extent to which they are swayed by the real-time intervention effectiveness information. Applying the model to vaccination uptake and three human pathogens-pandemic influenza, SARS-CoV-2 and Ebola virus-we explored through model simulation how these intervention adherence decision parameters and behavioural heterogeneity impacted epidemiological outcomes. From our model simulations, we found that in some pathogen systems, different types of outbreak information awareness at different outbreak stages may be more informative to an information-sensitive population and may lead to less severe epidemic outcomes. Incorporating behavioural functions that modify infection control intervention adherence into epidemiological models can aid our understanding of adherence dynamics during outbreaks. Ultimately, by parameterizing models with what we know about human behaviour towards vaccination adherence, such models can help assist decision-makers during outbreaks.
在传染病爆发期间，人们往往根据个人选择和观点来决定是否坚持采取干预策略。然而，由于数据限制和推理挑战，传染病模型通常会忽略这些变量。我们构建了一个分区确定性的易感-暴露-感染-康复（SEIR）疾病模型，该模型包含一个行为函数，其参数会影响干预措施的采用情况。该行为函数考虑了初始亚群体对干预措施的看法、他们对疫情信息的知晓敏感度以及他们受实时干预效果信息影响的程度。将该模型应用于疫苗接种以及三种人类病原体——大流行性流感、严重急性呼吸综合征冠状病毒2（SARS-CoV-2）和埃博拉病毒——我们通过模型模拟探究了这些干预依从性决策参数和行为异质性如何影响流行病学结果。从我们的模型模拟中发现，在某些病原体系统中，处于不同疫情阶段的不同类型的疫情信息知晓情况，对于信息敏感人群可能更具参考价值，并且可能导致疫情后果不那么严重。将修改感染控制干预依从性的行为函数纳入流行病学模型，有助于我们理解疫情期间的依从动态。最终，通过用我们所了解的人类疫苗接种依从行为来对模型进行参数化，此类模型可以在疫情期间帮助决策者。
