Tuschhoff Beth M, Kennedy David A
Department of Biology and the Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
J Theor Biol. 2025 Aug 21;611:112186. doi: 10.1016/j.jtbi.2025.112186. Epub 2025 Jun 17.
While it is well established that host heterogeneity in transmission and host heterogeneity in susceptibility each individually impact disease dynamics in characteristic ways, it is generally unknown how disease dynamics are impacted when both types of heterogeneity are simultaneously present. Here we explore this question. We first conducted a systematic review of published studies from which we determined that the effects of correlations have been drastically understudied. We then filled in the knowledge gaps by developing and analyzing a stochastic, individual-based SIR model that includes both heterogeneity in transmission and susceptibility and flexibly allows for positive, negative, or zero correlations between transmissibility and susceptibility. We found that in comparison to the uncorrelated case, positive correlations result in major epidemics that are larger, faster, and more likely, whereas negative correlations result in major epidemics that are smaller and less likely. We additionally found that, counter to the conventional wisdom that heterogeneity in susceptibility always reduces outbreak size, heterogeneity in susceptibility can lead to major epidemics that are larger and more likely than the homogeneous case when correlations between transmissibility and susceptibility are positive, but this effect only arises at small to moderate R. Moreover, positive correlations can frequently lead to major epidemics even with subcritical R. To illustrate the potential importance of heterogeneity and correlations, we developed an SEIR model to describe mpox disease dynamics in New York City, demonstrating that the dynamics of a 2022 outbreak can be reasonably well explained by the presence of positive correlations between susceptibility and transmissibility. Ultimately, we show that correlations between transmissibility and susceptibility profoundly impact disease dynamics.
虽然已经充分确定，传播中的宿主异质性和易感性中的宿主异质性各自以独特的方式影响疾病动态，但通常不清楚当两种类型的异质性同时存在时疾病动态会受到怎样的影响。在此我们探讨这个问题。我们首先对已发表的研究进行了系统综述，从中确定相关性的影响一直被严重忽视。然后我们通过开发和分析一个基于个体的随机SIR模型来填补知识空白，该模型既包括传播中的异质性又包括易感性，并且灵活地允许传播性和易感性之间存在正相关、负相关或零相关。我们发现，与不相关的情况相比，正相关会导致规模更大、速度更快且更有可能发生的重大疫情，而负相关会导致规模较小且可能性较低的重大疫情。我们还发现，与易感性异质性总是会减小疫情规模的传统观念相反，当传播性和易感性之间为正相关时，易感性异质性会导致比同质情况规模更大且更有可能发生的重大疫情，但这种效应仅在R值较小到中等时出现。此外，即使R值低于临界值，正相关也经常会导致重大疫情。为了说明异质性和相关性的潜在重要性，我们开发了一个SEIR模型来描述纽约市的猴痘疾病动态，证明2022年疫情的动态可以通过易感性和传播性之间的正相关得到合理的解释。最终，我们表明传播性和易感性之间的相关性对疾病动态有深远影响。
