Enns Eva A, Brandeau Margaret L
Division of Health Policy and Management, University of Minnesota School of Public Health, 420 Delaware St. SE, MMC 729, Minneapolis, MN 55455, USA.
Department of Management Science and Engineering, Stanford University, 475 Via Ortega, Stanford, CA 94305, USA.
J Theor Biol. 2015 Apr 21;371:154-65. doi: 10.1016/j.jtbi.2015.02.005. Epub 2015 Feb 16.
For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two "preventive" approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two "reactive" approaches (S-I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdös-Rényi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S-I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S-I edge centrality algorithm is a function of both network structure (higher for unstructured Erdös-Rényi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing which nodes are initially infected by comparing the performance improvement achieved by reactive over preventive strategies. We find that such information is most valuable for moderate budget levels, with increasing value as disease spread becomes more likely (due to either increased connectedness of the network or increased infectiousness of the disease).
对于许多传染病而言，了解疾病传播所经由的潜在接触网络对于确定适当的控制措施至关重要。当行为改变是疾病预防的主要干预手段时，了解如何利用有限资源最佳地改变网络连通性以控制疾病传播就显得尤为重要。我们描述并比较了四种从网络中选择有限数量链路进行移除的算法：两种“预防性”方法（边中心性、R0最小化），其中移除哪些链路的决策在任何疾病爆发之前做出，且仅取决于网络结构；以及两种“反应性”方法（S - I边中心性、最优隔离），其中节点的初始疾病状态信息被纳入移除哪些链路的决策中。我们评估了这些算法在最小化急性疾病爆发过程中发生的感染总数方面的性能。我们考虑了不同的网络结构，包括具有不同连通性水平的静态和动态厄多斯 - 雷尼随机网络、通过注射吸毒连接的住宅酒店的真实世界网络以及呈现社区结构的网络。我们表明，反应性方法在避免感染方面优于预防性方法。在反应性方法中，当链路移除预算较小时，按S - I边中心性顺序移除链路更受青睐，而当链路移除预算足够大时，最优隔离表现最佳。最优隔离优于S - I边中心性算法的预算阈值是网络结构（与具有社区结构的网络或真实世界网络相比，非结构化厄多斯 - 雷尼随机网络的该阈值更高）和疾病传染性（高传染性疾病的该阈值更低）两者的函数。我们通过比较反应性策略与预防性策略所实现的性能提升，对知晓哪些节点最初被感染进行了信息价值分析。我们发现，此类信息对于中等预算水平最为有价值，并且随着疾病传播可能性增加（由于网络连通性增加或疾病传染性增加）其价值也会增加。
