利用网络信息提高疫情防控效率的疫苗接种策略。

Efficient vaccination strategies for epidemic control using network information.

机构信息

Department of Biostatistics, Harvard T.H. Chan School of Public Health, United States.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, United States; Institute for Global Health, University College London, United Kingdom; Africa Health Research Institute, KwaZulu-Natal, South Africa.

出版信息

Epidemics. 2019 Jun;27:115-122. doi: 10.1016/j.epidem.2019.03.002. Epub 2019 Mar 6.

Abstract

BACKGROUND

Network-based interventions against epidemic spread are most powerful when the full network structure is known. However, in practice, resource constraints require decisions to be made based on partial network information. We investigated how the accuracy of network data available at individual and village levels affected network-based vaccination effectiveness.

METHODS

We simulated a Susceptible-Infected-Recovered process on static empirical social networks from 75 rural Indian villages. First, we used regression analysis to predict the percentage of individuals ever infected (cumulative incidence) based on village-level network properties for simulated datasets from 10 representative villages. Second, we simulated vaccinating 10% of each of the 75 empirical village networks at baseline, selecting vaccinees through one of five network-based approaches: random individuals (Random); random contacts of random individuals (Nomination); random high-degree individuals (High Degree); highest degree individuals (Highest Degree); or most central individuals (Central). The first three approaches require only sample data; the latter two require full network data. We also simulated imposing a limit on how many contacts an individual can nominate (Fixed Choice Design, FCD), which reduces the data collection burden but generates only partially observed networks.

RESULTS

In regression analysis, we found mean and standard deviation of the degree distribution to strongly predict cumulative incidence. In simulations, the Nomination method reduced cumulative incidence by one-sixth compared to Random vaccination; full network methods reduced infection by two-thirds. The High Degree approach had intermediate effectiveness. Somewhat surprisingly, FCD truncating individuals' degrees at three was as effective as using complete networks.

CONCLUSIONS

Using even partial network information to prioritize vaccines at either the village or individual level, i.e. determine the optimal order of communities or individuals within each village, substantially improved epidemic outcomes. Such approaches may be feasible and effective in outbreak settings, and full ascertainment of network structure may not be required.

摘要

背景

当完全了解网络结构时,基于网络的干预措施对控制疫情传播最为有效。然而,在实践中,资源限制要求在部分网络信息的基础上做出决策。我们研究了个体和村庄层面可获得的网络数据的准确性如何影响基于网络的疫苗接种效果。

方法

我们在 75 个印度农村社区的静态经验社会网络上模拟了一个易感染-感染-恢复的过程。首先,我们使用回归分析根据 10 个有代表性的村庄的模拟数据集,预测基于村庄层面网络特征的个体曾经感染的百分比(累积发病率)。其次,我们在基线时模拟对 75 个经验社区网络中的每一个网络接种 10%的疫苗,通过五种基于网络的方法之一选择疫苗接种者:随机个体(Random);随机个体的随机接触者(Nomination);随机高度数个体(High Degree);最高度数个体(Highest Degree);或最中心个体(Central)。前三种方法仅需要样本数据;后两种方法需要完整的网络数据。我们还模拟了对个体可以提名的接触人数施加限制(固定选择设计,FCD),这减少了数据收集负担,但只生成部分观察到的网络。

结果

在回归分析中,我们发现度分布的平均值和标准差强烈预测累积发病率。在模拟中,与随机接种相比,Nomination 方法将累积发病率降低了六分之一;全网络方法将感染率降低了三分之二。High Degree 方法的效果介于两者之间。有些出人意料的是,将个体的度数截断为三个的 FCD 与使用完整网络一样有效。

结论

即使使用部分网络信息,例如在村庄或个体层面确定疫苗接种的优先级,即确定每个村庄内的最佳社区或个体顺序,也可以大大改善疫情结果。这些方法在疫情爆发的情况下可能是可行且有效的,而且可能不需要完全确定网络结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e44b/6677279/ca5aafffba3c/nihms-1530575-f0001.jpg

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