数据驱动多层网络上传染病监测策略的效果评估

Evaluating infectious disease surveillance on data-driven multilayer networks

摘要

当前传染病监测策略的效果评估研究主要基于单层网络建模，未能充分考虑真实接触模式的多层性以及传染病的流行病学特征。该文采用包含症前感染与无症状状态的机制模型刻画疾病状态转变，并用真实感染数据估计模型参数，开展了基于数据驱动多层网络上的传染病监测策略研究。从早期时滞、波峰时延、波峰比率3个维度评估不同监测策略的效果，同时提出了兼顾接触权重与流行病学特征的集体影响策略。结果表明，经典策略在数据驱动多层网络上仅能提前约3天感知疫情，而考虑网络结构的策略可将感知时间提升至7天。相比之下，集体影响策略能提前约14天感知疫情，且该策略受监测子集规模影响较小，具有鲁棒性。

Abstract

Current infectious disease surveillance studies often overlook the multi-layered nature of real-world contacts and disease characteristics. This paper presents a data-driven, multi-layer network approach that models disease transitions including pre-symptomatic and asymptomatic states and uses real infection data to estimate model parameters. The effectiveness of different monitoring strategies is evaluated from three dimensions of early latency, peak delay, and peak ratio. Subsequently, a collective influence strategy that takes into account both contact weights and epidemiological features is proposed. Findings show that traditional strategies can detect outbreaks about 3 days earlier, the strategies accounting for network structure can advance this to 7 days. In contrast, our proposed collective influence strategy can predict outbreaks up to 14 days in advance, demonstrating robustness to the size of the surveillance subset.

关键词

数据驱动 / 疾病监测 / 多层复杂网络 / 集体影响

Key words

data-driven / disease surveillance / multilayer complex network / collective influence

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基金资助

国家自然科学基金(62373264)

国家自然科学基金(T2293771)

四川省自然科学基金(2025ZNSFSC0473)