Evaluating infectious disease surveillance on data-driven multilayer networks

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.