Quantifying noise effects on networked epidemic transmission through structural predictability

Estimating networked transmission dynamics of infectious diseases from noisy surveillance data remains a persistent challenge in modern epidemiology. Key parameters are derived from time series data to inform policymakers about disease trends and assess public health interventions. However, the accuracy of these inferences critically hinges on reliable data sources. In this work, we established a dual equivalence between epidemic dynamics stability to noise and network structural predictability. Specifically, we have demonstrated the equivalence of epidemic dynamics stability concerning data noise and structural noise. The stability of epidemic dynamics, in the context of structural noise, can be quantified using network predictability measures. Leveraging this connection, we utilize network predictability to characterize the stability of disease spread dynamics, allowing us to assess the impact of noise on model predictions. Network predictability reflects the limit of accuracy in forecasting missing links, showcasing structural and functional traits. Existing studies lack precision, a distinct definition, and an explanation of predictability. Our approach links network dynamics stability and predictability, introducing a precise, non-training metric. By building upon the discoveries related to predictability, we can quantify the extent to which noise affects the network in the dynamics of disease transmission.