Local dynamical survival analysis and human sensor networks for epidemic tracking and forecasting

Abstract

Predictive modelling for infectious diseases remains an important problem in epidemiology. Current approaches are often limited by their dependence on time-series data, which can lead to biased estimates of key epidemiological parameters. Here, we present an extension of the dynamical survival analysis approach that can accommodate epidemic data from a large-scale, complex, agent-based model. We use localized predictions that allow for flexibility with time-dependent parameters to account for human behaviour and environmental factors during the epidemic. Our results demonstrate that it is possible to evaluate the quality of predictions and highlight the potential benefits of surveillance via longitudinal follow-up of individuals sampled from the population, which we call a human sensor network.