Infectious diseases that incorporate presymptomatic transmission are challenging to monitor, model, predict, and contain. We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered model on arbitrary network instances using an analytical framework based on the method of dynamic message passing. This framework provides a good estimate of the probabilistic evolution of the spread on both static and contact networks, offering a significantly improved accuracy with respect to individual-based mean-field approaches while requiring a much lower computational cost compared to numerical simulations. It facilitates the derivation of epidemic thresholds, which are phase boundaries separating parameter regimes where infections can be effectively contained from those where they cannot. These have clear implications on different containment strategies through topological (reducing contacts) and infection parameter changes (e.g., social distancing and wearing face masks), with relevance to the recent COVID-19 pandemic.
Bibliographical note©2021 American Physical Society
Funding: B.L. and D.S. acknowledge support from European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No.~835913. D.S. acknowledges support from the EPSRC program grant TRANSNET (EP/R035342/1).
- epidemic spreading
- contact networks
- dynamic message-passing
- presymptomatic transmission
- epidemic threshold
- nonbacktracking centrality