Impact of Pre-symptomatic Transmission on Epidemic Spreading in Contact Networks

Infectious diseases that incorporate pre-symptomatic transmission are challenging to monitor, model, predict and contain. We address this scenario by studying a variant of stochastic susceptible-exposed-infected-recovered (SEIR) 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 at a low computational cost compared to numerical simulation. 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. Our studies show quantitatively the deficiency of using the reproduction number as a measure for predicting the spreading rate in some topologies, how effective isolation reduces the need in strict social distancing measures, and the importance of mass testing in scenarios with long asymptomatic exposure since isolation of symptomatic individuals is insufficient to mitigate the spread.