Predicting epidemic evolution on contact networks from partial observations

TL;DR

This paper demonstrates that Belief Propagation can effectively predict the future course of an epidemic from partial early observations on contact networks, outperforming Monte Carlo sampling in various network types.

Contribution

It extends the application of Belief Propagation to epidemic forecasting from partial data, showing its advantages over traditional sampling methods.

Findings

Belief Propagation provides more accurate epidemic predictions than Monte Carlo sampling.

Prediction quality depends on the type and amount of observed data.

The method performs well on both synthetic and real-world contact networks.

Abstract

The massive employment of computational models in network epidemiology calls for the development of improved inference methods for epidemic forecast. For simple compartment models, such as the Susceptible-Infected-Recovered model, Belief Propagation was proved to be a reliable and efficient method to identify the origin of an observed epidemics. Here we show that the same method can be applied to predict the future evolution of an epidemic outbreak from partial observations at the early stage of the dynamics. The results obtained using Belief Propagation are compared with Monte Carlo direct sampling in the case of SIR model on random (regular and power-law) graphs for different observation methods and on an example of real-world contact network. Belief Propagation gives in general a better prediction that direct sampling, although the quality of the prediction depends on the quantity under study (e.g. marginals of individual states, epidemic size, extinction-time distribution) and on the actual number of observed nodes that are infected before the observation time.