Network-Based Prediction of the 2019-nCoV Epidemic Outbreak in the Chinese Province Hubei

TL;DR

This paper introduces a network-based model to predict the spread of 2019-nCoV in Hubei, using city interactions inferred from epidemic data, aiding targeted disease control.

Contribution

It presents a novel network inference method for epidemic prediction, improving accuracy over traditional models by incorporating city interaction data.

Findings

Network-based modeling improves epidemic forecast accuracy.

City interactions inferred from data are crucial for prediction.

Model successfully predicts virus prevalence in Hubei cities.

Abstract

At the moment of writing (12 February, 2020), the future evolution of the 2019-nCoV virus is unclear. Predictions of the further course of the epidemic are decisive to deploy targeted disease control measures. We consider a network-based model to describe the 2019-nCoV epidemic in the Hubei province. The network is composed of the cities in Hubei and their interactions (e.g., traffic flow). However, the precise interactions between cities is unknown and must be inferred from observing the epidemic. We propose a network-based method to predict the future prevalence of the 2019-nCoV virus in every city. Our results indicate that network-based modelling is beneficial for an accurate forecast of the epidemic outbreak.