Data-driven prediction and origin identification of epidemics in population networks

TL;DR

This paper presents a Bayesian framework for predicting epidemics and identifying their origins in population networks, leveraging parallel computing to handle complex models with limited noisy data.

Contribution

It introduces a novel Bayesian uncertainty quantification method for epidemic prediction and origin detection that is scalable with parallel computing architectures.

Findings

Effective model fitting with noisy data

Successful identification of epidemic origins

Framework applicable to real-world epidemic management

Abstract

Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics.