A Bayesian generative neural network framework for epidemic inference problems

TL;DR

This paper introduces a Bayesian generative neural network framework designed to infer missing epidemic information on contact networks, improving accuracy and computational efficiency in critical epidemic inference tasks.

Contribution

The paper presents a novel neural network approach that learns to generate probable infection cascades, offering a general, Bayesian, and variational framework for epidemic inference.

Findings

Achieves better or comparable results to existing methods.

Effective in synthetic and real contact networks.

Suitable for small and medium-sized epidemic scenarios.

Abstract

The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies. The identification and warning of infectious but asymptomatic individuals (i.e., contact tracing), the well-known patient-zero problem, or the inference of the infectivity values in structured populations are examples of significant epidemic inference problems. As the number of possible epidemic cascades grows exponentially with the number of individuals involved and only an almost negligible subset of them is compatible with the observations (e.g., medical tests), epidemic inference in contact networks poses incredible computational challenges. We present a new generative neural networks framework that learns to generate the most probable infection cascades compatible with observations. The proposed method achieves better (in some cases, significantly better) or comparable results with existing methods in all problems considered both in synthetic and real contact networks. Given its generality, clear Bayesian and variational nature, the presented framework paves the way to solve fundamental inference epidemic problems with high precision in small and medium-sized real case scenarios such as the spread of infections in workplaces and hospitals.