Inferring Spatial Source of Disease Outbreaks using Maximum Entropy

TL;DR

This paper introduces a maximum entropy framework for inferring the spatial origin of disease outbreaks, which is robust to noisy data and effective even at advanced stages of an epidemic.

Contribution

The proposed maximum entropy method provides a flexible, noise-robust approach for inferring disease origins and trajectories, outperforming existing models especially with sparse data.

Findings

Accurately predicts future disease spread in synthetic and real networks.

Infers outbreak origin with high confidence even at advanced epidemic stages.

Demonstrates robustness to noisy and incomplete data.

Abstract

Mathematical modeling of disease outbreaks can infer the future trajectory of an epidemic, which can inform policy decisions. Another task is inferring the origin of a disease, which is relatively difficult with current mathematical models. Such frameworks -- across varying levels of complexity -- are typically sensitive to input data on epidemic parameters, case-counts and mortality rates, which are generally noisy and incomplete. To alleviate these limitations, we propose a maximum entropy framework that fits epidemiological models, provides a calibrated infection origin probabilities, and is robust to noise due to a prior belief model. Maximum entropy is agnostic to the parameters or model structure used and allows for flexible use when faced with sparse data conditions and incomplete knowledge in the dynamical phase of disease-spread, providing for more reliable modeling at early stages of outbreaks. We evaluate the performance of our model by predicting future disease trajectories in synthetic graph networks and the real mobility network of New York state. In addition, unlike existing approaches, we demonstrate that the method can be used to infer the origin of the outbreak with accurate confidence. Indeed, despite the prevalent belief on the feasibility of contact-tracing being limited to the initial stages of an outbreak, we report the possibility of reconstructing early disease dynamics, including the epidemic seed, at advanced stages.