Epidemic mitigation by statistical inference from contact tracing data

Abstract

Contact-tracing is an essential tool in order to mitigate the impact of pandemic such as the COVID-19. In order to achieve efficient and scalable contact-tracing in real time, digital devices can play an important role. While a lot of attention has been paid to analyzing the privacy and ethical risks of the associated mobile applications, so far much less research has been devoted to optimizing their performance and assessing their impact on the mitigation of the epidemic. We develop Bayesian inference methods to estimate the risk that an individual is infected. This inference is based on the list of his recent contacts and their own risk levels, as well as personal information such as results of tests or presence of syndromes. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our results show that in some range of epidemic spreading (typically when the manual tracing of all contacts of infected people becomes practically impossible, but before the fraction of infected people reaches the scale where a lock-down becomes unavoidable), this inference of individuals at risk could be an efficient way to mitigate the epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact. Such communication may be encrypted and anonymized and thus compatible with privacy preserving standards. We conclude that probabilistic risk estimation is capable to enhance performance of digital contact tracing and should be considered in the currently developed mobile applications.

21 pages, 7 figures

References in corpus (4)

• PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing
• Dynamic message-passing equations for models with unidirectional dynamics
• The zero-patient problem with noisy observations
• COVI White Paper

Cited by in corpus (11)

• A Bayesian generative neural network framework for epidemic inference problems
• Impact of presymptomatic transmission on epidemic spreading in contact networks: A dynamic message-passing analysis
• Predicting Infectiousness for Proactive Contact Tracing
• Bayes-optimal inference for spreading processes on random networks
• Inference in conditioned dynamics through causality restoration
• Statistical Mechanics of Inference in Epidemic Spreading
• Effectiveness of probabilistic contact tracing in epidemic containment: the role of super-spreaders and transmission path reconstruction
• Small-Coupling Dynamic Cavity: a Bayesian mean-field framework for epidemic inference
• Stochasticity of infectious outbreaks and consequences for optimal interventions
• A data-driven analysis of the impact of non-compliant individuals on epidemic diffusion in urban settings
• Inference in Spreading Processes with Neural-Network Priors