PREDICTING INFECTIOUSNESS FOR PROACTIVE CONTACT TRACING

Abstract

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) 1 has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). Similarly to other works, we find that compared to no tracing, all DCT methods tested are able to reduce spread of the disease and thus save lives, even at low adoption rates, strongly supporting a role for DCT methods in managing the pandemic. Further, we find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

1. INTRODUCTION

Until pharmaceutical interventions such as a vaccine become available, control of the COVID-19 pandemic relies on nonpharmaceutical interventions such as lockdown and social distancing. While these have often been successful in limiting spread of the disease in the short term, these restrictive measures have important negative social, mental health, and economic impacts. Digital contact tracing (DCT), a technique to track the spread of the virus among individuals in a population using smartphones, is an attractive potential solution to help reduce growth in the number of cases and thereby allow more economic and social activities to resume while keeping the number of cases low. Most currently deployed DCT solutions use binary contact tracing (BCT), which sends a quarantine recommendation to all recent contacts of a person after a positive test result. While BCT is simple and fast to deploy, and most importantly can help curb spread of the disease (Abueg et al., 2020) , epidemiological simulations by Hinch et al. (2020) suggest that using only one bit of information about the infection status can lead to quarantining many healthy individuals while failing to quarantine infectious individuals. Relying only on positive test results as a trigger is also inefficient for a number of reasons: (i) Tests have high false negative rates (Li et al., 2020) ; (ii) Tests are administered late, only after symptoms appear, leaving the asymptomatic population, estimated 20%-30% of cases (Gandhi et al., 2020) , likely untested; (iii) It is estimated that infectiousness is highest before symptoms appear, well before someone would get a test (Heneghan et al., 2020) , thus allowing them to infect others before being traced, (iv) Results typically take at least 1-2 days, and (v) In many places, tests are in limited supply. Recognizing the issues with test-based tracing, Gupta et al. ( 2020) propose a rule-based system leveraging other input clues potentially available on a smartphone (e.g. symptoms, pre-existing medical conditions), an approach they call feature-based contact tracing (FCT). Probabilistic (nonbinary) approaches, using variants of belief propagation in graphical models, e.g. (Baker et al., 2020; Satorras & Welling, 2020; Briers et al., 2020) , could also make use of features other than test results to improve over BCT, although these approaches rely on knowing the social graph, either centrally or via distributed exchanges between nodes. The latter solution may require many bits exchanged between nodes (for precise probability distributions), which is challenging both in terms of privacy and bandwidth. Building on these works, we propose a novel FCT methodology we call proactive contact tracing (PCT), in which we use the type of features proposed by Gupta et al. (2020) as inputs to a predictor trained to output proactive (before current-day) estimates of expected infectiousness (i.e. risk of having infected others in the past and of infecting them in the future). The challenges of privacy and bandwidth motivated our particular form of distributed inference where we pretrain the predictor offline and do not assume that the messages exchanged are probability distributions, but instead just informative inputs to the node-level predictor of infectiousness. We use a recently proposed COVID-19 agent-based simulation testbed (Gupta et al., 2020) called COVI-AgentSim to compare PCT to other contact tracing methods under a wide variety of conditions. We develop deep learning predictors for PCT in concert with a professional app-development company, ensuring inference models are appropriate for legacy smartphones. By leveraging the rich individual-level data produced by COVI-AgentSim to train predictors offline, we are able to perform individual-level infectiousness predictions locally to the smartphone, with sensitive personal data never required to leave the device. We find deep learning based methods to be consistently able to reduce the spread of the disease more effectively, at lower cost to mobility, and at lower adoption rates than other predictors. These results suggest that deep learning enabled PCT could be deployed in a smartphone app to help produce a better trade-off between the spread of the virus and the economic cost of mobility constraints than other DCT methods, while enforcing strong privacy constraints. 



All bolded terms are defined in the Glossary; Appendix 1. *, †, ‡ Equal contributions, alphabetically sorted; a Mila, Québec; b University of Oxford; c Max-Planck Institute for Intelligent Systems Tübingen; d University of Ottawa; e McGill University; f Université de Montreal; g École Polytechnique de Montreal; h The Alan Turing Institute; i HEC Montréal



1.1 SUMMARY OF TECHNICAL CONTRIBUTIONS 1. We examine the consequential problem of COVID-19 infectiousness prediction and propose a new method for contact tracing, called proactive contact tracing (see Sec. 2). 2. In order to perform distributed inference with deep learning models, we develop an architectural scaffold whose core is any set-based neural network. We embed two recently proposed networks, namely Deep Sets (Zaheer et al., 2017) and Set Transformers (Lee et al., 2018) and evaluate the resulting models via the COVI-AgentSim testbed (Gupta et al., 2020) (see Sec. 3.1). 3. To our knowledge the combination of techniques in this pipeline is entirely novel, and of potential interest in other settings where privacy, safety, and domain shift are of concern. Our training pipeline consists of training an ML infectiousness predictor on the domain-randomized output of an agent-based epidemiological model, in several loops of retraining to mitigate issues with (i) non-stationarity and (ii) distributional shift due to predictions made by one phone influencing the input for the predictions of other phones. (see Sec. 3.2) 4. To our knowledge this is the first work to apply and benchmark a deep learning approach for probabilistic contact tracing and infectiousness risk assessment. We find such models are able to leverage weak signals and patterns in noisy, heterogeneous data to better estimate infectiousness compared to binary contact tracing and rule-based methods (see Sec. 4)

