Impact of the representation of contact data on the evaluation of interventions in infectious diseases simulations (preprint)

Computational models offer a unique setting to test strategies to mitigate infectious diseases’ spread, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency: many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high resolution contact data, we use data representations ranging from very detailed to very coarse to inform a model for the spread of SARS-CoV-2 and simulate several mitigation strategies. We find that coarse data representations underestimate the risk of super-spreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs that may trigger the adoption of protocols, as these may depend on data representation.

Minimizing school disruption under high incidence conditions due to the Omicron variant in early 2022 (preprint)

As record cases due to the Omicron variant are currently registered in Europe, schools remain a vulnerable setting suffering large disruption. Extending previous modeling of SARS-CoV-2 transmission in schools in France, we estimate that at high incidence rates reactive screening protocols (as currently applied in France) require comparable test resources as weekly screening (as currently applied in some Swiss cantons), for considerably lower control. Our findings can be used to define incidence levels triggering school protocols and optimizing their cost-effectiveness.

Self-testing and vaccination against COVID-19 to minimize school closure (preprint)

Schools were largely closed in 2020-2021 to counter COVID-19 spread, impacting students education and well-being. With highly contagious variants expanding in Europe while vaccine hesitancy persists, safe options to maintain schools open are urgently needed. We developed an agent-based model of SARS-CoV-2 transmission in school. We used empirical contact data measured in a primary and a secondary school in France, and field estimates for adherence to screening from 683 schools during the spring 2021 wave. Examining different screening protocols, we performed a cost-benefit analysis for varying epidemic conditions and vaccination scenarios. In a partially immunized school population, weekly screening would reduce the number of cases on average by 24% in the primary and 53% in the secondary school compared to symptom-based testing alone, if R=1.3 and 50% adhered to screening. This adherence was met in primary schools (53% (95% confidence interval 21-85%)), but insufficient participation was recorded in secondary schools (10% (1-38%) in middle schools, 6% (2-12%) in high schools). Regular screening would also reduce by 90% the number of student-days lost compared to reactive class closure. No difference was predicted when fully vaccinating teachers, due to their limited number and mixing. Partially vaccinating adolescents would still require regular screening for additional control (20% case reduction with 50% vaccinated students). In the upcoming fall, COVID-19 epidemic will likely continue to pose a risk to the safe opening of schools. Increasing vaccination coverage in adolescents and implementing regular testing while largely incentivizing adherence are essential steps to keep schools open.

Anatomy of digital contact tracing: role of age, transmission setting, adoption and case detection (preprint)

The efficacy of digital contact tracing against COVID-19 epidemic is debated: smartphone penetration is limited in many countries, non-uniform across age groups, with low coverage among elderly, the most vulnerable to SARS-CoV-2. We developed an agent-based model to precise the impact of digital contact tracing and household isolation on COVID-19 transmission. The model, calibrated on French population, integrates demographic, contact-survey and epidemiological information to describe the risk factors for exposure and transmission of COVID-19. We explored realistic levels of case detection, app adoption, population immunity and transmissibility. Assuming a reproductive ratio R = 2.6 and 50% detection of clinical cases, a ~20% app adoption reduces peak incidence by ~35%. With R = 1.7, >30% app adoption lowers the epidemic to manageable levels. Higher coverage among adults, playing a central role in COVID-19 transmission, yields an indirect benefit for elderly. These results may inform the inclusion of digital contact tracing within a COVID-19 response plan.

Effect of manual and digital contact tracing on COVID-19 outbreaks: a study on empirical contact data (preprint)

In the fight against the COVID-19 pandemic, lockdowns have succeeded in limiting contagions in many countries, at however heavy societal costs: more targeted non-pharmaceutical interventions are desirable to contain or mitigate potential resurgences. Contact tracing, by identifying and quarantining people who have been in prolonged contact with an infectious individual, has the potential to stop the spread where and when it occurs, with thus limited impact. The limitations of manual contact tracing (MCT), due to delays and imperfect recall of contacts, might be compensated by digital contact tracing (DCT) based on smartphone apps, whose impact however depends on the app adoption. To assess the efficiency of such interventions in realistic settings, we use here datasets describing contacts between individuals in several contexts, with high spatial and temporal resolution, to feed numerical simulations of a compartmental model for COVID-19. We find that the obtained reduction of epidemic size has a robust behavior: this benefit is linear in the fraction of contacts recalled during MCT, and quadratic in the app adoption, with no threshold effect. The combination of tracing strategies can yield important benefits, and the cost (number of quarantines) vs. benefit curve has a typical parabolic shape, independent on the type of tracing, with a high benefit and low cost if app adoption and MCT efficiency are high enough. Our numerical results are qualitatively confirmed by analytical results on simplified models. These results may inform the inclusion of MCT and DCT within COVID-19 response plans.

Digital Proximity Tracing in the COVID-19 Pandemic on Empirical Contact Networks (preprint)

Digital contact tracing is increasingly considered as a tool to control infectious disease outbreaks. As part of a broader test, trace, isolate, and quarantine strategy, digital contract tracing apps have been proposed to alleviate lock-downs, and to return societies to a more normal situation in the ongoing COVID-19 crisis. Early work evaluating digital contact tracing did not consider important features and heterogeneities present in real-world contact patterns which impact epidemic dynamics. Here, we fill this gap by considering a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing apps in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread of COVID-19 in realistic scenarios such as a university campus, a workplace, or a high school. We find that restrictive policies are more effective in confining the epidemics but come at the cost of quarantining a large part of the population. It is possible to avoid this effect by considering less strict policies, which only consider contacts with longer exposure and at shorter distance to be at risk. Our results also show that isolation and tracing can help keep re-emerging outbreaks under control provided that hygiene and social distancing measures limit the reproductive number to 1.5. Moreover, we confirm that a high level of app adoption is crucial to make digital contact tracing an effective measure. Our results may inform app-based contact tracing efforts currently being implemented across several countries worldwide.

Using real-world contact networks to quantify theeffectiveness of digital contact tracing and isolation strategies for Covid-19 pandemic (preprint)

Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15-20 minutes and closer than 2-3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

Decentralized Privacy-Preserving Proximity Tracing (preprint)

This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale. This system, referred to as DP3T, provides a technological foundation to help slow the spread of SARS-CoV-2 by simplifying and accelerating the process of notifying people who might have been exposed to the virus so that they can take appropriate measures to break its transmission chain. The system aims to minimise privacy and security risks for individuals and communities and guarantee the highest level of data protection. The goal of our proximity tracing system is to determine who has been in close physical proximity to a COVID-19 positive person and thus exposed to the virus, without revealing the contact's identity or where the contact occurred. To achieve this goal, users run a smartphone app that continually broadcasts an ephemeral, pseudo-random ID representing the user's phone and also records the pseudo-random IDs observed from smartphones in close proximity. When a patient is diagnosed with COVID-19, she can upload pseudo-random IDs previously broadcast from her phone to a central server. Prior to the upload, all data remains exclusively on the user's phone. Other users' apps can use data from the server to locally estimate whether the device's owner was exposed to the virus through close-range physical proximity to a COVID-19 positive person who has uploaded their data. In case the app detects a high risk, it will inform the user.