Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

By L. Ferretti, C. Wymant, M. Kendall, Lele Zhao, A. Nurtay, L. Abeler-Dörner, Michael Parker, D. Bonsall, Christophe Fraser. Science, 2020

2,454 citations recorded by the source corpus.

Instantaneous contact tracing New analyses indicate that severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) is more infectious and less virulent than the earlier SARS-CoV-1, which emerged in China in 2002. Unfortunately, the current virus has greater epidemic potential because it is difficult to trace mild or presymptomatic infections. As no treatment is currently available, the only tools that we can currently deploy to stop the epidemic are contact tracing, social distancing, and quarantine, all of which are slow to implement. However imperfect the data, the current global emergency requires more timely interventions. Ferretti et al. explored the feasibility of protecting the population (that is, achieving transmission below the basic reproduction number) using isolation coupled with classical contact tracing by questionnaires versus algorithmic instantaneous contact tracing assisted by a mobile phone application. For prevention, the crucial information is understanding the relative contributions of different routes of transmission. A phone app could show how finite resources must be divided between different intervention strategies for the most effective control. Science, this issue p. eabb6936 Instantaneous contact tracing and notifications by mobile phone app could potentially stop the COVID-19 epidemic. INTRODUCTION Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2), has clear potential for a long-lasting global pandemic, high fatality rates, and incapacitated health systems. Until vaccines are widely available, the only available infection prevention approaches are case isolation, contact tracing and quarantine, physical distancing, decontamination, and hygiene measures. To implement the right measures at the right time, it is of crucial importance to understand the routes and timings of transmission. RATIONALE We used key parameters of epidemic spread to estimate the contribution of different transmission routes with a renewal equation formulation, and analytically determined the speed and scale for effective identification and contact tracing required to stop the epidemic. RESULTS We developed a mathematical model for infectiousness to estimate the basic reproductive number R0 and to quantify the contribution of different transmission routes. To parameterize the model, we analyzed 40 well-characterized source-recipient pairs and estimated the distribution of generation times (time from infection to onward transmission). The distribution had a median of 5.0 days and standard deviation of 1.9 days. We used published parameters for the incubation time distribution (median 5.2 days) and the epidemic doubling time (5.0 days) from the early epidemic data in China. The model estimated R0 = 2.0 in the early stages of the epidemic in China. The contributions to R0 included 46% from presymptomatic individuals (before showing symptoms), 38% from symptomatic individuals, 10% from asymptomatic individuals (who never show symptoms), and 6% from environmentally mediated transmission via contamination. Results on the last two routes are speculative. According to these estimates, presymptomatic transmissions alone are almost sufficient to sustain epidemic growth. To estimate the requirements for successful contact tracing, we determined the combination of two key parameters needed to reduce R0 to less than 1: the proportion of cases who need to be isolated, and the proportion of their contacts who need to be quarantined. For a 3-day delay in notification assumed for manual contact tracing, no parameter combination leads to epidemic control. Immediate notification through a contact-tracing mobile phone app could, however, be sufficient to stop the epidemic if used by a sufficiently high proportion of the population. We propose an app, based on existing technology, that allows instant contact tracing. Proximity events between two phones running the app are recorded. Upon an individual’s COVID-19 diagnosis, contacts are instantly, automatically, and anonymously notified of their risk and asked to self-isolate. Practical and logistical factors (e.g., uptake, coverage, R0 in a given population) will determine whether an app is sufficient to control viral spread on its own, or whether additional measures to reduce R0 (e.g., physical distancing) are required. The performance of the app in scenarios with higher values of R0 can be explored at https://bdi-pathogens.shinyapps.io/covid-19-transmission-routes/. CONCLUSION Given the infectiousness of SARS-CoV-2 and the high proportion of transmissions from presymptomatic individuals, controlling the epidemic by manual contact tracing is infeasible. The use of a contact-tracing app that builds a memory of proximity contacts and immediately notifies contacts of positive cases would be sufficient to stop the epidemic if used by enough people, in particular when combined with other measures such as physical distancing. An intervention of this kind raises ethical questions regarding access, transparency, the protection and use of personal data, and the sharing of knowledge with other countries. Careful oversight by an inclusive advisory body is required. Instant contact tracing can reduce the proportion of cases that need to be isolated and contacts who need to be quarantined to achieve control of an epidemic. Subject A becomes symptomatic after having had contact with other people in different settings the day before. Contacts are notified and quarantined where needed. In the inset, the green area indicates the success rates needed to control an epidemic with R0 = 2 (i.e., negative growth rates after isolating cases and quarantining their contacts). The newly emergent human virus SARS-CoV-2 (severe acute respiratory syndrome–coronavirus 2) is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact tracing needed to stop the epidemic. Although SARS-CoV-2 is spreading too fast to be contained by manual contact tracing, it could be controlled if this process were faster, more efficient, and happened at scale. A contact-tracing app that builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without resorting to mass quarantines (“lockdowns”) that are harmful to society. We discuss the ethical requirements for an intervention of this kind.

Subfields: Information Systems, Modeling and Simulation, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/31fb4ea7a135c545c8d79e78b72a02a4ebf32e61

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Citations in the corpus

Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study (2020, background): “For this reason, and also in light of the predictable wide adoption of mobile apps for contact tracing [14], which will likely increase the demand for population screening, there is an urgent need for alternative (or complementary) testing”
Covasim: An agent-based model of COVID-19 dynamics and interventions (2020, neutral): “We take a default assumption that it is not, but include a parameter that can be modified as needed depending on the modeling application or context, noting that some studies have used much lower rates of infectiousness for asymptomatic individuals (61).”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, background): “Another recent study [86] concluded that COVID-19’s “spread is too fast to be contained by manual contact tracing”.”
The Impact of the COVID-19 Pandemic on Consumption: Learning from High-Frequency Transaction Data (2020, neutral): “…aspect in perspective, the epidemiology literature is far from reaching a conclusion about the transmission mode of this novel coronavirus (Ferretti et al., 2020; Wong, Leo, and Tan, 2020), the spread rate (Li et al., 2020; Wu, Leung, and Leung, 2020), morbidity and mortality (Li et al.,…”
Identifying airborne transmission as the dominant route for the spread of COVID-19 (2020, neutral): “Differentiation of the effects of those mitigation measures in China is challenging (19), since the implementation occurred almost simultaneously in January 2020.”
Occurrence and transmission potential of asymptomatic and presymptomatic SARS-CoV-2 infections: A living systematic review and meta-analysis (2020, positive): “We included eight mathematical modelling studies (Fig 4) [19,20,33,51,63,69,78,91].”
Wearable sensor data and self-reported symptoms for COVID-19 detection (2020, background): “The early identification of symptomatic and pre-symptomatic infected individuals would be especially valuable as transmission is common and people may potentially be even more infectious during this period 19–21 .”
Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature (2020, background): “A study by Ferretti et al [35] analyzed the key parameters of the COVID-19 epidemic spread to estimate the contributions of different transmission routes and determine the requirements for successful case isolation and contact tracing.”
The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology (2020, background): “By enabling rapid tracing of perhaps higher proportions of affected individuals, these apps can reduce the amount of time that a potentially infected person would have to infect others, particularly in asymptomatic or pre-symptomatic phases of infection 46 .”