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      Drivers of epidemic dynamics in real time from daily digital COVID-19 measurements

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          Abstract

          Understanding the drivers of respiratory pathogen spread is challenging, particularly in a timely manner during an ongoing epidemic. Here we present insights obtained using daily data from the NHS COVID-19 app for England and Wales and shared with health authorities in almost real time. Our indicator of the reproduction number R(t) was available days earlier than other estimates, with a novel capability to decompose R(t) into contact rates and probabilities of infection. When Omicron arrived, the main epidemic driver switched from contacts to transmissibility. We separate contacts and transmissions by day of exposure and setting, finding pronounced variability over days of the week and during Christmas holidays and events. As an example, during the Euro football tournament in 2021, days with England matches showed sharp spikes in exposures and transmissibility. Digital contact tracing technologies can help control epidemics not only by directly preventing transmissions but also by enabling rapid analysis at scale and with unprecedented resolution.

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          Welcome to the Tidyverse

          Hadley Wickham, Mara Averick, Jennifer Bryan … (2019)
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            Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

            Luca Ferretti, Chris Wymant, Michelle L. Kendall … (2020)
            The newly emergent human virus SARS-CoV-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. We conclude that viral spread is too fast to be contained by manual contact tracing, but could be controlled if this process was faster, more efficient and happened at scale. A contact-tracing App which 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 need for mass quarantines (‘lock-downs’) that are harmful to society. We discuss the ethical requirements for an intervention of this kind.
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              Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study

              Adam Kucharski, Petra Klepac, Andrew Conlan … (2020)
              Summary Background The isolation of symptomatic cases and tracing of contacts has been used as an early COVID-19 containment measure in many countries, with additional physical distancing measures also introduced as outbreaks have grown. To maintain control of infection while also reducing disruption to populations, there is a need to understand what combination of measures—including novel digital tracing approaches and less intensive physical distancing—might be required to reduce transmission. We aimed to estimate the reduction in transmission under different control measures across settings and how many contacts would be quarantined per day in different strategies for a given level of symptomatic case incidence. Methods For this mathematical modelling study, we used a model of individual-level transmission stratified by setting (household, work, school, or other) based on BBC Pandemic data from 40 162 UK participants. We simulated the effect of a range of different testing, isolation, tracing, and physical distancing scenarios. Under optimistic but plausible assumptions, we estimated reduction in the effective reproduction number and the number of contacts that would be newly quarantined each day under different strategies. Results We estimated that combined isolation and tracing strategies would reduce transmission more than mass testing or self-isolation alone: mean transmission reduction of 2% for mass random testing of 5% of the population each week, 29% for self-isolation alone of symptomatic cases within the household, 35% for self-isolation alone outside the household, 37% for self-isolation plus household quarantine, 64% for self-isolation and household quarantine with the addition of manual contact tracing of all contacts, 57% with the addition of manual tracing of acquaintances only, and 47% with the addition of app-based tracing only. If limits were placed on gatherings outside of home, school, or work, then manual contact tracing of acquaintances alone could have an effect on transmission reduction similar to that of detailed contact tracing. In a scenario where 1000 new symptomatic cases that met the definition to trigger contact tracing occurred per day, we estimated that, in most contact tracing strategies, 15 000–41 000 contacts would be newly quarantined each day. Interpretation Consistent with previous modelling studies and country-specific COVID-19 responses to date, our analysis estimated that a high proportion of cases would need to self-isolate and a high proportion of their contacts to be successfully traced to ensure an effective reproduction number lower than 1 in the absence of other measures. If combined with moderate physical distancing measures, self-isolation and contact tracing would be more likely to achieve control of severe acute respiratory syndrome coronavirus 2 transmission. Funding Wellcome Trust, UK Engineering and Physical Sciences Research Council, European Commission, Royal Society, Medical Research Council.
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                Author and article information

                Contributors
                Michelle Kendall: (View ORCID Profile)
                Luca Ferretti: (View ORCID Profile)
                Chris Wymant: (View ORCID Profile)
                Daphne Tsallis: (View ORCID Profile)
                James Petrie: (View ORCID Profile)
                Francesco Di Lauro: (View ORCID Profile)
                Lucie Abeler-Dörner: (View ORCID Profile)
                Harrison Manley: (View ORCID Profile)
                Jasmina Panovska-Griffiths: (View ORCID Profile)
                Alice Ledda: (View ORCID Profile)
                Xavier Didelot: (View ORCID Profile)
                Christophe Fraser: (View ORCID Profile)
                Journal
                Title: Science
                Abbreviated Title: Science
                Publisher: American Association for the Advancement of Science (AAAS)
                ISSN (Print): 0036-8075
                ISSN (Electronic): 1095-9203
                Publication date (Electronic): July 11 2024
                Affiliations
                [1 ]Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.
                [2 ]Pandemic Sciences Institute, Nuffield Department for Medicine, University of Oxford, Old Road Campus, Oxford OX3 7DQ, UK.
                [3 ]Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, Old Road Campus, University of Oxford, Oxford OX3 7LF, UK.
                [4 ]Zühlke Engineering Ltd., 80 Great Eastern Street, London EC2A 3JL, UK.
                [5 ]UK Health Security Agency, Nobel House, 17 Smith Square, London SW1P 3JR, UK.
                [6 ]School of Life Sciences, University of Warwick, Coventry CV4 7AL, UK.
                Article
                DOI: 10.1126/science.adm8103
                SO-VID: 0e1a1670-18eb-4c3b-ad93-012ef887d36a
                Copyright © © 2024
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