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      Inference of the SARS-CoV-2 generation time using UK household data

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          Abstract

          The distribution of the generation time (the interval between individuals becoming infected and transmitting the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to data from the early months of the COVID-19 pandemic (December 2019-March 2020) compared to existing models (Hart et al., 2021). However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March to November 2020 in the UK, we provide updated estimates of the generation time. We considered both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% credible interval 3.3–5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0–8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (5.9 days, 5.2–7.0 days) and a similar standard deviation (4.8 days, 4.0–6.3 days). As well as estimating the generation time using data from the entire study period, we also considered whether the generation time varied temporally. Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, further data collection and analysis is necessary to continue to monitor ongoing transmission and inform future public health policy decisions.

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          Temporal dynamics in viral shedding and transmissibility of COVID-19

          We report temporal patterns of viral shedding in 94 patients with laboratory-confirmed COVID-19 and modeled COVID-19 infectiousness profiles from a separate sample of 77 infector-infectee transmission pairs. We observed the highest viral load in throat swabs at the time of symptom onset, and inferred that infectiousness peaked on or before symptom onset. We estimated that 44% (95% confidence interval, 25-69%) of secondary cases were infected during the index cases' presymptomatic stage, in settings with substantial household clustering, active case finding and quarantine outside the home. Disease control measures should be adjusted to account for probable substantial presymptomatic transmission.
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            Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England

            Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has the capacity to generate variants with major genomic changes. The UK variant B.1.1.7 (also known as VOC 202012/01) has many mutations that alter virus attachment and entry into human cells. Using a variety of statistical and dynamic modeling approaches, Davies et al. characterized the spread of the B.1.1.7 variant in the United Kingdom. The authors found that the variant is 43 to 90% more transmissible than the predecessor lineage but saw no clear evidence for a change in disease severity, although enhanced transmission will lead to higher incidence and more hospital admissions. Large resurgences of the virus are likely to occur after the easing of control measures, and it may be necessary to greatly accelerate vaccine roll-out to control the epidemic. Science , this issue p. eabg3055 The major coronavirus variant that emerged at the end of 2020 in the UK is more transmissible than its predecessors and could spark resurgences. INTRODUCTION Several novel variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, emerged in late 2020. One of these, Variant of Concern (VOC) 202012/01 (lineage B.1.1.7), was first detected in southeast England in September 2020 and spread to become the dominant lineage in the United Kingdom in just a few months. B.1.1.7 has since spread to at least 114 countries worldwide. RATIONALE The rapid spread of VOC 202012/01 suggests that it transmits more efficiently from person to person than preexisting variants of SARS-CoV-2. This could lead to global surges in COVID-19 hospitalizations and deaths, so there is an urgent need to estimate how much more quickly VOC 202012/01 spreads, whether it is associated with greater or lesser severity of disease, and what control measures might be effective in mitigating its impact. We used social contact and mobility data, as well as demographic indicators linked to SARS-CoV-2 community testing data in England, to assess whether the spread of the new variant may be an artifact of higher baseline transmission rates in certain geographical areas or among specific demographic subpopulations. We then used a series of complementary statistical analyses and mathematical models to estimate the transmissibility of VOC 202012/01 across multiple datasets from the UK, Denmark, Switzerland, and the United States. Finally, we extended a mathematical model that has been extensively used to forecast COVID-19 dynamics in the UK to consider two competing SARS-CoV-2 lineages: VOC 202012/01 and preexisting variants. By fitting this model to a variety of data sources on infections, hospitalizations, and deaths across seven regions of England, we assessed different hypotheses for why the new variant appears to be spreading more quickly, estimated the severity of disease associated with the new variant, and evaluated control measures including vaccination and nonpharmaceutical interventions. Combining multiple lines of evidence allowed us to draw robust inferences. RESULTS The rapid spread of VOC 202012/01 is not an artifact of geographical differences in contact behavior and does not substantially differ by age, sex, or socioeconomic stratum. We estimate that the new variant has a 43 to 90% higher reproduction number (range of 95% credible intervals, 38 to 130%) than preexisting variants. Similar increases are observed in Denmark, Switzerland, and the United States. The most parsimonious explanation for this increase in the reproduction number is that people infected with VOC 202012/01 are more infectious than people infected with a preexisting variant, although there is also reasonable support for a longer infectious period and multiple mechanisms may be operating. Our estimates of severity are uncertain and are consistent with anything from a moderate decrease to a moderate increase in severity (e.g., 32% lower to 20% higher odds of death given infection). Nonetheless, our mathematical model, fitted to data up to 24 December 2020, predicted a large surge in COVID-19 cases and deaths in 2021, which has been borne out so far by the observed burden in England up to the end of March 2021. In the absence of stringent nonpharmaceutical interventions and an accelerated vaccine rollout, COVID-19 deaths in the first 6 months of 2021 were projected to exceed those in 2020 in England. CONCLUSION More than 98% of positive SARS-CoV-2 infections in England are now due to VOC 202012/01, and the spread of this new variant has led to a surge in COVID-19 cases and deaths. Other countries should prepare for potentially similar outcomes. Impact of SARS-CoV-2 Variant of Concern 202012/01. ( A ) Spread of VOC 202012/01 (lineage B.1.1.7) in England. ( B ) The estimated relative transmissibility of VOC 202012/01 (mean and 95% confidence interval) is similar across the United Kingdom as a whole, England, Denmark, Switzerland, and the United States. ( C ) Projected COVID-19 deaths (median and 95% confidence interval) in England, 15 December 2020 to 30 June 2021. Vaccine rollout and control measures help to mitigate the burden of VOC 202012/01. A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in September 2020 and is rapidly spreading toward fixation. Using a variety of statistical and dynamic modeling approaches, we estimate that this variant has a 43 to 90% (range of 95% credible intervals, 38 to 130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine rollout, COVID-19 hospitalizations and deaths across England in the first 6 months of 2021 were projected to exceed those in 2020. VOC 202012/01 has spread globally and exhibits a similar transmission increase (59 to 74%) in Denmark, Switzerland, and the United States.
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              Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

              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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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                09 February 2022
                2022
                09 February 2022
                : 11
                : e70767
                Affiliations
                [1 ] Mathematical Institute, University of Oxford ( https://ror.org/052gg0110) Oxford United Kingdom
                [2 ] Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine ( https://ror.org/00a0jsq62) London United Kingdom
                [3 ] Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine ( https://ror.org/00a0jsq62) London United Kingdom
                [4 ] Immunisation and Countermeasures Division, UK Health Security Agency ( https://ror.org/00vbvha87) London United Kingdom
                [5 ] Data and Analytical Sciences, UK Health Security Agency London United Kingdom
                [6 ] Mathematics Institute, University of Warwick ( https://ror.org/01a77tt86) Coventry United Kingdom
                [7 ] Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick ( https://ror.org/01a77tt86) Coventry United Kingdom
                The University of Melbourne ( https://ror.org/01ej9dk98) Australia
                McGill University ( https://ror.org/01pxwe438) Canada
                The University of Melbourne ( https://ror.org/01ej9dk98) Australia
                The University of Melbourne ( https://ror.org/01ej9dk98) Australia
                University of Edinburgh ( https://ror.org/01nrxwf90) United Kingdom
                Author notes
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-2504-6860
                https://orcid.org/0000-0001-6377-7296
                https://orcid.org/0000-0002-1884-0097
                https://orcid.org/0000-0002-0146-9164
                https://orcid.org/0000-0002-2842-3406
                https://orcid.org/0000-0001-8545-5212
                Article
                70767
                10.7554/eLife.70767
                8967386
                35138250
                430bcecb-ca5c-4259-83e1-0b7eca81e7df
                © 2022, Hart et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 29 May 2021
                : 07 February 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/R513295/1
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: NIHR200929
                Award Recipient :
                Funded by: Taisho Pharmaceutical Co., Ltd;
                Award ID: Research grant
                Award Recipient :
                Funded by: UKRI;
                Award ID: EP/V053507/1
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Advance
                Epidemiology and Global Health
                Custom metadata
                Updated estimates of the generation time of SARS-CoV-2 infections indicate that the generation time has become shorter.

                Life sciences
                sars-cov-2,covid-19,generation time,generation interval,presymptomatic transmission,mathematical modelling,viruses

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