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      Key questions for modelling COVID-19 exit strategies

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      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 3 , 3 , 17 , 18 , 19 , 1 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 16 , 3 , 33 , 19 , 33 , 34 , 35 , 15 , 36 , 37 , 23 , 9 , 13 , 38 , 39
      Proceedings of the Royal Society B: Biological Sciences
      The Royal Society
      COVID-19, SARS-CoV-2, exit strategy, mathematical modelling, epidemic control, uncertainty

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          Abstract

          Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute ‘Models for an exit strategy’ workshop (11–15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

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          Most cited references163

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          Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

          Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
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            Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

            Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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              An interactive web-based dashboard to track COVID-19 in real time

              In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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                Author and article information

                Journal
                Proc Biol Sci
                Proc Biol Sci
                RSPB
                royprsb
                Proceedings of the Royal Society B: Biological Sciences
                The Royal Society
                0962-8452
                1471-2954
                12 August 2020
                12 August 2020
                12 August 2020
                : 287
                : 1932
                : 20201405
                Affiliations
                [1 ]Mathematical Institute, University of Oxford , Woodstock Road, Oxford OX2 6GG, UK
                [2 ]Christ Church, University of Oxford , St Aldates, Oxford OX1 1DP, UK
                [3 ]Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine , Keppel Street, London WC1E 7HT, UK
                [4 ]Big Data Institute, University of Oxford , Old Road Campus, Oxford OX3 7LF, UK
                [5 ]Department of Statistical Science, University College London , Gower Street, London WC1E 6BT, UK
                [6 ]School of Environmental Sciences, University of Liverpool , Brownlow Street, Liverpool L3 5DA, UK
                [7 ]The Alan Turing Institute, British Library , 96 Euston Road, London NW1 2DB, UK
                [8 ]Department of Mathematical Sciences, University of Bath , North Road, Bath BA2 7AY, UK
                [9 ]Department of Mathematics, Stockholm University , Kräftriket, 106 91 Stockholm, Sweden
                [10 ]College of Engineering, Mathematical and Physical Sciences, University of Exeter , Exeter EX4 4QE, UK
                [11 ]Department of Plant Sciences, University of Oxford , South Parks Road, Oxford OX1 3RB, UK
                [12 ]Saw Swee Hock School of Public Health, National University of Singapore , 12 Science Drive, Singapore 117549, Singapore
                [13 ]Department of Plant Sciences, University of Cambridge , Downing Street, Cambridge CB2 3EA, UK
                [14 ]Statistical Laboratory, University of Cambridge , Wilberforce Road, Cambridge CB3 0WB, UK
                [15 ]Department of Statistics, University of Oxford , St Giles', Oxford OX1 3LB, UK
                [16 ]MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, London W2 1PG, UK
                [17 ]Department of Sociology, University of Surrey , Stag Hill, Guildford GU2 7XH, UK
                [18 ]Department of Mathematics, University of Manchester , Oxford Road, Manchester M13 9PL, UK
                [19 ]Centre for Mathematical Sciences, University of Cambridge , Wilberforce Road, Cambridge CB3 0WA, UK
                [20 ]Department of Population Health Sciences, Utrecht University , Yalelaan, 3584 CL Utrecht, The Netherlands
                [21 ]IBM Research, The Hartree Centre , Daresbury, Warrington WA4 4AD, UK
                [22 ]Mathematics Institute, University of Warwick , Gibbet Hill Road, Coventry CV4 7AL, UK
                [23 ]Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick , Gibbet Hill Road, Coventry CV4 7AL, UK
                [24 ]School of Mathematical and Physical Sciences, University of Sussex , Falmer, Brighton BN1 9QH, UK
                [25 ]Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University , Heidelberglaan 100, 3584CX Utrecht, The Netherlands
                [26 ]Biomathematics Graduate Program and Department of Mathematics, North Carolina State University , Raleigh, NC 27695, USA
                [27 ]Australian Institute of Tropical Health and Medicine, James Cook University , Townsville, Queensland 4811, Australia
                [28 ]School of Mathematics and Statistics, University of Melbourne , Carlton, Victoria 3010, Australia
                [29 ]College of Medicine and Health, University of Exeter , Barrack Road, Exeter EX2 5DW, UK
                [30 ]Department of Mathematics and Statistics, La Trobe University , Bundoora, Victoria 3086, Australia
                [31 ]Department of Sociology, University of Washington , Savery Hall, Seattle, WA 98195, USA
                [32 ]School of Mathematical Sciences, University of Nottingham , University Park, Nottingham NG7 2RD, UK
                [33 ]South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University , Jonkershoek Road, Stellenbosch 7600, South Africa
                [34 ]School of Mathematical Sciences, University of Adelaide , South Australia 5005, Australia
                [35 ]Department of Mathematics, University of Rome Tor Vergata , 00133 Rome, Italy
                [36 ]Rights Lab, University of Nottingham , Highfield House, Nottingham NG7 2RD, UK
                [37 ]Escola de Matemática Aplicada, Fundação Getúlio Vargas , Praia de Botafogo, 190 Rio de Janeiro, Brazil
                [38 ]Department of Actuarial Mathematics and Statistics, Heriot-Watt University , Edinburgh EH14 4AS, UK
                [39 ]Department of Veterinary Medicine, University of Cambridge , Madingley Road, Cambridge CB3 0ES, UK
                Author notes

                Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.5077858.

                Author information
                http://orcid.org/0000-0001-8545-5212
                http://orcid.org/0000-0001-5962-4238
                http://orcid.org/0000-0001-5588-7081
                http://orcid.org/0000-0001-8661-2718
                http://orcid.org/0000-0002-3533-8672
                http://orcid.org/0000-0002-7410-6882
                http://orcid.org/0000-0002-0362-6717
                http://orcid.org/0000-0002-2842-3406
                http://orcid.org/0000-0002-5937-2410
                http://orcid.org/0000-0002-2504-6860
                http://orcid.org/0000-0001-5835-8062
                http://orcid.org/0000-0003-4639-4765
                http://orcid.org/0000-0003-1473-6644
                http://orcid.org/0000-0002-6389-6321
                http://orcid.org/0000-0002-2452-3098
                http://orcid.org/0000-0003-0701-7860
                http://orcid.org/0000-0002-9918-8167
                http://orcid.org/0000-0002-4059-2376
                http://orcid.org/0000-0003-0569-1659
                http://orcid.org/0000-0001-9158-853X
                Article
                rspb20201405
                10.1098/rspb.2020.1405
                7575516
                32781946
                283a0761-da72-4646-a6f8-a5563f1e9a4a
                © 2020 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 15 June 2020
                : 21 July 2020
                Funding
                Funded by: BBSRC;
                Award ID: BB/R009236/1
                Funded by: Wellcome Trust, http://dx.doi.org/10.13039/100004440;
                Award ID: 202562/Z/16/Z
                Award ID: 210758/Z/18/Z
                Funded by: Bill and Melinda Gates Foundation, http://dx.doi.org/10.13039/100000865;
                Award ID: OPP1184344
                Funded by: HDR;
                Award ID: MR/S003975/1
                Funded by: Leverhulme Trust, http://dx.doi.org/10.13039/501100000275;
                Award ID: RPG-2017-370
                Funded by: Engineering and Physical Sciences Research Council, http://dx.doi.org/10.13039/501100000266;
                Award ID: EP/R014604/1
                Funded by: Royal Society, http://dx.doi.org/10.13039/501100000288;
                Award ID: INF\R2\180067
                Funded by: Christ Church (Oxford);
                Award ID: Junior Research Fellowship
                Funded by: NERC;
                Award ID: NE/N014979/1
                Funded by: Vetenskapsradet;
                Award ID: 2016-04566
                Funded by: ZonMw, http://dx.doi.org/10.13039/501100001826;
                Award ID: 10430022010001
                Award ID: 91216062
                Funded by: MRC;
                Award ID: MC_PC 19065
                Award ID: MR/R015600/1
                Award ID: MR/V009761/1
                Categories
                1001
                87
                Evidence Synthesis
                Evidence Synthesis
                Custom metadata
                August 12, 2020

                Life sciences
                covid-19,sars-cov-2,exit strategy,mathematical modelling,epidemic control,uncertainty
                Life sciences
                covid-19, sars-cov-2, exit strategy, mathematical modelling, epidemic control, uncertainty

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