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      Quarantine alone or in combination with other public health measures to control COVID‐19: a rapid review

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          Coronavirus disease 2019 (COVID‐19) is a rapidly emerging disease classified as a pandemic by the World Health Organization (WHO). To support the WHO with their recommendations on quarantine, we conducted a rapid review on the effectiveness of quarantine during severe coronavirus outbreaks.


          To assess the effects of quarantine (alone or in combination with other measures) of individuals who had contact with confirmed or suspected cases of COVID‐19, who travelled from countries with a declared outbreak, or who live in regions with high disease transmission.

          Search methods

          An information specialist searched the Cochrane COVID‐19 Study Register, and updated the search in PubMed, Ovid MEDLINE, WHO Global Index Medicus, Embase, and CINAHL on 23 June 2020.

          Selection criteria

          Cohort studies, case‐control studies, time series, interrupted time series, case series, and mathematical modelling studies that assessed the effect of any type of quarantine to control COVID‐19. We also included studies on SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome) as indirect evidence for the current coronavirus outbreak.

          Data collection and analysis

          Two review authors independently screened abstracts and titles in duplicate. Two review authors then independently screened all potentially relevant full‐text publications. One review author extracted data, assessed the risk of bias and assessed the certainty of evidence with GRADE and a second review author checked the assessment. We used three different tools to assess risk of bias, depending on the study design: ROBINS‐I for non‐randomised studies of interventions, a tool provided by Cochrane Childhood Cancer for non‐randomised, non‐controlled studies, and recommendations from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) for modelling studies. We rated the certainty of evidence for the four primary outcomes: incidence, onward transmission, mortality, and costs.

          Main results

          We included 51 studies; 4 observational studies and 28 modelling studies on COVID‐19, one observational and one modelling study on MERS, three observational and 11 modelling studies on SARS, and three modelling studies on SARS and other infectious diseases. Because of the diverse methods of measurement and analysis across the outcomes of interest, we could not conduct a meta‐analysis and undertook a narrative synthesis. We judged risk of bias to be moderate for 2/3 non‐randomized studies of interventions (NRSIs) and serious for 1/3 NRSI. We rated risk of bias moderate for 4/5 non‐controlled cohort studies, and serious for 1/5. We rated modelling studies as having no concerns for 13 studies, moderate concerns for 17 studies and major concerns for 13 studies.

          Quarantine for individuals who were in contact with a confirmed/suspected COVID‐19 case in comparison to no quarantine

          Modelling studies consistently reported a benefit of the simulated quarantine measures, for example, quarantine of people exposed to confirmed or suspected cases may have averted 44% to 96% of incident cases and 31% to 76% of deaths compared to no measures based on different scenarios (incident cases: 6 modelling studies on COVID‐19, 1 on SARS; mortality: 2 modelling studies on COVID‐19, 1 on SARS, low‐certainty evidence). Studies also indicated that there may be a reduction in the basic reproduction number ranging from 37% to 88% due to the implementation of quarantine (5 modelling studies on COVID‐19, low‐certainty evidence). Very low‐certainty evidence suggests that the earlier quarantine measures are implemented, the greater the cost savings may be (2 modelling studies on SARS).

          Quarantine in combination with other measures to contain COVID‐19 in comparison to other measures without quarantine or no measures

          When the models combined quarantine with other prevention and control measures, such as school closures, travel restrictions and social distancing, the models demonstrated that there may be a larger effect on the reduction of new cases, transmissions and deaths than measures without quarantine or no interventions (incident cases: 9 modelling studies on COVID‐19; onward transmission: 5 modelling studies on COVID‐19; mortality: 5 modelling studies on COVID‐19, low‐certainty evidence). Studies on SARS and MERS were consistent with findings from the studies on COVID‐19.

          Quarantine for individuals travelling from a country with a declared COVID‐19 outbreak compared to no quarantine

          Very low‐certainty evidence indicated that the effect of quarantine of travellers from a country with a declared outbreak on reducing incidence and deaths may be small for SARS, but might be larger for COVID‐19 (2 observational studies on COVID‐19 and 2 observational studies on SARS).

          Authors' conclusions

          The current evidence is limited because most studies on COVID‐19 are mathematical modelling studies that make different assumptions on important model parameters. Findings consistently indicate that quarantine is important in reducing incidence and mortality during the COVID‐19 pandemic, although there is uncertainty over the magnitude of the effect. Early implementation of quarantine and combining quarantine with other public health measures is important to ensure effectiveness. In order to maintain the best possible balance of measures, decision makers must constantly monitor the outbreak and the impact of the measures implemented.

          This review was originally commissioned by the WHO and supported by Danube‐University‐Krems. The update was self‐initiated by the review authors.

          Plain language summary

          Does quarantine, alone or in combination with other public health measures, control coronavirus (COVID‐19)?


Coronavirus disease 2019 (COVID‐19) is caused by a new virus that has spread quickly throughout the world. Most infected people either experience no symptoms or suffer mild, flu‐like symptoms, but some become seriously ill, and may die.

There is no vaccine (a medicine that stops people catching a specific disease) for COVID‐19, so other ways of slowing its spread are needed. One way of controlling the disease is quarantine. This means separating healthy people from other healthy people, who may have the virus after being in close contact with an infected person, or because they have returned from an area with high infection rates. Similar recommendations include isolation (like quarantine, but for people who tested positive for COVID‐19) and physical distancing (people without symptoms keep a distance from each other).

 What did we want to find out?

We wanted to find out whether and how effectively quarantine stops COVID‐19 spreading and if it prevents death. We wanted to know if it was more effective when combined with other measures, and how much it costs.

 Study characteristics COVID‐19 is spreading rapidly, so we needed to answer these questions as quickly as possible. This meant we shortened some steps of the normal Cochrane Review process. Nevertheless, we are confident that these changes do not affect our overall conclusions.

We looked for studies that assessed the effect of any type of quarantine, anywhere, on the spread and severity of COVID‐19. We also looked for studies that assessed quarantine alongside other measures, such as isolation, physical distancing or school closures. COVID‐19 is a new disease, so, to find as much evidence as possible, we also looked for studies on similar viruses, such as SARS (severe acute respiratory syndrome) and MERS (Middle East respiratory syndrome).

Studies measured the number of COVID‐19, SARS or MERS cases, how many people were infected, how quickly the virus spread, how many people died, and the costs of quarantine.

 Key results We included 51 studies. Thirty‐two studies focused on COVID‐19, 14 on SARS, three on SARS plus other viruses, and two on MERS. Most studies combined existing data from multiple sources and assumptions to create a model (a simulation) for predicting how events might occur over time, for people in different situations (called modelling studies). Four COVID‐19 studies observed the effects of quarantine (observational studies) on 6064 individuals in China, Greece and Singapore. Twenty‐eight COVID‐19 studies simulated outbreaks in Algeria, China, Canada, Italy, Kazakhstan, Nepal, UK, USA, Singapore, South Korea, on the cruise ship Diamond Princess, and in a general population. Four studies looked back on the effect of quarantine on 178,122 people involved in SARS and MERS outbreaks. The remaining 15 studies modelled SARS and MERS outbreaks.

          The modelling studies all found that simulated quarantine measures reduce the number of people with COVID‐19 and the number of deaths. With quarantine, estimates showed a minimum reduction in the number of people with COVID‐19 of 44%, and a maximum reduction of 96%. Similarly, with quarantine, estimates of the number of deaths showed a minimum reduction of 31%, and a maximum reduction of 76%. Combining quarantine with other measures, such as closing schools or physical distancing, may be more effective at reducing the spread of COVID‐19 than quarantine alone. The SARS and MERS studies agreed with the studies on COVID‐19.

Two SARS modelling studies assessed costs. They found that the costs may be lower when quarantine measures start earlier.

 Reliability of the evidence

We are uncertain about the evidence we found for several reasons. The observational studies on COVID‐19 did not include a comparison group without quarantine. The COVID‐19 studies based their models on limited data and made different assumptions about the virus (e.g. how quickly it would spread). The other studies investigated SARS and MERS so they only provide indirect evidence.

Despite limited evidence, all the studies found quarantine to be important in reducing the number of people infected and the number of deaths. Results suggest that quarantine was most effective, and cost less, when it started earlier. Combining quarantine with other prevention and control measures may have a greater effect than quarantine alone.
This review includes evidence published up to 23 June 2020.

          Related collections

          Most cited references 73

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          The reproductive number of COVID-19 is higher compared to SARS coronavirus

          Introduction In Wuhan, China, a novel and alarmingly contagious primary atypical (viral) pneumonia broke out in December 2019. It has since been identified as a zoonotic coronavirus, similar to SARS coronavirus and MERS coronavirus and named COVID-19. As of 8 February 2020, 33 738 confirmed cases and 811 deaths have been reported in China. Here we review the basic reproduction number (R 0) of the COVID-19 virus. R 0 is an indication of the transmissibility of a virus, representing the average number of new infections generated by an infectious person in a totally naïve population. For R 0 > 1, the number infected is likely to increase, and for R 0 < 1, transmission is likely to die out. The basic reproduction number is a central concept in infectious disease epidemiology, indicating the risk of an infectious agent with respect to epidemic spread. Methods and Results PubMed, bioRxiv and Google Scholar were accessed to search for eligible studies. The term ‘coronavirus & basic reproduction number’ was used. The time period covered was from 1 January 2020 to 7 February 2020. For this time period, we identified 12 studies which estimated the basic reproductive number for COVID-19 from China and overseas. Table 1 shows that the estimates ranged from 1.4 to 6.49, with a mean of 3.28, a median of 2.79 and interquartile range (IQR) of 1.16. Table 1 Published estimates of R 0 for 2019-nCoV Study (study year) Location Study date Methods Approaches R 0 estimates (average) 95% CI Joseph et al. 1 Wuhan 31 December 2019–28 January 2020 Stochastic Markov Chain Monte Carlo methods (MCMC) MCMC methods with Gibbs sampling and non-informative flat prior, using posterior distribution 2.68 2.47–2.86 Shen et al. 2 Hubei province 12–22 January 2020 Mathematical model, dynamic compartmental model with population divided into five compartments: susceptible individuals, asymptomatic individuals during the incubation period, infectious individuals with symptoms, isolated individuals with treatment and recovered individuals R 0 = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta$\end{document} / \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\alpha$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\beta$\end{document} = mean person-to-person transmission rate/day in the absence of control interventions, using nonlinear least squares method to get its point estimate \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\alpha$\end{document} = isolation rate = 6 6.49 6.31–6.66 Liu et al. 3 China and overseas 23 January 2020 Statistical exponential Growth, using SARS generation time = 8.4 days, SD = 3.8 days Applies Poisson regression to fit the exponential growth rateR 0 = 1/M(−𝑟)M = moment generating function of the generation time distributionr = fitted exponential growth rate 2.90 2.32–3.63 Liu et al. 3 China and overseas 23 January 2020 Statistical maximum likelihood estimation, using SARS generation time = 8.4 days, SD = 3.8 days Maximize log-likelihood to estimate R 0 by using surveillance data during a disease epidemic, and assuming the secondary case is Poisson distribution with expected value R 0 2.92 2.28–3.67 Read et al. 4 China 1–22 January 2020 Mathematical transmission model assuming latent period = 4 days and near to the incubation period Assumes daily time increments with Poisson-distribution and apply a deterministic SEIR metapopulation transmission model, transmission rate = 1.94, infectious period =1.61 days 3.11 2.39–4.13 Majumder et al. 5 Wuhan 8 December 2019 and 26 January 2020 Mathematical Incidence Decay and Exponential Adjustment (IDEA) model Adopted mean serial interval lengths from SARS and MERS ranging from 6 to 10 days to fit the IDEA model, 2.0–3.1 (2.55) / WHO China 18 January 2020 / / 1.4–2.5 (1.95) / Cao et al. 6 China 23 January 2020 Mathematical model including compartments Susceptible-Exposed-Infectious-Recovered-Death-Cumulative (SEIRDC) R = K 2 (L × D) + K(L + D) + 1L = average latent period = 7,D = average latent infectious period = 9,K = logarithmic growth rate of the case counts 4.08 / Zhao et al. 7 China 10–24 January 2020 Statistical exponential growth model method adopting serial interval from SARS (mean = 8.4 days, SD = 3.8 days) and MERS (mean = 7.6 days, SD = 3.4 days) Corresponding to 8-fold increase in the reporting rateR 0 = 1/M(−𝑟)𝑟 =intrinsic growth rateM = moment generating function 2.24 1.96–2.55 Zhao et al. 7 China 10–24 January 2020 Statistical exponential growth model method adopting serial interval from SARS (mean = 8.4 days, SD = 3.8 days) and MERS (mean = 7.6 days, SD = 3.4 days) Corresponding to 2-fold increase in the reporting rateR 0 = 1/M(−𝑟)𝑟 =intrinsic growth rateM = moment generating function 3.58 2.89–4.39 Imai (2020) 8 Wuhan January 18, 2020 Mathematical model, computational modelling of potential epidemic trajectories Assume SARS-like levels of case-to-case variability in the numbers of secondary cases and a SARS-like generation time with 8.4 days, and set number of cases caused by zoonotic exposure and assumed total number of cases to estimate R 0 values for best-case, median and worst-case 1.5–3.5 (2.5) / Julien and Althaus 9 China and overseas 18 January 2020 Stochastic simulations of early outbreak trajectories Stochastic simulations of early outbreak trajectories were performed that are consistent with the epidemiological findings to date 2.2 Tang et al. 10 China 22 January 2020 Mathematical SEIR-type epidemiological model incorporates appropriate compartments corresponding to interventions Method-based method and Likelihood-based method 6.47 5.71–7.23 Qun Li et al. 11 China 22 January 2020 Statistical exponential growth model Mean incubation period = 5.2 days, mean serial interval = 7.5 days 2.2 1.4–3.9 Averaged 3.28 CI, Confidence interval. Figure 1 Timeline of the R 0 estimates for the 2019-nCoV virus in China The first studies initially reported estimates of R 0 with lower values. Estimations subsequently increased and then again returned in the most recent estimates to the levels initially reported (Figure 1). A closer look reveals that the estimation method used played a role. The two studies using stochastic methods to estimate R 0, reported a range of 2.2–2.68 with an average of 2.44. 1 , 9 The six studies using mathematical methods to estimate R 0 produced a range from 1.5 to 6.49, with an average of 4.2. 2 , 4–6 , 8 , 10 The three studies using statistical methods such as exponential growth estimated an R 0 ranging from 2.2 to 3.58, with an average of 2.67. 3 , 7 , 11 Discussion Our review found the average R 0 to be 3.28 and median to be 2.79, which exceed WHO estimates from 1.4 to 2.5. The studies using stochastic and statistical methods for deriving R 0 provide estimates that are reasonably comparable. However, the studies using mathematical methods produce estimates that are, on average, higher. Some of the mathematically derived estimates fall within the range produced the statistical and stochastic estimates. It is important to further assess the reason for the higher R 0 values estimated by some the mathematical studies. For example, modelling assumptions may have played a role. In more recent studies, R 0 seems to have stabilized at around 2–3. R 0 estimations produced at later stages can be expected to be more reliable, as they build upon more case data and include the effect of awareness and intervention. It is worthy to note that the WHO point estimates are consistently below all published estimates, although the higher end of the WHO range includes the lower end of the estimates reviewed here. R 0 estimates for SARS have been reported to range between 2 and 5, which is within the range of the mean R 0 for COVID-19 found in this review. Due to similarities of both pathogen and region of exposure, this is expected. On the other hand, despite the heightened public awareness and impressively strong interventional response, the COVID-19 is already more widespread than SARS, indicating it may be more transmissible. Conclusions This review found that the estimated mean R 0 for COVID-19 is around 3.28, with a median of 2.79 and IQR of 1.16, which is considerably higher than the WHO estimate at 1.95. These estimates of R 0 depend on the estimation method used as well as the validity of the underlying assumptions. Due to insufficient data and short onset time, current estimates of R 0 for COVID-19 are possibly biased. However, as more data are accumulated, estimation error can be expected to decrease and a clearer picture should form. Based on these considerations, R 0 for COVID-19 is expected to be around 2–3, which is broadly consistent with the WHO estimate. Author contributions J.R. and A.W.S. had the idea, and Y.L. did the literature search and created the table and figure. Y.L. and A.W.S. wrote the first draft; A.A.G. drafted the final manuscript. All authors contributed to the final manuscript. Conflict of interest None declared.
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            ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

            Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.
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              Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

              Highlights • The novel coronavirus (2019-nCoV) pneumonia has caused 2033 confirmed cases, including 56 deaths in mainland China, by 2020-01-26 17:06. • We aim to estimate the basic reproduction number of 2019-nCoV in Wuhan, China using the exponential growth model method. • We estimated that the mean R 0 ranges from 2.24 to 3.58 with an 8-fold to 2-fold increase in the reporting rate. • Changes in reporting likely occurred and should be taken into account in the estimation of R 0.

                Author and article information

                Cochrane Database Syst Rev
                Cochrane Database Syst Rev
                The Cochrane Database of Systematic Reviews
                John Wiley & Sons, Ltd (Chichester, UK )
                14 September 2020
                September 2020
                14 September 2020
                : 2020
                : 9
                deptCochrane Austria, Department for Evidence-based Medicine and Evaluation Danube University Krems KremsAustria
                deptDepartment of Public Health Health Services Research and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology Hall in TirolAustria
                Donau-Universität Krems KremsAustria
                deptDivision of Health Technology Assessment and Bioinformatics Oncotyrol - Center for Personalized Cancer Medicine InnsbruckAustria
                deptCenter for Health Decision Science, Department of Health Policy and Management Harvard T.H. Chan School of Public Health BostonUSA
                deptInstitute for Technology Assessment and Department of Radiology Massachusetts General Hospital, Harvard Medical School BostonMassachusettsUSA
                RTI International Research Triangle ParkNorth CarolinaUSA
                CD013574.pub2 CD013574
                Copyright © 2020 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.

                This is an open access article under the terms of the Creative Commons Attribution-Non-Commercial Licence, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                Child health
                Infectious disease
                Lungs & airways


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