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      Duration of quarantine in hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection: a question needing an answer

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          Abstract

          In December 2019 a new form of pneumonia was observed in the Chinese province of Hubei.[1] The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was subsequently identified as responsible of this condition, defined coronavirus disease (COVID-19).[2] The virus has now spread outside Chinese borders with 82,297 cases and 2,804 deaths worldwide at the 26th of February.[3] After infection, symptoms appear after an incubation time of 3-5 days, with 80% of those infected developing a mild disease, 15% a severe disease and 5% will require support in intensive care unit (ICU).[4] Overall, the estimated case-fatality rate is comprised between 0.4% and 2.9% and the basic reproduction number is approximately 3.28.[4,5] SARS-CoV-2 is a new pathogen for humankind and any type of immune protection exist, thus everybody can be potentially infected. Moreover, no primary prophylaxis measures (vaccination) nor effective treatments are available. If the above represented percentages are applied to the worldwide populations, it appears clear why any measure should be considered to avoid a further diffusion of the virus and prevent the saturation and collapse of health systems and the most catastrophic pandemic since 1919 Spanish flu. Isolation of those affected and the use of personal protective equipment (PPE) are the mainstay to block transmission of this pathogen, which is presumed through respiratory droplets. A 14 days quarantine is applied to subjects coming from endemic areas or who had contact with confirmed cases. It is assumed that, if in this period the subject does not develop any sign or symptoms compatible with COVID-19, he is not infected and thus the quarantine can be removed, and the subject returned to the community. Domiciliary quarantine of 14 days since a positive test is applied also for patients with a diagnosed mild disease who did not need medical support. These rules are effective in controlling infections in the community, but several doubts arise when it is necessary to transpose them in the hospital setting. Hospitals are indeed a delicate place in epidemics: they collect fragile persons who can be exposed to the virus and are subsequently readmitted to the community thus spreading the infection. Indeed, the ongoing outbreak in Northern Italy has been linked to a single infected patient who accessed to a community hospital where he transmitted the virus to several other patients and health-care operators.[6] Moreover, the isolation of patients in the hospital setting impose a significant burden in terms of PPE used by the health-care operators, space dedicated and time employed in their management. Even more complex is the situation of patients in ICU, where viral spreading is facilitated by endotracheal tubes and manoeuvres performed on the respiratory tract. Therefore, a clear definition of the infectiousness timing and intensity of viral spreading is mandatory to alleviate the burden on the health-care system. Unfortunately, the data available on the topic are scarce and composed only of measurements of viral shedding, without an assessment of the infectivity. Kim et al.[7] assessed the viral load kinetics of SARS-CoV-2 in upper and lower respiratory tract materials in the first two confirmed patients in Korea. They employed real-time reverse transcriptase polymerase chain reaction (rRT-PCR) to detect SARS-CoV-2 and converted cycle threshold (CT) values of rRT-PCR into RNA copy number. The detection limit of rRT-PCR was 2,690 copies/mL. Overall, viral load above detection limit was detected until 14 and 25 days after symptoms onset and for 13 and 11 days after the first detection, respectively.[7] Of note, both patients received treatment with lopinavir/ritonavir. Instead, Zou and colleagues analysed viral load in repeated nasal and throat swabs obtained from the 17 symptomatic patients.[8] They also employed rRT-PCR and considered a CT of 40 as detection limit. Higher viral loads were observed in nasal swabs and in samples collected soon after symptoms onset. Overall, only two patients presented positive samples, and only in nasal swab, 14 days after symptoms onset, and with low viral load. In conclusion, a larger amount of data about duration of viral spreading and infectivity in hospitalized patients, especially in ICU, is badly needed to better define quarantine period and avoid nosocomial transmission. Before their availability, the canonical 14 days period of quarantine should be respected. Financial support none related to the content of this manuscript. Conflict of interests none related to the content of this manuscript.

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          A Novel Coronavirus from Patients with Pneumonia in China, 2019

          Summary In December 2019, a cluster of patients with pneumonia of unknown cause was linked to a seafood wholesale market in Wuhan, China. A previously unknown betacoronavirus was discovered through the use of unbiased sequencing in samples from patients with pneumonia. Human airway epithelial cells were used to isolate a novel coronavirus, named 2019-nCoV, which formed a clade within the subgenus sarbecovirus, Orthocoronavirinae subfamily. Different from both MERS-CoV and SARS-CoV, 2019-nCoV is the seventh member of the family of coronaviruses that infect humans. Enhanced surveillance and further investigation are ongoing. (Funded by the National Key Research and Development Program of China and the National Major Project for Control and Prevention of Infectious Disease in China.)
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            Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention

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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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                Author and article information

                Contributors
                Journal
                J Hosp Infect
                J. Hosp. Infect
                The Journal of Hospital Infection
                The Healthcare Infection Society. Published by Elsevier Ltd.
                0195-6701
                1532-2939
                6 March 2020
                6 March 2020
                Affiliations
                [1 ]Infectious Diseases Unit, IRCCS Ca' Granda Ospedale Maggiore Policlinico Foundation, Milano, Italy
                [2 ]Department of Pathophysiology and Transplantation, University of Milano, Milano, Italy
                Author notes
                []Corresponding author: Infectious Diseases Unit IRCCS Ca' Granda Ospedale Maggiore Policlinico Foundation Via Francesco Sforza 35, 20122, Milan, Italy Tel: +393409010358 andrea.lombardi@ 123456policlinico.mi.it
                Article
                S0195-6701(20)30102-X
                10.1016/j.jhin.2020.03.003
                7134399
                32151674
                b64612f4-0755-4b48-8245-e4804aa49acf
                © 2020 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 28 February 2020
                : 2 March 2020
                Categories
                Article

                Infectious disease & Microbiology
                covid-19,sars-cov-2,quarantine,nosocomial transmission
                Infectious disease & Microbiology
                covid-19, sars-cov-2, quarantine, nosocomial transmission

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