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      Combination of CT and RT-PCR in the screening or diagnosis of COVID-19

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          Abstract

          Coronavirus Disease 2019 (COVID-19), emerged in Wuhan, China, in December 2019. As of 10 March 2020, COVID-19 cases have been reported in 114 countries from all Continents except Antarctica, with accumulative 80 932 cases in China and 29 432 in other countries [1]. The transmission potential of COVID-19, determined by reproduction number (R0) of 3.28, is much higher that of severe acute respiratory syndrome (SARS) [2]. Bold measures taken by China have effectively curbed the rapid spread of this new respiratory disease source and changed the dangerous process of rapid spread of the epidemic [3]. The world is not yet ready to organize and implement the measures that have been proved to be efficient and effective by China to block or minimize the spread of new coronavirus [3]. The crude case-fatality rate (CFR) of COVID-19 is reported to be 2.3% in all patients [4], while higher to be 61.5% in critically ill patients [5]. Therefore, early screening and quarantining mild or asymptomatic cases and early diagnosis of sever patients for intensive treatment are urgent to avoid the pandemic of COVID-19. Reverse Transcription-Polymerase Chain Reaction (RT-PCR) testing was recommended to confirm COVID-19 cases by China medical authority [6], however the total positive rate of RT-PCR for throat swab samples was about 30% to 60% at initial presentation. Chest Computed Tomography (CT), a routine imaging method, has also been applied to diagnose COVID-19 infection [7]. A study on the correlation of chest CT and RT-PCR testing of COVID-19 based on 1014 cases demonstrated that the sensitivity of chest CT imaging for COVID-19 was 97% (580/601), and the specificity was 25% (105/413), with RT-PCR as a diagnosis criterion [8]. As a new emerging infectious disease, there is no gold criteria for the diagnosis of COVID-19. Our current study showed that if chest CT was taken as a reference of diagnosis standard, the sensitivity of RT-PCR testing for COVID-19 was 65% (580/888), and the specificity is 83% (105/126) ( Table 1 ). Thus, if both sensitivity and specificity were taken into account simultaneously, neither chest CT nor RT-PCR testing alone is accurate enough for the diagnosis of COVID-19 infection. Table 1 The performance of RT-PCR for COVID-19 infection with chest CT result as reference CT RT-PCR Sensitivity (95% CI) Specificity (95% CI) positive negative Positive 580 308 65% (62%-68%) 83% (76%-89%) Negative 21 105 CT – computed tomography, RT-PCR – reverse transcriptase polymerase chain reaction, CI – confidence interval Considering that asymptomatic cases are also of higher transmission potential, sensitivity should be first considered for the screening purpose of COVID-19 infection. When confirmation for intensive treatment, specificity should be first considered to avoid false treatment. Parallel tests and serial test are needed to increase both sensitivity and specificity. Parallel tests perform RT-PCR and CT imaging at the same time and the results are cross-referenced to make the diagnosis [9]. Serial test employs as a secondary screening test which is performed only if the result of initial screening test is positive [9]. For screening purposes, parallel tests, ie, positive in either RT-PCR or chest CT is used to clinically diagnose COVID-19, can improve sensitivity and decrease false negative cases. For therapy purposes serial tests should be used to improve specificity and decrease false positive cases. Consequently, we recommend that parallel tests are used in screening, while series tests should be used for diagnosis confirmation of COVID-19. The proposed strategic approach for screening and diagnosis confirmation of COVID-19 infection might also be of reference significance for other countries or other emerging infectious disease. Photo: Parallel or serial test (from the authors’ own collection, used with permission).

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          Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study

          Summary Background An ongoing outbreak of pneumonia associated with the severe acute respiratory coronavirus 2 (SARS-CoV-2) started in December, 2019, in Wuhan, China. Information about critically ill patients with SARS-CoV-2 infection is scarce. We aimed to describe the clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia. Methods In this single-centered, retrospective, observational study, we enrolled 52 critically ill adult patients with SARS-CoV-2 pneumonia who were admitted to the intensive care unit (ICU) of Wuhan Jin Yin-tan hospital (Wuhan, China) between late December, 2019, and Jan 26, 2020. Demographic data, symptoms, laboratory values, comorbidities, treatments, and clinical outcomes were all collected. Data were compared between survivors and non-survivors. The primary outcome was 28-day mortality, as of Feb 9, 2020. Secondary outcomes included incidence of SARS-CoV-2-related acute respiratory distress syndrome (ARDS) and the proportion of patients requiring mechanical ventilation. Findings Of 710 patients with SARS-CoV-2 pneumonia, 52 critically ill adult patients were included. The mean age of the 52 patients was 59·7 (SD 13·3) years, 35 (67%) were men, 21 (40%) had chronic illness, 51 (98%) had fever. 32 (61·5%) patients had died at 28 days, and the median duration from admission to the intensive care unit (ICU) to death was 7 (IQR 3–11) days for non-survivors. Compared with survivors, non-survivors were older (64·6 years [11·2] vs 51·9 years [12·9]), more likely to develop ARDS (26 [81%] patients vs 9 [45%] patients), and more likely to receive mechanical ventilation (30 [94%] patients vs 7 [35%] patients), either invasively or non-invasively. Most patients had organ function damage, including 35 (67%) with ARDS, 15 (29%) with acute kidney injury, 12 (23%) with cardiac injury, 15 (29%) with liver dysfunction, and one (2%) with pneumothorax. 37 (71%) patients required mechanical ventilation. Hospital-acquired infection occurred in seven (13·5%) patients. Interpretation The mortality of critically ill patients with SARS-CoV-2 pneumonia is considerable. The survival time of the non-survivors is likely to be within 1–2 weeks after ICU admission. Older patients (>65 years) with comorbidities and ARDS are at increased risk of death. The severity of SARS-CoV-2 pneumonia poses great strain on critical care resources in hospitals, especially if they are not adequately staffed or resourced. Funding None.
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            Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases

            Background Chest CT is used for diagnosis of 2019 novel coronavirus disease (COVID-19), as an important complement to the reverse-transcription polymerase chain reaction (RT-PCR) tests. Purpose To investigate the diagnostic value and consistency of chest CT as compared with comparison to RT-PCR assay in COVID-19. Methods From January 6 to February 6, 2020, 1014 patients in Wuhan, China who underwent both chest CT and RT-PCR tests were included. With RT-PCR as reference standard, the performance of chest CT in diagnosing COVID-19 was assessed. Besides, for patients with multiple RT-PCR assays, the dynamic conversion of RT-PCR results (negative to positive, positive to negative, respectively) was analyzed as compared with serial chest CT scans for those with time-interval of 4 days or more. Results Of 1014 patients, 59% (601/1014) had positive RT-PCR results, and 88% (888/1014) had positive chest CT scans. The sensitivity of chest CT in suggesting COVID-19 was 97% (95%CI, 95-98%, 580/601 patients) based on positive RT-PCR results. In patients with negative RT-PCR results, 75% (308/413) had positive chest CT findings; of 308, 48% were considered as highly likely cases, with 33% as probable cases. By analysis of serial RT-PCR assays and CT scans, the mean interval time between the initial negative to positive RT-PCR results was 5.1 ± 1.5 days; the initial positive to subsequent negative RT-PCR result was 6.9 ± 2.3 days). 60% to 93% of cases had initial positive CT consistent with COVID-19 prior (or parallel) to the initial positive RT-PCR results. 42% (24/57) cases showed improvement in follow-up chest CT scans before the RT-PCR results turning negative. Conclusion Chest CT has a high sensitivity for diagnosis of COVID-19. Chest CT may be considered as a primary tool for the current COVID-19 detection in epidemic areas. A translation of this abstract in Farsi is available in the supplement. - ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.
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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

                Journal
                J Glob Health
                J Glob Health
                JGH
                Journal of Global Health
                International Society of Global Health
                2047-2978
                2047-2986
                June 2020
                24 April 2020
                : 10
                : 1
                : 010347
                Affiliations
                [1 ]Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
                [2 ]Inner Mongolia Comprehensive Center for Disease Control and Prevention, Hohhot, Inner Mongolia Autonomous Region, China
                [3 ]School of Public Health, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, Shandong Province, China
                [4 ]School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
                Author notes
                Correspondence to:
Wei Wang, MD, PhD, FFPH, FRSB, FRSM
School of Medical and Health Sciences
Edith Cowan University
270 Joondalup Drive
Perth 60127
Australia
 wei.wang@ 123456ecu.edu.au
                Article
                jogh-10-010347
                10.7189/jogh.10.010347
                7183249
                32373325
                dcf62590-9fa6-414d-ad73-aa5d565e5aaa
                Copyright © 2020 by the Journal of Global Health. All rights reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License.

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