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Epidemiology, genome, and clinical features of the pandemic SARS-CoV-2: a recent view.

ABSTRACT: Since the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, the number of globally confirmed cases according to World Health Organization statistics reached 292 124 in 189 countries by 22 March 2020. The number of deaths reached 12 784, with estimated case-fatality rates ranging from 0.5% to 5.7%. Children population seems to be the least affected by the disease, while the highest rate of death is among the elderly and people with comorbidities. Most infected individuals are asymptomatic or only exhibit mild symptoms. After the incubation period, the most common symptoms are fever, cough and fatigue. Asymptomatic carrier state is of paramount importance because of carriers' ability to spread the infection and to shed the virus into the air and surroundings. Although much is still unknown about SARS-CoV-2, the scientific research is moving at an unprecedented pace towards understanding the nature, effective control, prevention and treatment of SARS-CoV-2. Various reports have suggested an in vivo evolution of the virus, which may explain the rapid spread and changing epidemiology of SARS-CoV-2, but further evidence is needed. Unfortunately, no effective treatment or therapeutic drug is available for the disease; only supportive treatment and classical intervention measures are available for confronting the SARS-CoV-2 pandemic.

SUBMITTER: Abduljalil JM

PROVIDER: S-EPMC7171182 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Epidemiology, genome, and clinical features of the pandemic SARS-CoV-2: a recent view.

Abduljalil J M JM Abduljalil B M BM

New microbes and new infections 20200331

Since the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, the number of globally confirmed cases according to World Health Organization statistics reached 292 124 in 189 countries by 22 March 2020. The number of deaths reached 12 784, with estimated case-fatality rates ranging from 0.5% to 5.7%. Children population seems to be the least affected by the disease, while the highest rate of death is among the elderly and people with comorbidities. Most ...[more]

PMID: 32322400

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Project description:IntroductionWithin a few months coronavirus disease 2019 (COVID-19) evolved into a pandemic causing millions of cases worldwide, but it remains challenging to diagnose the disease in a timely fashion in the emergency department (ED). In this study we aimed to construct machine-learning (ML) models to predict severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection based on the clinical features of patients visiting an ED during the early COVID-19 pandemic.MethodsWe retrospectively collected the data of all patients who received reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 at the ED of Baylor Scott & White All Saints Medical Center, Fort Worth, from February 23-May 12, 2020. The variables collected included patient demographics, ED triage data, clinical symptoms, and past medical history. The primary outcome was the confirmed diagnosis of COVID-19 (or SARS-CoV-2 infection) by a positive RT-PCR test result for SARS-CoV-2, and was used as the label for ML tasks. We used univariate analyses for feature selection, and variables with P<0.1 were selected for model construction. Samples were split into training and testing cohorts on a 60:40 ratio chronologically. We tried various ML algorithms to construct the best predictive model, and we evaluated performances with the area under the receiver operating characteristic curve (AUC) in the testing cohort.ResultsA total of 580 ED patients were tested for SARS-CoV-2 during the study periods, and 98 (16.9%) were identified as having the SARS-CoV-2 infection based on the RT-PCR results. Univariate analyses selected 21 features for model construction. We assessed three ML methods for performance: of the three methods, random forest outperformed the others with the best AUC result (0.86), followed by gradient boosting (0.83) and extra trees classifier (0.82).ConclusionThis study shows that it is feasible to use ML models as an initial screening tool for identifying patients with SARS-CoV-2 infection. Further validation will be necessary to determine how effectively this prediction model can be used prospectively in clinical practice.

Project description:SARS-CoV-2 RNA quantification in wastewater is an important tool for monitoring the prevalence of COVID-19 disease on a community scale which complements case-based surveillance systems. As novel variants of concern (VOCs) emerge there is also a need to identify the primary circulating variants in a community, accomplished to date by sequencing clinical samples. Quantifying variants in wastewater offers a cost-effective means to augment these sequencing efforts. In this study, SARS-CoV-2 N1 RNA concentrations and daily loadings were determined and compared to case-based data collected as part of a national surveillance programme to determine the validity of wastewater surveillance to monitor infection spread in the greater Dublin area. Further, sequencing of clinical samples was conducted to determine the primary SARS-CoV-2 lineages circulating in Dublin. Finally, digital PCR was employed to determine whether SARS-CoV-2 VOCs, Alpha and Delta, were quantifiable from wastewater. No lead or lag time was observed between SARS-CoV-2 wastewater and case-based data and SARS-CoV-2 trends in Dublin wastewater significantly correlated with the notification of confirmed cases through case-based surveillance preceding collection with a 5-day average. This demonstrates that viral RNA in Dublin's wastewater mirrors the spread of infection in the community. Clinical sequence data demonstrated that increased COVID-19 cases during Ireland's third wave coincided with the introduction of the Alpha variant, while the fourth wave coincided with increased prevalence of the Delta variant. Interestingly, the Alpha variant was detected in Dublin wastewater prior to the first genome being sequenced from clinical samples, while the Delta variant was identified at the same time in clinical and wastewater samples. This work demonstrates the validity of wastewater surveillance for monitoring SARS-CoV-2 infections and also highlights its effectiveness in identifying circulating variants which may prove useful when sequencing capacity is limited.

Dataset Information

Epidemiology, genome, and clinical features of the pandemic SARS-CoV-2: a recent view.

Publications

Epidemiology, genome, and clinical features of the pandemic SARS-CoV-2: a recent view.

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