Dataset Information

Serial Intervals and Case Isolation Delays for Coronavirus Disease 2019: A Systematic Review and Meta-Analysis.

ABSTRACT:

Background

Estimates of the serial interval distribution contribute to our understanding of the transmission dynamics of coronavirus disease 2019 (COVID-19). Here, we aimed to summarize the existing evidence on serial interval distributions and delays in case isolation for COVID-19.

Methods

We conducted a systematic review of the published literature and preprints in PubMed on 2 epidemiological parameters, namely, serial intervals and delay intervals relating to isolation of cases for COVID-19 from 1 January 2020 to 22 October 2020 following predefined eligibility criteria. We assessed the variation in these parameter estimates using correlation and regression analysis.

Results

Of 103 unique studies on serial intervals of COVID-19, 56 were included, providing 129 estimates. Of 451 unique studies on isolation delays, 18 were included, providing 74 estimates. Serial interval estimates from 56 included studies varied from 1.0 to 9.9 days, while case isolation delays from 18 included studies varied from 1.0 to 12.5 days, which were associated with spatial, methodological, and temporal factors. In mainland China, the pooled mean serial interval was 6.2 days (range, 5.1-7.8) before the epidemic peak and reduced to 4.9 days (range, 1.9-6.5) after the epidemic peak. Similarly, the pooled mean isolation delay related intervals were 6.0 days (range, 2.9-12.5) and 2.4 days (range, 2.0-2.7) before and after the epidemic peak, respectively. There was a positive association between serial interval and case isolation delay.

Conclusions

Temporal factors, such as different control measures and case isolation in particular, led to shorter serial interval estimates over time. Correcting transmissibility estimates for these time-varying distributions could aid mitigation efforts.

SUBMITTER: Ali ST

PROVIDER: S-EPMC8241473 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

Serial Intervals and Case Isolation Delays for Coronavirus Disease 2019: A Systematic Review and Meta-Analysis.

Ali Sheikh Taslim ST Yeung Amy A Shan Songwei S Wang Lin L Gao Huizhi H Du Zhanwei Z Xu Xiao-Ke XK Wu Peng P Lau Eric H Y EHY Cowling Benjamin J BJ

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20220301 4

<h4>Background</h4>Estimates of the serial interval distribution contribute to our understanding of the transmission dynamics of coronavirus disease 2019 (COVID-19). Here, we aimed to summarize the existing evidence on serial interval distributions and delays in case isolation for COVID-19.<h4>Methods</h4>We conducted a systematic review of the published literature and preprints in PubMed on 2 epidemiological parameters, namely, serial intervals and delay intervals relating to isolation of cases ...[more]

PMID: 34037748

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Project description:BackgroundIn December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged with a high transmissibility rate and resulted in numerous negative impacts on global life. Preventive measures such as face masks, social distancing, and vaccination helped control the pandemic. Nonetheless, the emergence of SARS-CoV-2 variants, such as Omega and Delta, as well as coronavirus disease 2019 (COVID-19) reinfection, raise additional concerns. Therefore, this study aimed to determine the overall prevalence of reinfection on global and regional scales.MethodsA systematic search was conducted across three databases, PubMed, Scopus, and ProQuest Central, including all articles pertaining to COVID-19 reinfection without language restriction. After critical appraisal and qualitative synthesis of the identified relevant articles, a meta-analysis considering random effects was used to pool the studies.ResultsWe included 52 studies conducted between 2019 and 2022, with a total sample size of 3,623,655 patients. The overall prevalence of COVID-19 reinfection was 4.2% (95% confidence interval [CI]: 3.7-4.8%; n = 52), with high heterogeneity between studies. Africa had the highest prevalence of 4.7% (95% CI: 1.9-7.5%; n = 3), whereas Oceania and America had lower estimates of 0.3% (95% CI: 0.2-0.4%; n = 1) and 1% (95% CI: 0.8-1.3%; n = 7), respectively. The prevalence of reinfection in Europe and Asia was 1.2% (95% CI: 0.8-1.5%; n = 8) and 3.8% (95% CI: 3.4-4.3%; n = 43), respectively. Studies that used a combined type of specimen had the highest prevalence of 7.6% (95% CI: 5.8-9.5%; n = 15) compared with those that used oropharyngeal or nasopharyngeal swabs only that had lower estimates of 6.7% (95% CI: 4.8-8.5%; n = 8), and 3.4% (95% CI: 2.8-4.0%; n = 12) respectively.ConclusionCOVID-19 reinfection occurs with varying prevalence worldwide, with the highest occurring in Africa. Therefore, preventive measures, including vaccination, should be emphasized to ensure control of the pandemic.

Project description:ObjectiveClinical and laboratory biomarkers to predict the severity of coronavirus disease 2019 (COVID-19) are essential in this pandemic situation of which resource allocation must be urgently prepared especially in the context of respiratory support readiness. Lymphocyte count has been a marker of interest since the first COVID-19 publication. We conducted a systematic review and meta-analysis in order to investigate the association of lymphocyte count on admission and the severity of COVID-19. We would also like to analyze whether patient characteristics such as age and comorbidities affect the relationship between lymphocyte count and COVID-19.MethodsComprehensive and systematic literature search was performed from PubMed, SCOPUS, EuropePMC, ProQuest, Cochrane Central Databases, and Google Scholar. Research articles in adult patients diagnosed with COVID-19 with information on lymphocyte count and several outcomes of interest, including mortality, acute respiratory distress syndrome (ARDS), intensive care unit (ICU) care, and severe COVID-19, were included in the analysis. Inverse variance method was used to obtain mean differences and its standard deviations. Maentel-Haenszel formula was used to calculate dichotomous variables to obtain odds ratios (ORs) along with its 95% confidence intervals. Random-effect models were used for meta-analysis regardless of heterogeneity. Restricted-maximum likelihood random-effects meta-regression was performed for age, gender, cardiac comorbidity, hypertension, diabetes mellitus, COPD, and smoking.ResultsThere were a total of 3099 patients from 24 studies. Meta-analysis showed that patients with poor outcome have a lower lymphocyte count (mean difference - 361.06 μL [- 439.18, - 282.95], p < 0.001; I 2 84%) compared to those with good outcome. Subgroup analysis showed lower lymphocyte count in patients who died (mean difference - 395.35 μL [- 165.64, - 625.07], p < 0.001; I 2 87%), experienced ARDS (mean difference - 377.56 μL [- 271.89, - 483.22], p < 0.001; I 2 0%), received ICU care (mean difference - 376.53 μL [- 682.84, - 70.22], p = 0.02; I 2 89%), and have severe COVID-19 (mean difference - 353.34 μL [- 250.94, - 455.73], p < 0.001; I 2 85%). Lymphopenia was associated with severe COVID-19 (OR 3.70 [2.44, 5.63], p < 0.001; I 2 40%). Meta-regression showed that the association between lymphocyte count and composite poor outcome was affected by age (p = 0.034).ConclusionThis meta-analysis showed that lymphopenia on admission was associated with poor outcome in patients with COVID-19.

Project description:Background and objectiveNeuropathic pain is an occasionally reported complication of coronavirus disease 2019 (COVID-19) that has received increased attention in scientific literature. In this systematic review and meta-analysis, we aimed to provide information on the frequency of neuropathic pain associated with COVID-19.Databases and data treatmentWe systematically reviewed and analysed literature regarding neuropathic pain associated with COVID-19. Literature searches were conducted in PubMed, EMBASE and Cochrane Library databases. We considered prospective and retrospective studies published up until September 2022 (limitations included English language, full-text publications and studies including at least 10 patients). A random effects meta-analysis was performed and heterogeneity and publication bias were assessed.ResultsWe identified 149 studies. We included 17 studies in the systematic review, and six studies reporting the frequency of neuropathic pain in the acute/subacute phase of COVID-19 in the meta-analysis. The estimated frequency of neuropathic pain ranged between 0.4 and 25%. Forest plot analysis showed that the random effect overall frequency was 10% (95% confidence interval: 5%-15%), with a high level of heterogeneity (Chi2 = 104; Tau2 = 0.004; df = 5; I2 = 95%; test for overall effect: Z = 3.584; p < 0.0005). The overall risk of bias was moderate in all studies selected, particularly due to the poor description of neuropathic pain diagnostic criteria.ConclusionsThe pooled estimated frequency of neuropathic pain associated with COVID-19 should be considered with caution due to the high heterogeneity across studies and the poor description of the neuropathic pain diagnostic criteria applied.SignificanceEmerging evidence supports the development of neuropathic pain as a complication of COVID-19. However, longitudinal studies enrolling consecutive patients with COVID-19 that detail the diagnostic criteria for neuropathic pain are needed to better assess the frequency of this condition.

Project description:Importance: Coronavirus disease 2019 (COVID-19) is a newly emerging infectious disease that has caused a global pandemic. The presenting symptoms are mainly respiratory symptom, yet studies have reported nervous system involvement in the disease. A systematic review and meta-analysis of these studies are required to understanding the neurologic characteristic of the disease and help physicians with early diagnosis and management. Objective: To conduct a systematic review and meta-analysis on the neurologic characteristics in patients with COVID-19. Evidence Review: Authors conducted a literature search through PubMed from January 1st, 2020 to April 8th, 2020. Furthermore, the authors added additional sources by reviewing related references. Studies presenting the neurologic features of COVID-19 patients in their data were included. Case reports and case series were also included in this review. The quality of the studies was assessed based on the Oxford Center for Evidence-Based Medicine guidelines. Selected studies were included in the meta-analysis of proportion and the heterogeneity test. Finding: From 280 identified studies, 33 were eligible, with 7,559 participants included. Most of the included studies were from China (29 [88%]). Muscle injury or myalgia was the most common (19.2%, 95%CI 15.4-23.2%) neurologic symptom of COVID-19, followed by headache (10.9%, 95%CI 8.62-13.51%); dizziness (8.7%, 95%CI 5.02-13.43%); nausea with or without vomiting (4.6%, 95%CI 3.17-6.27%); concurrent cerebrovascular disease (4.4%, 95%CI 1.92-7.91%); and impaired consciousness (3.8%, 95%CI 0.16-12.04%). Underlying cerebrovascular disease was found in 8.5% (95%CI 4.5-13.5%) of the studies. Conclusion: Neurologic findings vary from non-specific to specific symptoms in COVID-19 patients. Some severe symptoms or diseases can present in the later stage of the disease. Physicians should be aware of the presence of neurologic signs and symptoms as a chief complaint of COVID-19, in order to improve management and prevent a worsening outcome of the patients.

Dataset Information

Serial Intervals and Case Isolation Delays for Coronavirus Disease 2019: A Systematic Review and Meta-Analysis.

Background

Methods

Results

Conclusions

Publications

Serial Intervals and Case Isolation Delays for Coronavirus Disease 2019: A Systematic Review and Meta-Analysis.

Similar Datasets

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