Dataset Information

A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden.

ABSTRACT: Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2-43.6%) likelihood of developing respiratory symptoms or fever ≥ 37.5 °C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3-25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4-2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4-30.4%), 8.8% (95%CI: 7.3-10.5%) and 0.4% (95%CI: 0.1-0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0-0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9-19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1-2%) and 19.2% (95%CI: 10.9-30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3-21) days; the length of stay in ICU was 11 (IQR: 6-19) days. The obtained estimates provide insights into the epidemiology of COVID-19 and could be instrumental to refine mathematical modeling work supporting public health decisions.

SUBMITTER: Zardini A

PROVIDER: S-EPMC8595250 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundSARS-CoV-2 and influenza are lipid-enveloped viruses with differential morbidity and mortality but shared modes of transmission.ObjectiveWith a descriptive epidemiological framing, we assessed whether recent historical patterns of regional influenza burden are reflected in the observed heterogeneity in COVID-19 cases across regions of the world.MethodsWeekly surveillance data reported by the World Health Organization from January 2017 to December 2019 for influenza and from January 1, 2020 through October 31, 2020, for COVID-19 were used to assess seasonal and temporal trends for influenza and COVID-19 cases across the seven World Bank regions.ResultsIn regions with more pronounced influenza seasonality, COVID-19 epidemics have largely followed trends similar to those seen for influenza from 2017 to 2019. COVID-19 epidemics in countries across Europe, Central Asia, and North America have been marked by a first peak during the spring, followed by significant reductions in COVID-19 cases in the summer months and a second wave in the fall. In Latin America and the Caribbean, COVID-19 epidemics in several countries peaked in the summer, corresponding to months with the highest influenza activity in the region. Countries from regions with less pronounced influenza activity, including South Asia and sub-Saharan Africa, showed more heterogeneity in COVID-19 epidemics seen to date. However, similarities in COVID-19 and influenza trends were evident within select countries irrespective of region.ConclusionsEcological consistency in COVID-19 trends seen to date with influenza trends suggests the potential for shared individual, structural, and environmental determinants of transmission. Using a descriptive epidemiological framework to assess shared regional trends for rapidly emerging respiratory pathogens with better studied respiratory infections may provide further insights into the differential impacts of nonpharmacologic interventions and intersections with environmental conditions. Ultimately, forecasting trends and informing interventions for novel respiratory pathogens like COVID-19 should leverage epidemiologic patterns in the relative burden of past respiratory pathogens as prior information.

Project description:(1) Background: Respiratory insufficiency with acute respiratory distress syndrome (ARDS) and multi-organ dysfunction leads to high mortality in COVID-19 patients. In times of limited intensive care unit (ICU) resources, chest CTs became an important tool for the assessment of lung involvement and for patient triage despite uncertainties about the predictive diagnostic value. This study evaluated chest CT-based imaging parameters for their potential to predict in-hospital mortality compared to clinical scores. (2) Methods: 89 COVID-19 ICU ARDS patients requiring mechanical ventilation or continuous positive airway pressure mask ventilation were included in this single center retrospective study. AI-based lung injury assessment and measurements indicating pulmonary hypertension (PA-to-AA ratio) on admission CT, oxygenation indices, lung compliance and sequential organ failure assessment (SOFA) scores on ICU admission were assessed for their diagnostic performance to predict in-hospital mortality. (3) Results: CT severity scores and PA-to-AA ratios were not significantly associated with in-hospital mortality, whereas the SOFA score showed a significant association (p < 0.001). In ROC analysis, the SOFA score resulted in an area under the curve (AUC) for in-hospital mortality of 0.74 (95%-CI 0.63-0.85), whereas CT severity scores (0.53, 95%-CI 0.40-0.67) and PA-to-AA ratios (0.46, 95%-CI 0.34-0.58) did not yield sufficient AUCs. These results were consistent for the subgroup of more critically ill patients with moderate and severe ARDS on admission (oxygenation index <200, n = 53) with an AUC for SOFA score of 0.77 (95%-CI 0.64-0.89), compared to 0.55 (95%-CI 0.39-0.72) for CT severity scores and 0.51 (95%-CI 0.35-0.67) for PA-to-AA ratios. (4) Conclusions: Severe COVID-19 disease is not limited to lung (vessel) injury but leads to a multi-organ involvement. The findings of this study suggest that risk stratification should not solely be based on chest CT parameters but needs to include multi-organ failure assessment for COVID-19 ICU ARDS patients for optimized future patient management and resource allocation.

Project description:Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter. Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively. Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan. Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight nonessential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread. Significance for public health In fighting global pandemic such as COVID-19, an important early task for understanding the spread is to closely monitor the infection size and assess the disease epidemiological parameters. The in- sights gained from the epidemiological parameter estimation enable public health practitioners to dynamically monitor the temporal spread trend and to quantitatively analyze the effectiveness of new public health policies. In this paper, we aim to address a key technical challenge potentially arising from the under-reporting issues in pandemic early periods, and critically re-examine the COVID-19 situation at the initial epicenter Wuhan city as a practically relevant case study. Methodological development for modeling dynamic evolution involving parameter estimation therefore has important public health applications and is expected to have significant impact on modeling practice for understanding future epidemic events well beyond COVID-19.

Project description:BackgroundCOVID-19 threatens the global community because a large fraction of infected people are asymptomatic, yet can effectively transmit SARS-CoV-2. Finding and isolating these silent carriers is a crucial step in confining the spread of the disease. A sudden loss of the sense of smell has been self-reported by COVID-19 patients across different countries, consistent with expression of the molecular factors mediating SARS-CoV-2 uptake into human olfactory epithelial supporting cells. However, precise quantification of olfactory loss in asymptomatic COVID-19 carriers is missing to date.MethodsTo quantify olfactory functions in asymptomatic COVID-19 patients, we designed an olfactory-action meter that determines detectability indices at different odor concentrations and an olfactory matching accuracy score using monomolecular odors. The optimization of test parameters allowed us to reliably and accurately assess olfactory deficits in a patient within 20 minutes.FindingsMeasurement of detection indices at low concentrations revealed a 50% reduction in asymptomatic COVID-19 carriers. Further, patients with better detection scores showed significantly reduced olfactory matching accuracies compared to normal healthy subjects. Our quantification of olfactory loss, considering all parameters, identified 82% of the asymptomatic SARS-CoV-2 carriers with olfactory deficits. However, on subjective evaluation, only 15% of the patients noticed a compromised ability to smell.InterpretationCompromised olfactory fitness can serve as a strong basis for identifying asymptomatic COVID-19 patients. Detailed design specifications and protocols provided here should enable the development of a sensitive, fast, and economical screening strategy that can be administered to large populations to prevent the rapid spread of COVID-19.FundingThis work was supported by the DBT - Wellcome Trust India Alliance intermediate grant (IA/I/14/1/501,306 to N.A.) and UGC NET Fellowship (A.B.). All the funding sources played no roles in the study.

Project description:Rationale, aims, and objectivesCOVID-19 has caused an ongoing public health crisis. Many systematic reviews and meta-analyses have been performed to synthesize evidence for better understanding this new disease. However, some concerns have been raised about rapid COVID-19 research. This meta-epidemiological study aims to methodologically assess the current systematic reviews and meta-analyses on COVID-19.MethodsWe searched in various databases for systematic reviews with meta-analyses published between 1 January 2020 and 31 October 2020. We extracted their basic characteristics, data analyses, evidence appraisal, and assessment of publication bias and heterogeneity.ResultsWe identified 295 systematic reviews on COVID-19. The median time from submission to acceptance was 33 days. Among these systematic reviews, 73.9% evaluated clinical manifestations or comorbidities of COVID-19. Stata was the most used software programme (43.39%). The odds ratio was the most used effect measure (34.24%). Moreover, 28.14% of the systematic reviews did not present evidence appraisal. Among those reporting the risk of bias results, 14.64% of studies had a high risk of bias. Egger's test was the most used method for assessing publication bias (38.31%), while 38.66% of the systematic reviews did not assess publication bias. The I2 statistic was widely used for assessing heterogeneity (92.20%); many meta-analyses had high values of I2 . Among the meta-analyses using the random-effects model, 75.82% did not report the methods for model implementation; among those meta-analyses reporting implementation methods, the DerSimonian-Laird method was the most used one.ConclusionsThe current systematic reviews and meta-analyses on COVID-19 might suffer from low transparency, high heterogeneity, and suboptimal statistical methods. It is recommended that future systematic reviews on COVID-19 strictly follow well-developed guidelines. Sensitivity analyses may be performed to examine how the synthesized evidence might depend on different methods for appraising evidence, assessing publication bias, and implementing meta-analysis models.

Dataset Information

A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets