Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Estimating the case fatality ratio for COVID-19 using a time-shifted distribution analysis.

ABSTRACT: Estimating the case fatality ratio (CFR) for COVID-19 is an important aspect of public health. However, calculating CFR accurately is problematic early in a novel disease outbreak, due to uncertainties regarding the time course of disease and difficulties in diagnosis and reporting of cases. In this work, we present a simple method for calculating the CFR using only public case and death data over time by exploiting the correspondence between the time distributions of cases and deaths. The time-shifted distribution (TSD) analysis generates two parameters of interest: the delay time between reporting of cases and deaths and the CFR. These parameters converge reliably over time once the exponential growth phase has finished. Analysis is performed for early COVID-19 outbreaks in many countries, and we discuss corrections to CFR values using excess-death and seroprevalence data to estimate the infection fatality ratio (IFR). While CFR values range from 0.2% to 20% in different countries, estimates for IFR are mostly around 0.5-0.8% for countries that experienced moderate outbreaks and 1-3% for severe outbreaks. The simplicity and transparency of TSD analysis enhance its usefulness in characterizing a new disease as well as the state of the health and reporting systems.

SUBMITTER: Thomas BS

PROVIDER: S-EPMC8438516 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Estimating case fatality rates of COVID-19.

Project description: Not available

| S-EPMC7270730 | biostudies-literature

Estimating the COVID-19 infection fatality ratio accounting for seroreversion using statistical modelling.

Project description:BackgroundThe infection fatality ratio (IFR) is a key statistic for estimating the burden of coronavirus disease 2019 (COVID-19) and has been continuously debated throughout the COVID-19 pandemic. The age-specific IFR can be quantified using antibody surveys to estimate total infections, but requires consideration of delay-distributions from time from infection to seroconversion, time to death, and time to seroreversion (i.e. antibody waning) alongside serologic test sensitivity and specificity. Previous IFR estimates have not fully propagated uncertainty or accounted for these potential biases, particularly seroreversion.MethodsWe built a Bayesian statistical model that incorporates these factors and applied this model to simulated data and 10 serologic studies from different countries.ResultsWe demonstrate that seroreversion becomes a crucial factor as time accrues but is less important during first-wave, short-term dynamics. We additionally show that disaggregating surveys by regions with higher versus lower disease burden can inform serologic test specificity estimates. The overall IFR in each setting was estimated at 0.49-2.53%.ConclusionWe developed a robust statistical framework to account for full uncertainties in the parameters determining IFR. We provide code for others to apply these methods to further datasets and future epidemics.

| S-EPMC9120146 | biostudies-literature

Estimating under-reporting of COVID-19 cases in Indian states: an approach using a delay-adjusted case fatality ratio.

Project description:ObjectivesThe COVID-19 pandemic has spread to all states in India. Due to limitations in testing coverage, the true extent of the spread may not be fully reflected in the reported cases. In this study, we obtain time-varying estimates of the fraction of COVID-19 infections reported in the different states.MethodsFollowing a methodology developed in prior work, we use a delay-adjusted case fatality ratio to estimate the true fraction of cases reported in different states. We also develop a delay adjusted test positivity estimation method and study the relationship between the estimated test positivity rate for each state and the estimated fraction of cases reported.SettingWe apply this method of analysis to all Indian states reporting at least 100 deaths as of 10 October 2020.ResultsOur analysis suggests that delay-adjusted case fatality ratios observed in different states range from 0.47% to 3.55%. The estimated fraction of cases reported in different states ranges from 39% to 100% for an assumed baseline case fatality ratio of 1.38%, from 18.6% to 100% for an assumed baseline case fatality ratio of 0.66%, and from 2.8% to 19.7% for an assumed baseline case fatality ratio of 0.1%. We also demonstrate a statistically significant negative relationship between the fraction of cases reported in each state and the testing positivity rate.ConclusionsThe estimates provide a means to quantify and compare the trends of reporting and the true level of current infections in different states. This information may be used to guide policies for prioritising testing in different states, and also to analyse the time-varying effects of different quarantine measures adopted in different states.

| S-EPMC7818846 | biostudies-literature

Case-Fatality Risk Estimates for COVID-19 Calculated by Using a Lag Time for Fatality.

Project description:We estimated the case-fatality risk for coronavirus disease cases in China (3.5%); China, excluding Hubei Province (0.8%); 82 countries, territories, and areas (4.2%); and on a cruise ship (0.6%). Lower estimates might be closest to the true value, but a broad range of 0.25%-3.0% probably should be considered.

| S-EPMC7258483 | biostudies-literature

Estimating the Case Fatality Risk of COVID-19 among Mechanically Ventilated Patients.

Project description: Not available

| S-EPMC7781131 | biostudies-literature

Estimating the impact of virus testing strategies on the COVID-19 case fatality rate using fixed-effects models.

Project description:The SARS-CoV2 has now spread worldwide causing over four million deaths. Testing strategies are highly variable between countries and their impact on mortality is a major issue. Retrospective multicenter study with a prospective database on all inpatients throughout mainland France. Using fixed effects models, we exploit policy discontinuities at region borders in France to estimate the effect of testing on the case fatality rate. In France, testing policies are determined at a regional level, generating exogenous variation in testing rates between departments on each side of a region border. We compared all contiguous department pairs located on the opposite sides of a region border. The increase of one percentage point in the test rate is associated with a decrease of 0.0015 percentage point in the death rate, that is, for each additional 2000 tests, we could observe three fewer deaths. Our study suggests that COVID-19 population testing could have a significant impact on the mortality rate which should be considered in decision-making. As concern grows over the current second wave of COVID-19, our findings support the implementation of large-scale screening strategies in such epidemic contexts.

| S-EPMC8569180 | biostudies-literature

Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020.

Project description:Adjusting for delay from confirmation to death, we estimated case and infection fatality ratios (CFR, IFR) for coronavirus disease (COVID-19) on the Diamond Princess ship as 2.6% (95% confidence interval (CI): 0.89-6.7) and 1.3% (95% CI: 0.38-3.6), respectively. Comparing deaths on board with expected deaths based on naive CFR estimates from China, we estimated CFR and IFR in China to be 1.2% (95% CI: 0.3-2.7) and 0.6% (95% CI: 0.2-1.3), respectively.

| S-EPMC7118348 | biostudies-literature

Understanding COVID-19: Digit ratio (2D:4D) and sex differences in national case fatality rates.

Project description:BACKGROUND:The reported national case fatality rates (CFRs) for coronavirus disease 2019 (COVID-19) shows a sex bias with males > females. The relative lengths of the index (2D) and ring (4D) fingers (digit ratio; 2D:4D) is a sexually dimorphic (males < females) proxy of fetal sex steroids (low 2D:4D indicates high prenatal testosterone/low prenatal estrogen). AIM:To examine sex-specific relationships of 2D:4D per nation with national values of COVID-19 CFRs. STUDY DESIGN:COVID-19 CFRs and the percent of male deaths were related to mean national (self-reported) 2D:4D by sex and hand from a large online survey (the BBC Internet Study). SUBJECTS:103,482 men and 83,366 women. OUTCOME MEASURES:Relationships of mean national 2D:4D with CFRs from 41 countries and with national male death rates from 16 countries. RESULTS:Male right and left hand 2D:4D showed positive relationships with CFR. These relationships remained significant after removing the influence of female 2D:4D. A positive association of male right and left 2D:4D was detected with the percentage of male deaths. CONCLUSIONS:At the national level, high mean 2D:4D (indicating low prenatal testosterone/high prenatal estrogen) is associated with high CFRs and percent male mortality. At the individual level, high 2D:4D may be a risk factor for severity of COVID-19 in males. We speculate that male 2D:4D is a negative correlate for expression of the SARS-CoV2 receptor (ACE2).

| S-EPMC7224643 | biostudies-literature

COVID-19 Case Fatality and Alzheimer's Disease.

Project description:Previous studies have identified dementia as a risk factor for death from coronavirus disease 2019 (COVID-19). However, it is unclear whether Alzheimer's disease (AD) is an independent risk factor for COVID-19 case fatality rate. In a retrospective cohort study, we identified 387,841 COVID-19 patients through TriNetX. After adjusting for demographics and comorbidities, we found that AD patients had higher odds of dying from COVID-19 compared to patients without AD (Odds Ratio: 1.20, 95%confidence interval: 1.09-1.32, p < 0.001). Interestingly, we did not observe increased mortality from COVID-19 among patients with vascular dementia. These data are relevant to the evolving COVID-19 pandemic.

| S-EPMC8841099 | biostudies-literature

Using Early Data to Estimate the Actual Infection Fatality Ratio from COVID-19 in France.

Project description:The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work is to estimate the actual number of people infected with COVID-19 and to deduce the IFR during the observation window in France. We develop a `mechanistic-statistical' approach coupling a SIR epidemiological model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method. The actual number of infected cases in France is probably higher than the observations: we find here a factor ×8 (95%-CI: 5-12) which leads to an IFR in France of 0.5% (95%-CI: 0.3-0.8) based on hospital death counting data. Adjusting for the number of deaths in nursing homes, we obtain an IFR of 0.8% (95%-CI: 0.45-1.25). This IFR is consistent with previous findings in China (0.66%) and in the UK (0.9%) and lower than the value previously computed on the Diamond Princess cruse ship data (1.3%).

| S-EPMC7284549 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data