COVID-19: Recovering estimates of the infected fatality rate during an ongoing pandemic through partial data

Villa, Matteo; Myers, James F.; Turkheimer, Federico

doi:10.1101/2020.04.10.20060764

Cited by 14 publications

(12 citation statements)

References 10 publications

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“…Studies varied widely in design, with 3 entirely modelled estimates ( [32] , [33] , [34] ), 4 observational studies ( [13] , [35] , [36] , [37] ), 5 pre-prints that were challenging to otherwise classify ( [2] , [38] , [39] , [40] , [41] ), and a number of serological surveys of varying types reported by government agencies ( [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] ). For the purposes of this research, an estimate for New York City was calculated from official statistics and the serosurvey, however this was correlated with a published estimate ( 31 ) to ensure validity.…”

Section: Resultsmentioning

confidence: 99%

A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates

Meyerowitz-Katz

Merone

2020

International Journal of Infectious Diseases

361

272

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An important unknown during the coronavirus disease-2019 (COVID-19) pandemic has been the infection fatality rate (IFR). This differs from the case fatality rate (CFR) as an estimate of the number of deaths and as a proportion of the total number of cases, including those who are mild and asymptomatic. While the CFR is extremely valuable for experts, IFR is increasingly being called for by policy makers and the lay public as an estimate of the overall mortality from COVID-19. Methods: Pubmed, Medline, SSRN, and Medrxiv were searched using a set of terms and Boolean operators on 25/04/2020 and researched on 14/05/2020, 21/05/2020 and 16/06/2020. Articles were screened for inclusion by both authors. Meta-analysis was performed in Stata 15.1 by using the metan command, based on IFR and confidence intervals extracted from each study. Google/Google Scholar was used to assess the grey literature relating to government reports. Results: After exclusions, there were 24 estimates of IFR included in the final meta-analysis, from a wide range of countries, published between February and June 2020. The meta-analysis demonstrated a point estimate of IFR of 0.68% (0.53%-0.82%) with high heterogeneity (p < 0.001). Conclusion: Based on a systematic review and meta-analysis of published evidence on COVID-19 until July 2020, the IFR of the disease across populations is 0.68% (0.53%-0.82%). However, due to very high heterogeneity in the meta-analysis, it is difficult to know if this represents a completely unbiased point estimate. It is likely that, due to age and perhaps underlying comorbidities in the population, different places will experience different IFRs due to the disease. Given issues with mortality recording, it is also likely that this represents an underestimate of the true IFR figure. More research looking at age-stratified IFR is urgently needed to inform policymaking on this front.

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Section: Resultsmentioning

confidence: 99%

A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates

Meyerowitz-Katz

Merone

2020

International Journal of Infectious Diseases

361

272

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“…A recent review and meta-analysis determined that a best estimate of the population IFR is 0.68%, with a 95% confidence interval of 0.58–0.82% [ 22 ]. Of the 26 studies that were included, individual best estimates of the IFR ranged from 0.09% [ 23 ] to 1.60% [ 24 ]. When the meta-analysis was restricted to the six studies that were considered to have a low risk of bias, a best estimate of 0.76% and a 95% confidence interval of 0.37–1.15% was obtained [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Robust estimates of the true (population) infection rate for COVID-19: a backcasting approach

2020

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Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 developed countries. Our sample comprised countries with similar levels of medical care and with populations that have similar age distributions. Monte Carlo methods were used to robustly sample parameter uncertainty. We found a strong and statistically significant negative relationship between the proportion of the population who test positive and the implied true detection rate. Despite an overall improvement in detection rates as the pandemic has progressed, our estimates showed that, as at 31 August 2020, the true number of people to have been infected across our sample of 15 countries was 6.2 (95% CI: 4.3–10.9) times greater than the reported number of cases. In individual countries, the true number of cases exceeded the reported figure by factors that range from 2.6 (95% CI: 1.8–4.5) for South Korea to 17.5 (95% CI: 12.2–30.7) for Italy.

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“…Estimating the severity of the coronavirus disease 2019 (COVID- 19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across demographic groups is of medical interest, but will also be crucial in the design of health and economic policies to deal with the next phases of the pandemic. Several approaches to measure lethality have been proposed since the start of the outbreak 1,2,3,4 , with most of the relevant population infected. Given the mounting evidence that a substantial proportion of infected people are either asymptomatic or display very mild symptoms 5,6 , as well as the difficulties many countries are encountering in ramping up testing, it has been difficult to obtain precise estimates of the total number of infected.…”

Section: Introductionmentioning

confidence: 99%

An empirical estimate of the infection fatality rate of COVID-19 from the first Italian outbreak

Rinaldi

Paradisi²

2020

Preprint

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Background: The coronavirus 2019 (COVID-19) pandemic has been spreading globally for months, yet the infection fatality ratio of the disease is still uncertain. This is partly because of inconsistencies in testing and death reporting standards across countries. We provide estimates which don't rely on official cases and deaths data but only on population level statistics. Methods: We collected demographic and death records data from the Italian Institute of Statistics. We focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years. We also assessed the sensitivity of our results to alternative assumptions on the proportion of population infected. Based on our estimates we finally studied the heterogeneity in overall lethality across countries. Findings: We estimate an overall infection fatality rate of 1.31% (95% credible interval [CrI] 0.94 − 1.89), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0 − 0.17) and a substantially higher 4.16% (CrI 3.05 − 5.80) for people above 60 years of age. In our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information. For instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60. Finally, using data on demographics we show large expected heterogeneity in overall IFR across countries. Interpretation: Our empirical estimates show a sharp difference in fatality rates between young and old people and rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.

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COVID-19: Recovering estimates of the infected fatality rate during an ongoing pandemic through partial data

Cited by 14 publications

References 10 publications

A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates

A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates

Robust estimates of the true (population) infection rate for COVID-19: a backcasting approach

An empirical estimate of the infection fatality rate of COVID-19 from the first Italian outbreak

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