Literature DB >> 33596249

A scaling approach to estimate the age-dependent COVID-19 infection fatality ratio from incomplete data.

Beatriz Seoane1.   

Abstract

SARS-CoV-2 has disrupted the life of billions of people around the world since the first outbreak was officially declared in China at the beginning of 2020. Yet, important questions such as how deadly it is or its degree of spread within different countries remain unanswered. In this work, we exploit the 'universal' increase of the mortality rate with age observed in different countries since the beginning of their respective outbreaks, combined with the results of the antibody prevalence tests in the population of Spain, to unveil both unknowns. We test these results with an analogous antibody rate survey in the canton of Geneva, Switzerland, showing a good agreement. We also argue that the official number of deaths over 70 years old might be importantly underestimated in most of the countries, and we use the comparison between the official records with the number of deaths mentioning COVID-19 in the death certificates to quantify by how much. Using this information, we estimate the infection fatality ratio (IFR) for the different age segments and the fraction of the population infected in different countries assuming a uniform exposure to the virus in all age segments. We also give estimations for the non-uniform IFR using the sero-epidemiological results of Spain, showing a very similar increase of the fatality ratio with age. Only for Spain, we estimate the probability (if infected) of being identified as a case, being hospitalized or admitted in the intensive care units as function of age. In general, we observe a nearly exponential increase of the fatality ratio with age, which anticipates large differences in total IFR in countries with different demographic distributions, with numbers that range from 1.82% in Italy, to 0.62% in China or even 0.14% in middle Africa.

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Year:  2021        PMID: 33596249      PMCID: PMC7888669          DOI: 10.1371/journal.pone.0246831

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  38 in total

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Journal:  Clin Infect Dis       Date:  2021-01-27       Impact factor: 20.999

3.  Estimates of the severity of coronavirus disease 2019: a model-based analysis.

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Journal:  Lancet Infect Dis       Date:  2020-03-30       Impact factor: 25.071

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5.  Clinical Characteristics of Coronavirus Disease 2019 in China.

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Journal:  N Engl J Med       Date:  2020-02-28       Impact factor: 91.245

6.  Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study.

Authors:  Qifang Bi; Yongsheng Wu; Shujiang Mei; Chenfei Ye; Xuan Zou; Zhen Zhang; Xiaojian Liu; Lan Wei; Shaun A Truelove; Tong Zhang; Wei Gao; Cong Cheng; Xiujuan Tang; Xiaoliang Wu; Yu Wu; Binbin Sun; Suli Huang; Yu Sun; Juncen Zhang; Ting Ma; Justin Lessler; Tiejian Feng
Journal:  Lancet Infect Dis       Date:  2020-04-27       Impact factor: 25.071

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Journal:  Lancet Infect Dis       Date:  2020-03-31       Impact factor: 25.071

8.  Time-to-Death approach in revealing Chronicity and Severity of COVID-19 across the World.

Authors:  Vivek Verma; Ramesh K Vishwakarma; Anita Verma; Dilip C Nath; Hafiz T A Khan
Journal:  PLoS One       Date:  2020-05-12       Impact factor: 3.240

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Journal:  Lancet Infect Dis       Date:  2020-03-12       Impact factor: 25.071

10.  Estimating the severity of COVID-19: Evidence from the Italian epicenter.

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Journal:  PLoS One       Date:  2020-10-01       Impact factor: 3.240

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  2 in total

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