Predicted COVID-19 fatality rates based on age, sex, comorbidities and health system capacity

Ghisolfi, Selene; Almås, Ingvild; Sandefur, Justin; Carnap, Tillman von; Heitner, Jesse; Bold, Tessa

doi:10.1136/bmjgh-2020-003094

Cited by 86 publications

(70 citation statements)

References 16 publications

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“…In Banerjee et al (2020), excess counts of deaths over one year in different levels of transmission of COVID-19 are determined. In Ghisolfi et al (2020), the fatality rate for Eastern Europe nations are estimated. In the USA, CDCP-Centers for Disease Control and Prevention, instructs the people to stay at home when they are sick, avoid touching nose and mouth by covering them, and frequently wash hands using soap before and after touching any object, to avoid the spread of coronavirus (Centers for Disease Control and Prevention 2020).…”

Section: Literature Survey On Mathematical Modelsmentioning

confidence: 99%

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

Selvakumar

Lokesh

2021

Soft Comput

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The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath. And, the disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. in Radiology 295(1):202–207, 2020). It is an international real-life problem. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people. In this article, we aim to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using three mathematical models. This article deals with a complex real-life problem people face all over the world, an international real-life problem. The main focus is on the USA due to large infection and death due to coronavirus and thereby the life of every individual is uncertain. The death counts of the USA from February 29 to April 22, 2020, are used in this article as a data set. The death counts of the USA are fitted by the solutions of three mathematical models and a solution to an international problem is achieved. Based on the death rate, the lifetime of the coronavirus COVID-19 is predicted as 1464.76 days from February 29, 2020. That is, after March 2024 there will be no death in the USA due to COVID-19 if everyone follows the guidelines of WHO and the advice of healthcare workers. People and government can get prepared for this situation and many lives can be saved. It is the contribution of soft computing. Finally, this article suggests several steps to control the spread and severity of the disease. The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature.

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Section: Literature Survey On Mathematical Modelsmentioning

confidence: 99%

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

Selvakumar

Lokesh

2021

Soft Comput

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“…4 13-16 Some studies have adjusted mortality estimates for population comorbidities by treating all comorbidities as equivalent or by multiplying the mortality rate by a fixed amount to adjust for population health. [17][18][19][20] One study combined condition-specific prevalence and HRs from a sample of hospitalisations, but excluded obesity and uncontrolled diabetes, and did not examine mortality or the age distribution of mortality as outcomes. 21 Using England as a benchmark, this study examines how comorbidities understood to increase COVID-19 mortality are likely to affect COVID-19 mortality rates in aggregate and across the age distribution in India, identifying the specific risk factors with the largest mortality effects.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 mortality effects of underlying health conditions in India: a modelling study

et al. 2020

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ObjectiveTo model how known COVID-19 comorbidities affect mortality rates and the age distribution of mortality in a large lower-middle-income country (India), and to identify which health conditions drive differences with high-income countries.DesignModelling study.SettingEngland and India.ParticipantsIndividual data were obtained from the fourth round of the District Level Household Survey and Annual Health Survey in India, and aggregate data were obtained from the Health Survey for England and the Global Burden of Disease, Risk Factors and Injuries Studies.Main outcome measuresThe primary outcome was the modelled age-specific mortality in each country due to each COVID-19 mortality risk factor (diabetes, hypertension, obesity and respiratory illness, among others). The change in overall mortality and in the share of deaths under age 60 from the combination of risk factors was estimated in each country.ResultsRelative to England, Indians have higher rates of diabetes (10.6% vs 8.5%) and chronic respiratory disease (4.8% vs 2.5%), and lower rates of obesity (4.4% vs 27.9%), chronic heart disease (4.4% vs 5.9%) and cancer (0.3% vs 2.8%). Population COVID-19 mortality in India, relative to England, is most increased by uncontrolled diabetes (+5.67%) and chronic respiratory disease (+1.88%), and most reduced by obesity (−5.47%), cancer (−3.65%) and chronic heart disease (−1.20%). Comorbidities were associated with a 6.26% lower risk of mortality in India compared with England. Demographics and population health explain a third of the difference in share of deaths under age 60 between the two countries.ConclusionsKnown COVID-19 health risk factors are not expected to have a large effect on mortality or its age distribution in India relative to England. The high share of COVID-19 deaths from people under age 60 in low- and middle-income countries (LMICs) remains unexplained. Understanding the mortality risk associated with health conditions prevalent in LMICs, such as malnutrition and HIV/AIDS, is essential for understanding differential mortality.

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“…There have been few studies reporting IFRs for younger nations such as in Africa, possibly due to difficulties in testing, measuring the number of asymptomatic infections and reporting accurate death rates. It has been suggested the different regions will experience different IFRs due to age structure and co-morbidities [6, 17-18]. A single estimate of the IFR for all nations may not capture the true global distribution.…”

Section: Discussionmentioning

confidence: 99%

Clustering of age standardised COVID-19 infection fatality ratios and death trajectories

Kelly

Humphrey²,

Miller

et al. 2020

Preprint

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Background An accurate measure of the impact of COVID-19 is the infection fatality ratio, or the proportion of deaths among those infected, which does not depend on variable testing rates between nations. The risk of mortality from COVID-19 depends strongly on age and current estimates of the infection fatality ratio do not account for differences in national age profiles. Comparisons of cumulative death trajectories allow the effect and timing of public health interventions to be assessed. Our purpose is to (1) determine whether countries are clustered according to infection fatality ratios and (2) compare interventions to slow the spread of the disease by clustering death trajectories. Methods National age standardised infection fatality ratios were derived from age stratified estimates from China and population estimates from the World Health Organisation. The IFRs were clustered into groups using Gaussian mixture models. Trajectory analysis clustered cumulative death rates in two time windows, 50 and 100 days after the first reported death. Findings Infection fatality ratios from 201 nations were clustered into three groups: young, medium and older, with corresponding means (SD) of 0.20% (0.03%), 0.38% (0.11%) and 0.93% (0.21%). At 50 and 100 days after the first reported death, there were two clusters of cumulative death trajectories from 113 nations with at least 25 deaths reported at 100 days. The first group had slowly increasing or stable cumulative death rates, while the second group had accelerating rates at the end of the time window. Fifty-two nations changed group membership between the time windows. Conclusion A cluster of younger nations have a lower estimated infection fatality ratio than older nations. The effect and timing of public health interventions in preventing the spread of the disease can be tracked by clustering death rate trajectories into stable or accelerating and comparing changes over time.

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Predicted COVID-19 fatality rates based on age, sex, comorbidities and health system capacity

Cited by 86 publications

References 16 publications

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

COVID-19 mortality effects of underlying health conditions in India: a modelling study

Clustering of age standardised COVID-19 infection fatality ratios and death trajectories

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