Age-specific mortality and immunity patterns of SARS-CoV-2

O’Driscoll, Megan; Santos, Gabriel Ribeiro dos; Wang, Lin; Cummings, Derek A. T.; Azman, Andrew S.; Paireau, Juliette; Fontanet, Arnaud; Cauchemez, Simon; Salje, Henrik

doi:10.1038/s41586-020-2918-0

Cited by 1,099 publications

(1,220 citation statements)

References 29 publications

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“…High mortality in European countries was primarily due to high CFR, and high mortality in American countries was primarily due to high prevalence. CFR was high in Western Europe, 11 Mexico, Canada, and Ecuador, where the prevalence was not very high. In contrast, Chile, USA, Peru, and Brazil had high prevalence, but CFR was equal to or less than that of Asian countries such as Japan.…”

Section: Resultsmentioning

confidence: 90%

Epidemic trends of SARS-CoV-2 associated with immunity, race, and viral mutations

Takahashi

Kamikubo

Hattori

2020

Preprint

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The world has been plagued by complex waves of SARS-CoV-2 epidemics that vary from region to region, leaving the end of the pandemic unpredictable. Here we performed "genetic fingerprinting" to compare the local viral genotypes with epidemiological information and reveal the molecular dynamics of the SARS-CoV-2 epidemic worldwide. A multifaceted analyses of the epidemic trends and their relationship to virus genotypes, regional herd immunity, population density, and race has shown that epidemic outcomes are affected by: (1) Increased fitness of the virus due to mutations of viral proteins; (2) Immunity against previously prevalent subtypes that prevents or exacerbates COVID-19; (3) Immune evasion due to viral mutations; (4) Viral competition with coexisting subtypes; (5) Dense and crowded living environment; (6) Racial and social disparities; (7) Upper limit of viral mutations that enable natural selection. These findings provide an overview of the current epidemic, help predict the future, and develop effective countermeasures.

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Section: Resultsmentioning

confidence: 90%

Epidemic trends of SARS-CoV-2 associated with immunity, race, and viral mutations

Takahashi

Kamikubo

Hattori

2020

Preprint

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“…We note that to estimate the mean IFR for each province, we re-weighted the IFR of each age group by its respective share of the total population in each province. This procedure relies on the assumption that contagion was homogeneous across all age groups which may not be true due to reasons such as the heterogeneity in the burden of the epidemic in nursing homes [14] and potential age-varying susceptibility to infection by SARS-CoV-2 [26]. However, a recent analysis shows that the increase in mortality by age from COVID-19 strongly resembles the age pattern of all-cause mortality [27].…”

Section: Discussionmentioning

confidence: 99%

“…To calculate the standard deviation in Y, we took the age-stratified 95%CI estimates from [14] Table 1: Seasonal trend of provinces with (+ sign) or without (-sign) significant levels of excess mortality from fall 1398 SH to summer 1399 SH.…”

Section: Methodsmentioning

confidence: 99%

Excess deaths associated with the Iranian COVID-19 epidemic: a province-level analysis

Ghafari

Kadivar²

2020

Preprint

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BackgroundThe number of publicly reported deaths from COVID-19 may underestimate the true death toll from the epidemic as they rely on provisional data that are often incomplete or omit undocumented deaths from COVID-19. In addition, these reports may be subject to significant under-reporting due to a limited testing capacity of a country to identify suspect cases. This study estimated the number of seasonal excess deaths attributable to the COVID-19 epidemic in 31 provinces of Iran.MethodsWe gathered the nationwide and provincial time series of the seasonal all-cause mortality data from spring 2015 to summer 2020 (21 March 2015 to 21 September 2020), in accordance with the Solar Hijri (SH) calendar, from the National Organization for Civil Registration (NOCR). We estimated the expected number of seasonal deaths for each province using a piecewise linear regression model which we established based on the mortality figures for the previous years and considered any significant deviations from the expectation during winter, spring, and summer of 2020 to be directly associated with COVID-19.ResultsOur analysis shows that from the start of winter to the end of summer (from 22 December 2019 to 21 September 2020), there were a total of 58.9K (95%CI: 46.9K - 69.5K) excess deaths across all 31 provinces with 27% (95%CI: 20% - 34%) estimated nationwide exposure to SARS-CoV-2. In particular, 2 provinces in the central and northern Iran, namely Qom and Golestan, had the highest level of exposure with 57% (95%CI: 44% - 69%) and 56% (95%CI: 44% - 69%), respectively, while another 27 provinces had significant levels of excess mortality in at least one season with >20% population-level exposure to the virus. We also detected unexpectedly high levels of excess mortality during fall 2019 (from 23 September to 21 December 2019) across 18 provinces. Our findings suggest that this spike cannot be a result of an early cryptic transmission of COVID-19 across the country and is also inconsistent with the molecular phylogenetics estimates for the start of the pandemic and its arrival to Iran. However, in the absence of appropriate surveillance data for detecting severe acute respiratory infections we were unable to make a determination as to what caused the spike in fall 2019.Conflict of InterestNone.

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“…Compared to other pandemics, one would have to regard SARS-CoV-2 as moderately infectious, much less than measles, but more than influenza. As for its lethality, a model framework estimated a population infection fatality rates of 0.79% (with notable heterogeneity) 21 and a recent meta-analysis found a median corrected COVID-19 infection fatality rate of 0.23% 22 -against an infection fatality rate of 0.1%-0.2% for influenza. 23 Measles is a good illustration of the fact that infection fatality rates are not a constant variable of a disease, but depend on contextual factors and social determinants, and evolve over time.…”

Section: Our Information About Sars-cov-2 and Covid-19 Is Rapidly Chamentioning

confidence: 99%

"When My Information Changes, I Alter My Conclusions." What Can We Learn From the Failures to Adaptively Respond to the SARS-CoV-2 Pandemic and the Under Preparedness of Health Systems to Manage COVID-19?

Paul

Brown

Kalk³

et al. 2020

Int J Health Policy Manag

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Age-specific mortality and immunity patterns of SARS-CoV-2

Cited by 1,099 publications

References 29 publications

Epidemic trends of SARS-CoV-2 associated with immunity, race, and viral mutations

Epidemic trends of SARS-CoV-2 associated with immunity, race, and viral mutations

Excess deaths associated with the Iranian COVID-19 epidemic: a province-level analysis

"When My Information Changes, I Alter My Conclusions." What Can We Learn From the Failures to Adaptively Respond to the SARS-CoV-2 Pandemic and the Under Preparedness of Health Systems to Manage COVID-19?

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