Dataset Information

Different SARS-CoV-2 haplotypes associate with geographic origin and case fatality rates of COVID-19 patients.

ABSTRACT: The current pandemic of COVID-19 is caused by the SARS-CoV-2 virus for which many variants at the Single Nucleotide Polymorphism (SNP) level have now been identified. We show here that different allelic variants among 692 SARS-CoV-2 genome sequences display a statistically significant association with geographic origin (p < 0.000001) and COVID-19 case severity (p = 0.016). Geographic variation in itself is associated with both case severity and allelic variation especially in strains from Indian origin (p < 0.000001). Using an new alternative bioinformatics approach we were able to confirm that the presence of the D614G mutation correlates with increased case severity in a sample of 127 sequences from a shared geographic origin in the US (p = 0.018). While leaving open the question on the pathogenesis mechanism involved, this suggests that in specific geographic locales certain genotypes of the virus are more pathogenic than others. We here show that viral genome polymorphisms may have an effect on case severity when other factors are controlled for, but that this effect is swamped out by these other factors when comparing cases across different geographic regions.

SUBMITTER: Goyal M

PROVIDER: S-EPMC7837616 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Different SARS-CoV-2 haplotypes associate with geographic origin and case fatality rates of COVID-19 patients.

Goyal Manisha M De Bruyne Katrien K van Belkum Alex A West Brian B

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases 20210126

The current pandemic of COVID-19 is caused by the SARS-CoV-2 virus for which many variants at the Single Nucleotide Polymorphism (SNP) level have now been identified. We show here that different allelic variants among 692 SARS-CoV-2 genome sequences display a statistically significant association with geographic origin (p < 0.000001) and COVID-19 case severity (p = 0.016). Geographic variation in itself is associated with both case severity and allelic variation especially in strains from Indian ...[more]

PMID: 33513449

Similar Datasets

Project description:BackgroundIn 2021, new variants of the SARS-CoV-2 virus appeared with increased transmissibility and virulence as compared with the original wild variant. The first variants of concern (VoCs), Alpha (B1.1.7) and Gamma (P.1), first appeared in the United Kingdom and Brazil, respectively. The Delta (B.1.617.2) variant, seen in India in October 2020, dominated COVID-19 infections across all regions through the second half of 2021.ObjectiveThis research explores the degree to which SARS-CoV-2 VoCs generate waves of fluctuations in case-fatality rates (CFRs) across countries in several regions, increase the risk of mortality to persons with certain comorbidities, and decrease the risk of mortality as the percentage of fully vaccinated populations increases.MethodsThis analysis introduces a measure of the temporal dynamics of COVID-19 infections in the form of a proxy CFR (pCFR), which can be compared among countries. It uses economic and demographic data reported by the World Bank and International Monetary Fund, plus publicly available epidemiological and medical statistics reported to the relevant national and international public health authorities. From these ecological data, pandemic average and daily COVID-19 CFRs and their correlations with potential cofactors were computed for 2021, a year dominated by the spread of World Health Organization-designated VoCs. The study does not investigate disease pathology; rather, it compares the daily case rates and pCFRs to reveal underlying contributing factors that vary from country to country and region to region.ResultsThe in-depth global regression analysis of cofactors found that the strongest single correlation with COVID-19 fatality was 0.36 (SD 0.02) with P<.001 for chronic kidney disease. No other single physiological cofactors display positive correlations exceeding 0.26 (SD 0.26), with P=.008 (asthma) and P=.01 (coronary disease). The study confirms that the pCFR is a valuable metric for tracking waves of infection due to different VoCs within countries.ConclusionsThe influence of social, economic, and medical cofactors on the CFR due to VoCs remains qualitatively similar, albeit strengthened, to the levels found for the wild strain. The strong regional variations of the influence of all cofactors observed for the wild strain persists in infections for all VoCs with very strong correlation coefficients seen in the Middle East for asthma (0.76), coronary heart disease (0.60), lung disease (0.70), and chronic kidney disease (0.52). Strong regional variations emphasize the influence on COVID-19 mortality due to regional differences in national economics, patterns of health care policies, and variations in cultural practices and environment. The pCFR-based analysis reveals clear patterns of the spread of VoCs across regions, but there is little evidence for the spread of the Lambda and Mu (B.1.621) variants of interest outside of South America.

Project description:INTRODUCTION:The SARS-CoV-2 pandemic has posed a real challenge to health systems. In Spain, the heterogeneous distribution of the virus infection and the different health strategies have conditioned the morbidity and fatality rate. The aim of this study was to analyse the lethality of the infection by sex and age range in the Autonomous Communities (AC) of Spain. MATERIAL AND METHODS:To perform the analysis, data were extracted from the Ministry of Health, Regional and Public Health Departments of the different AC. The infected population was estimated from the results of the ENE-COVID19 and the population registered on 1 January 2020 (INE) for the validity of the IgG antibody test with 80% sensitivity and 100% specificity. The case fatality rate (TL) (deaths/1000 estimated infected) by sex and age (<20years, 20-64 and ?65years) was calculated for each AC. The standardized case fatality ratio (REL) was calculated by the exact method (EPIDAT). RESULTS:The estimated prevalence of SARS-CoV-2 infection in Spain was 6% (range, 1.4% [Ceuta] -14.1% [Community of Madrid]). The TL in Spain was 9,6/1000, ranged per AC from 1/1000 in Melilla to 26.6/1000 in La Rioja, with no correlation between case fatality and prevalence of infection. The TL was higher in men (10.2/1000, ratio 1.17 with respect to women), except in Cataluña (ratio 0.92), and especially high in those over 64years of age in La Rioja (143.5/1000), Asturias (69.2/1000) and Basque Country (46.6/1000). Overall excess REL was found to be over 30% in La Rioja (2.91; 95%CI: 2.36-3.57), Asturias (1.51; 95%CI: 1.27-1.80), Basque Country (1.42; 95%CI: 1.31-1.54) and Extremadura (1.37; 95%CI: 1.20-1.57) and in those over 64 years in Madrid and the Canary Islands. CONCLUSIONS:SARs-CoV-2 virus infection has been very unevenly distributed in the different ACs, with notably differences in TL between ACs, particularly high in La Rioja, Asturias and the Basque Country. Is important to study the excess in TL the population over 64years of age in the ACs of Madrid and the Canary Islands.

Project description:There has been much discussion and debate around underreporting of deaths in India in media articles and in the scientific literature. In this brief report, we aim to meta-analyze the available/inferred estimates of infection fatality rates for SARS-CoV-2 in India based on the existent literature. These estimates account for uncaptured deaths and infections. We consider empirical excess death estimates based on all-cause mortality data as well as disease transmission-based estimates that rely on assumptions regarding infection transmission and ascertainment rates in India. Through an initial systematic review (Zimmermann et al., 2021) that followed PRISMA guidelines and comprised a search of databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv, and SSRN for preprints (accessed through iSearch) on July 3, 2021, we further extended the search verification through May 26, 2022. The screening process yielded 15 studies qualitatively analyzed, of which 9 studies with 11 quantitative estimates were included in the meta-analysis. Using a random effects meta-analysis framework, we obtain a pooled estimate of nationwide infection fatality rate (defined as the ratio of estimated deaths over estimated infections) and a corresponding confidence interval. Death underreporting from excess deaths studies varies by a factor of 6.1-13.0 with nationwide cumulative excess deaths ranging from 2.6-6.3 million, whereas the underreporting from disease transmission-based studies varies by a factor of 3.5-7.3 with SARS-CoV-2 related nationwide estimated total deaths ranging from 1.4-3.4 million, through June 2021 with some estimates extending to 31 December 2021. Underreporting of infections was found previously (Zimmermann et al., 2021) to be 24.9 (relying on the latest 4th nationwide serosurvey from 14 June-6 July 2021 prior to launch of the vaccination program). Conservatively, by considering the lower values of these available estimates, we infer that approximately 95% of infections and 71% of deaths were not accounted for in the reported figures in India. Nationwide pooled infection fatality rate estimate for India is 0.51% (95% confidence interval [CI]: 0.45%- 0.58%). We often tend to compare countries across the world in terms of total reported cases and deaths. Although the US has the highest number of reported cumulative deaths globally, after accounting for underreporting, India appears to have the highest number of cumulative total deaths (reported + unreported). However, the large number of estimated infections in India leads to a lower infection fatality rate estimate than the US, which in part is due to the younger population in India. We emphasize that the age-structure of different countries must be taken into consideration while making such comparisons. More granular data are needed to examine heterogeneities across various demographic groups to identify at-risk and underserved populations with high COVID mortality; the hope is that such disaggregated mortality data will soon be made available for India.

Dataset Information

Different SARS-CoV-2 haplotypes associate with geographic origin and case fatality rates of COVID-19 patients.

Publications

Different SARS-CoV-2 haplotypes associate with geographic origin and case fatality rates of COVID-19 patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets