Dataset Information

The cross-scale correlations between individuals and nations in COVID-19 mortality.

ABSTRACT: It is challenging to quantitatively clarify the determining medical and social factors of COVID-19 mortality, which varied by 2 to 3 orders of magnitude across countries. Here, we present evidence that the temporal evolution of mortality follows a logistic law for 54 countries in four waves. A universal linear law is found between the early mortality growth time and the epidemic duration, one of the most important quantities, with a factor of 7.3 confirmed by data. Saturation mortality is found to have a power law relationship with median age and bed occupancy, which quantitatively explains the great variation in mortality based on the two key thresholds of median age (= 38) and bed occupancy (= 22%). We predict that deaths will be reduced by 38.5% when the number of beds is doubled for countries with older populations. Facing the next wave of the epidemic, this model can make early predictions on the epidemic duration and hospital bed demand.

SUBMITTER: Zhang L

PROVIDER: S-EPMC9380985 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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The cross-scale correlations between individuals and nations in COVID-19 mortality.

Zhang Lei L She Yu-Rong YR She Guang-Hui GH Li Rong R She Zhen-Su ZS

Scientific reports 20220816 1

It is challenging to quantitatively clarify the determining medical and social factors of COVID-19 mortality, which varied by 2 to 3 orders of magnitude across countries. Here, we present evidence that the temporal evolution of mortality follows a logistic law for 54 countries in four waves. A universal linear law is found between the early mortality growth time and the epidemic duration, one of the most important quantities, with a factor of 7.3 confirmed by data. Saturation mortality is found ...[more]

PMID: 35974130

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Project description:Using RNA-seq and high-resolution mass spectrometry we performed a comprehensive systems analysis on 128 plasma and leukocyte samples from hospitalized patients with or without COVID-19 (n=102 and 26 respectively) and with differing degrees of disease severity. We generated abundance measurements for over 17,000 transcripts, proteins, metabolites, and lipids and compiled them with clinical data into a curated relational database. This resource offers the unique opportunity to perform systems analysis and cross-ome correlations to both molecules and patient outcomes. In total 219 molecular features were mapped with high significance to COVID-19 status and severity, including those involved in processes such as complement system activation, dysregulated lipid transport, and B cell activation. In one example, we detected a trio of covarying molecules – citrate, plasmenyl-phosphatidylcholines, and gelsolin (GSN) – that offer both pathophysiological insight and potential novel therapeutic targets. Further, our data revealed in some cases, and supported in others, that several biological processes were dysregulated in COVID-19 patients including vessel damage, platelet activation and degranulation, blood coagulation, and acute phase response. For example, we observed that the coagulation-related protein, cellular fibronectin (cFN), was highly increased within COVID-19 patients and provide new evidence that the upregulated proteoform stems from endothelial cells, consistent with endothelial injury as a major activator of the coagulation cascade. The abundance of prothrombin, which is cleaved to form thrombin during clotting, was significantly reduced and correlated with severity and might help to explain the hyper coagulative environment of SARS-CoV-2 infection. From transcriptomic analysis of leukocytes, we concluded that COVID-19 patients with acute respiratory distress syndrome (ARDS) demonstrated a phenotype that overlapped with, but was distinct from, that found in patients with non-COVID-19-ARDS. To aid in the global efforts toward elucidation of disease pathophysiology and therapeutic development, we created a web-based tool with interactive visualizations allowing for easy navigation of this systems-level compendium of biomolecule abundance in relation to COVID-19 status and severity. Finally, we leveraged these multi-omic data to predict COVID-19 patient outcomes with machine learning, which highlighted the predictive power of these expansive molecular measurements beyond the standardized clinical estimate of 10-year survival Charlson score.

Dataset Information

The cross-scale correlations between individuals and nations in COVID-19 mortality.

Publications

The cross-scale correlations between individuals and nations in COVID-19 mortality.

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