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• W3204006893 abstract "Abstract The dynamic characterization of the COVID-19 outbreak is critical to implement effective actions for its control and eradication but the information available at a global scale is not sufficiently reliable to be used directly. Here, we develop a quantitative approach to reliably quantify its temporal evolution and controllability through the integration of multiple data sources, including death records, clinical parametrization of the disease, and demographic data, and we explicitly apply it to countries worldwide, covering 97.4% of the human population, and to states within the United States (US). The validation of the approach shows that it can accurately reproduce the available prevalence data and that it can precisely infer the timing of nonpharmaceutical interventions. The results of the analysis identified general patterns of recession, stabilization, and resurgence. The diversity of dynamic behaviors of the outbreak across countries is paralleled by those of states and territories in the US, converging to remarkably similar global states in both cases. Our results offer precise insights into the dynamics of the outbreak and an efficient avenue for the estimation of the prevalence rates over time." @default.
• W3204006893 created "2021-10-11" @default.
• W3204006893 creator A5055607247 @default.
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• W3204006893 date "2021-10-07" @default.
• W3204006893 modified "2023-09-27" @default.
• W3204006893 title "Reliably quantifying the evolving worldwide dynamic state of the COVID-19 outbreak from death records, clinical parametrization, and demographic data" @default.
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• W3204006893 doi "https://doi.org/10.1038/s41598-021-99273-1" @default.
• W3204006893 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8497510" @default.
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• W3204006893 hasPublicationYear "2021" @default.
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