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      Estimation of Excess Deaths Associated With the COVID-19 Pandemic in the United States, March to May 2020

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          Abstract

          <p class="first" id="d17629219e204">Efforts to track the severity and public health impact of coronavirus disease 2019 (COVID-19) in the United States have been hampered by state-level differences in diagnostic test availability, differing strategies for prioritization of individuals for testing, and delays between testing and reporting. Evaluating unexplained increases in deaths due to all causes or attributed to nonspecific outcomes, such as pneumonia and influenza, can provide a more complete picture of the burden of COVID-19. </p>

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          Most cited references10

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          Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2)

          Estimation of the prevalence and contagiousness of undocumented novel coronavirus (SARS-CoV2) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV2, including the fraction of undocumented infections and their contagiousness. We estimate 86% of all infections were undocumented (95% CI: [82%–90%]) prior to 23 January 2020 travel restrictions. Per person, the transmission rate of undocumented infections was 55% of documented infections ([46%–62%]), yet, due to their greater numbers, undocumented infections were the infection source for 79% of documented cases. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of this virus will be particularly challenging.
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            Estimation of potential global pandemic influenza mortality on the basis of vital registry data from the 1918-20 pandemic: a quantitative analysis.

            The threat of an avian influenza pandemic is causing widespread public concern and health policy response, especially in high-income countries. Our aim was to use high-quality vital registration data gathered during the 1918-20 pandemic to estimate global mortality should such a pandemic occur today. We identified all countries with high-quality vital registration data for the 1918-20 pandemic and used these data to calculate excess mortality. We developed ordinary least squares regression models that related excess mortality to per-head income and absolute latitude and used these models to estimate mortality had there been an influenza pandemic in 2004. Excess mortality data show that, even in 1918-20, population mortality varied over 30-fold across countries. Per-head income explained a large fraction of this variation in mortality. Extrapolation of 1918-20 mortality rates to the worldwide population of 2004 indicates that an estimated 62 million people (10th-90th percentile range 51 million-81 million) would be killed by a similar influenza pandemic; 96% (95% CI 95-98) of these deaths would occur in the developing world. If this mortality were concentrated in a single year, it would increase global mortality by 114%. This analysis of the empirical record of the 1918-20 pandemic provides a plausible upper bound on pandemic mortality. Most deaths will occur in poor countries--ie, in societies whose scarce health resources are already stretched by existing health priorities.
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              Methods for current statistical analysis of excess pneumonia-influenza deaths.

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                Author and article information

                Journal
                JAMA Internal Medicine
                JAMA Intern Med
                American Medical Association (AMA)
                2168-6106
                July 01 2020
                Affiliations
                [1 ]Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut
                [2 ]Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, Maryland
                [3 ]Department of Biostatistics and the Public Health Modeling Unit, Yale School of Public Health, New Haven, Connecticut
                [4 ]Departments of Ecology and Evolutionary Biology, Statistics and Data Science, Yale School of Management, New Haven, Connecticut
                [5 ]Aledade Inc, Bethesda, Maryland
                [6 ]Department of Health and Mental Hygiene, New York, New York
                [7 ]Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst
                [8 ]Department of Science and Environment, Roskilde University, Fredeiksberg, Denmark
                Article
                10.1001/jamainternmed.2020.3391
                7330834
                32609310
                af8cd2a4-999c-40c0-856f-8cfce7b81c44
                © 2020
                History

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