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    Pre-existing health conditions and severe COVID-19 MESHD infection: Analysis of commercial health insurance data from 690, 000 infected MESHD patients

    Authors: Nathan E Wineinger; Victoria Li; Jill Waalen; Eric J Topol

    doi:10.1101/2021.03.11.21252708 Date: 2021-03-12 Source: medRxiv

    The development and distribution of new vaccines promises an end to the COVID-19 pandemic MESHD. With the elderly and front line workers first in line, the vaccination strategy for the general population remains unclear. In this study we identified 690,000 patients infected with COVID-19 MESHD with commercial health insurance coverage across the country between April 1, 2020 and September 30, 2020. From prior health care claims, we determined each person's pre-existing diseases among 26 common chronic diseases MESHD. Across age-sex strata we determined the relationships between these conditions and severe COVID-19 MESHD infections: ICU admission and extended in-patient stay. We classify disease according to risk, develop multivariable models to predict infection outcomes, and created an online risk assessment tool to estimate risk of ICU admission based on pre-existing health conditions. Our results could be used to help guide risk, vaccination health policy, and personal decision-making as these become available to the general population.

    Vaccine Rollout Strategies: The Case for Vaccinating Essential Workers Early

    Authors: Nicola Mulberry; Paul F Tupper; Christopher MacCabe; Erin Kirwin; Caroline Colijn

    doi:10.1101/2021.02.23.21252309 Date: 2021-02-25 Source: medRxiv

    In planning for upcoming mass vaccinations against COVID-19 MESHD, many jurisdictions have proposed using primarily age-based rollout strategies, where the oldest are vaccinated first and the youngest last. In the wake of growing evidence that approved vaccines are effective at preventing not only adverse outcomes, but also infection (and hence transmission of SARS-CoV-2), we propose that such age-based rollouts are both less equitable and less effective than strategies that prioritize essential workers. We demonstrate using modelling that strategies that target essential workers earlier consistently outperform those that do not, and that prioritizing essential workers provides a significant level of indirect protection for older adults. This conclusion holds across numerous outcomes, including cases, hospitalizations, deaths MESHD, prevalence of Long COVID, chronic impacts of COVID, quality adjusted life years lost and net monetary benefit lost. It also holds over a range of possible values for the efficacy of vaccination against infection. Our analysis focuses on regimes where the pandemic continues to be controlled with distancing and other measures as vaccination proceeds, and where the vaccination strategy is expected to last for over the coming 6-8 months - for example British Columbia, Canada. In such a setting with a total population of 5M, vaccinating essential workers sooner is expected to prevent over 200, 000 infections MESHD, over 600 deaths, and to produce a net monetary benefit of over $500M. 20-25% of the quality adjusted life years lost, and 28-34% of the net monetary benefit lost, are due to chronic impacts of COVID-19 MESHD.

    Coronavirus( COVID-19 MESHD)Outbreak Prediction Using Epidemiological Models of Richards Gompertz Logistic Ratkowsky and SIRD

    Authors: Ahmad Sedaghat; Seyed Amir Abbas Oloomi; Mahdi Ashtian Malayer; Nima Rezaei; Amir MOSAVI

    doi:10.1101/2020.11.29.20240580 Date: 2020-11-30 Source: medRxiv

    On 30 July 2020, a total number of 301,530 diagnosed COVID-19 MESHD cases were reported in Iran, with 261,200 recovered and 16,569 dead. The COVID-19 pandemic MESHD started with 2 patients in Qom city in Iran on 20 February 2020. Accurate prediction of the end of the COVID-19 pandemic MESHD and the total number of populations affected is challenging. In this study, several widely used models, including Richards, Gompertz, Logistic, Ratkowsky, and SIRD models, are used to project dynamics of the COVID-19 pandemic MESHD in the future of Iran by fitting the present and the past clinical data. Iran is the only country facing a second wave of COVID-19 MESHD infections, which makes its data difficult to analyze. The present studys main contribution is to forecast the near-future of COVID-19 MESHD trends to allow non-pharmacological interventions (NPI) by public health authorities and/or government policymakers. We have divided the COVID-19 pandemic MESHD in Iran into two waves, Wave I, from February 20, 2020 to May 4, 2020, and Wave II from May 5, 2020, to the present. Two statistical methods, i.e., Pearson correlation coefficient (R) and the coefficient of determination (R2), are used to assess the accuracy of studied models. Results for Wave I Logistic, Ratkowsky, and SIRD models have correctly fitted COVID-19 MESHD data in Iran. SIRD model has fitted the first peak of infection very closely on April 6, 2020, with 34,447 cases (The actual peak day was April 7, 2020, with 30,387 active infected MESHD patients) with the re-production number R0=3.95. Results of Wave II indicate that the SIRD model has precisely fitted with the second peak of infection, which was on June 20, 2020, with 19,088 active infected cases compared with the actual peak day on June 21, 2020, with 17,644 cases. In Wave II, the re-production number R0=1.45 is reduced, indicating a lower transmission rate. We aimed to provide even a rough project future trends of COVID-19 MESHD in Iran for NPI decisions. Between 180,000 to 250, 000 infected MESHD cases and a death toll of between 6,000 to 65,000 cases are expected in Wave II of COVID-19 MESHD in Iran. There is currently no analytical method to project more waves of COVID-19 MESHD beyond Wave II.

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MeSH Disease
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SARS-CoV-2 Proteins


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