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National Institute of Environmental Health Sciences

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Your Environment. Your Health.

Publication Detail

Title: COVID-19 Spread Mapper: a multi-resolution, unified framework and open-source tool.

Authors: Shi, Andy; Gaynor, Sheila M; Dey, Rounak; Zhang, Haoyu; Quick, Corbin; Lin, Xihong

Published In Bioinformatics, (2022 Apr 28)

Abstract: SUMMARY: Amidst the continuing spread of COVID-19, real-time data analysis and visualization remain critical the general public to track the pandemic's impact and to inform policy making by officials. Multiple metrics permit the evaluation of the spread, infection, and mortality of infectious diseases. For example, numbers of new cases and deaths provide easily interpretable measures of absolute impact within a given population and time frame, while the effective reproduction rate provides an epidemiological measure of the rate of spread. By evaluating multiple metrics concurrently, users can leverage complementary insights into the impact and current state of the pandemic when formulating prevention and safety plans for oneself and others. We describe COVID-19 Spread Mapper, a unified framework for estimating and quantifying the uncertainty in the smoothed daily effective reproduction number, case rate, and death rate in a region using log-linear models. We apply this framework to characterize COVID-19 impact at multiple geographic resolutions, including by US county and state as well as by country, demonstrating the variation across resolutions and the need for harmonized efforts to control the pandemic. We provide an open-source online dashboard for real-time analysis and visualization of multiple key metrics, which are critical to evaluate the impact of COVID-19 and make informed policy decisions. AVAILABILITY AND IMPLEMENTATION: Our model and tool are publicly available as implemented in R and hosted at https://metrics.covid19-analysis.org/. The source code is freely available from https://github.com/lin-lab/COVID19-Rt and https://github.com/lin-lab/COVID19-Viz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PubMed ID: 35244140 Exiting the NIEHS site

MeSH Terms: COVID-19*/epidemiology; Humans; Pandemics/prevention & control; SARS-CoV-2; Software

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