![]() This does not alter our adherence to PLOS ONE policies on sharing data and materials.Ĭoronavirus disease 2019 (COVID-19) mitigation and containment policies have significant economic, social, and health impact. The specific roles are articulated in the ‘author contributions’ section.Ĭompeting interests: API provided support in the form of salaries for author LP. API provided support in the form of salary for author LP but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are uploaded to GitHub ( ).įunding: The research described was supported, in part, by the National Institutes of Health /National Center for Advancing Translational Sciences through UCLA CTSI Grant Number UL1TR000124 and the UCLA CTSI TL1 Grant Number TL1TR001883. Received: AugAccepted: FebruPublished: April 30, 2021Ĭopyright: © 2021 Inkelas et al. PLoS ONE 16(4):Įditor: Arthur Wakefield Baker, Duke University, UNITED STATES (2021) Using control charts to understand community variation in COVID-19. Control charts could prove valuable given their potential ease of use and interpretability in real-time decision-making and for communication about the pandemic at a meaningful level for communities.Ĭitation: Inkelas M, Blair C, Furukawa D, Manuel VG, Malenfant JH, Martin E, et al. Policy-makers and communities require access to relevant, accurate data to respond to the evolving COVID-19 pandemic. The annotated time series presentation connects events and policies with observed data that may help mobilize and direct the actions of residents and other stakeholders. Such disaggregation provides granularity that decision-makers can use to respond to the pandemic. We found that COVID-19 rates vary by region and subregion, with periods of exponential and non-exponential growth and decline. We developed control charts at the county and city/neighborhood level within one state (California) to illustrate their potential value for decision-makers. Healthcare and other industries use statistical process control to study variation and disaggregate data for purposes of understanding behavior of processes and systems and intervening on them. Our aim was to demonstrate a novel use of statistical process control to provide timely and interpretable displays of COVID-19 data that inform local mitigation and containment strategies. Decision-makers need signals for action as the coronavirus disease 2019 (COVID-19) pandemic progresses.
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