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10.1371/journal.pone.0248500 http://scihub22266oqcxt.onion/10.1371/journal.pone.0248500 33930013!8087083!33930013     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (4): e0248500 Nephropedia Template TP {{tp|p=33930013|t=2021. Using control charts to understand community variation in COVID-19 |pdf=|usr=}}{{33930013}} gab.com Text Using control charts to understand community variation in COVID-19 JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33930013 #FOAvip Decision-makers need signals for action as the coronavirus disease 2019 (COVID-19) pandemic progresses. 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. 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. We developed control charts at the county and city/neighborhood level within one state (California) to illustrate their potential value for decision-makers. We found that COVID-19 rates vary by region and subregion, with periods of exponential and non-exponential growth and decline. Such disaggregation provides granularity that decision-makers can use to respond to the 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. Policy-makers and communities require access to relevant, accurate data to respond to the evolving COVID-19 pandemic. 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. Twit Text FOAVip Using control charts to understand community variation in COVID-19 ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=33930013 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33930013 #FOAvip English Wikipedia <ref>{{Cite pmid|33930013}}</ref> Using control charts to understand community variation in COVID-19 #MMPMID33930013 Inkelas M; Blair C; Furukawa D; Manuel VG; Malenfant JH; Martin E; Emeruwa I; Kuo T; Arangua L; Robles B; Provost LP PLoS One 2021[]; 16 (4): e0248500 PMID33930013show ga Decision-makers need signals for action as the coronavirus disease 2019 (COVID-19) pandemic progresses. 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. 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. We developed control charts at the county and city/neighborhood level within one state (California) to illustrate their potential value for decision-makers. We found that COVID-19 rates vary by region and subregion, with periods of exponential and non-exponential growth and decline. Such disaggregation provides granularity that decision-makers can use to respond to the 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. Policy-makers and communities require access to relevant, accurate data to respond to the evolving COVID-19 pandemic. 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. |COVID-19/diagnosis/*epidemiology[MESH] |California/epidemiology[MESH] |Cities/epidemiology[MESH] |Humans[MESH] |Models, Statistical[MESH] |Residence Characteristics[MESH]Using control charts to understand community variation in COVID-19 (epmc).pdf DeepDyve Pubget Overpricing