tetano
Editor, Senior Moderator
PLoS One
. 2021 Apr 30;16(4):e0248500.
doi: 10.1371/journal.pone.0248500. eCollection 2021.
Using control charts to understand community variation in COVID-19
Moira Inkelas[SUP] 1 2 [/SUP], Cher?e Blair[SUP] 3 [/SUP], Daisuke Furukawa[SUP] 3 [/SUP], Vladimir G Manuel[SUP] 2 4 [/SUP], Jason H Malenfant[SUP] 3 [/SUP], Emily Martin[SUP] 5 [/SUP], Iheanacho Emeruwa[SUP] 2 6 [/SUP], Tony Kuo[SUP] 2 4 7 8 [/SUP], Lisa Arangua[SUP] 8 [/SUP], Brenda Robles[SUP] 8 [/SUP], Lloyd P Provost[SUP] 9 [/SUP]
Affiliations
Abstract
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.
. 2021 Apr 30;16(4):e0248500.
doi: 10.1371/journal.pone.0248500. eCollection 2021.
Using control charts to understand community variation in COVID-19
Moira Inkelas[SUP] 1 2 [/SUP], Cher?e Blair[SUP] 3 [/SUP], Daisuke Furukawa[SUP] 3 [/SUP], Vladimir G Manuel[SUP] 2 4 [/SUP], Jason H Malenfant[SUP] 3 [/SUP], Emily Martin[SUP] 5 [/SUP], Iheanacho Emeruwa[SUP] 2 6 [/SUP], Tony Kuo[SUP] 2 4 7 8 [/SUP], Lisa Arangua[SUP] 8 [/SUP], Brenda Robles[SUP] 8 [/SUP], Lloyd P Provost[SUP] 9 [/SUP]
Affiliations
- PMID: 33930013
- DOI: 10.1371/journal.pone.0248500
Abstract
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.