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Risk scorecard to minimize impact of COVID-19 when reopening
BACKGROUND: We present a novel approach for exiting coronavirus disease 2019 (COVID-19) lockdowns using a ‘risk scorecard’ to prioritize activities to resume whilst allowing safe reopening. METHODS: We modelled cases generated in the community/week, incorporating parameters for social distancing, co...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8420627/ https://www.ncbi.nlm.nih.gov/pubmed/34318330 http://dx.doi.org/10.1093/jtm/taab113 |
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author | Lim, Shin B Pung, Rachael Tan, Kellie Lang, Jocelyn H S Yong, Dominique Z X Teh, Shi-Hua Quah, Elizabeth Sun, Yinxiaohe Ma, Stefan Lee, Vernon J M |
author_facet | Lim, Shin B Pung, Rachael Tan, Kellie Lang, Jocelyn H S Yong, Dominique Z X Teh, Shi-Hua Quah, Elizabeth Sun, Yinxiaohe Ma, Stefan Lee, Vernon J M |
author_sort | Lim, Shin B |
collection | PubMed |
description | BACKGROUND: We present a novel approach for exiting coronavirus disease 2019 (COVID-19) lockdowns using a ‘risk scorecard’ to prioritize activities to resume whilst allowing safe reopening. METHODS: We modelled cases generated in the community/week, incorporating parameters for social distancing, contact tracing and imported cases. We set thresholds for cases and analysed the effect of varying parameters. An online tool to facilitate country-specific use including the modification of parameters (https://sshsphdemos.shinyapps.io/covid_riskbudget/) enables visualization of effects of parameter changes and trade-offs. Local outbreak investigation data from Singapore illustrate this. RESULTS: Setting a threshold of 0.9 mean number of secondary cases arising from a case to keep R < 1, we showed that opening all activities excluding high-risk ones (e.g. nightclubs) allows cases to remain within threshold; while opening high-risk activities would exceed the threshold and result in escalating cases. An 80% reduction in imported cases per week (141 to 29) reduced steady-state cases by 30% (295 to 205). One-off surges in cases (due to superspreading) had no effect on the steady state if the R remains <1. Increasing the effectiveness of contact tracing (probability of a community case being isolated when infectious) by 33% (0.6 to 0.8) reduced cases by 22% (295 to 231). Cases grew exponentially if the product of the mean number of secondary cases arising from a case and (1—probability of case being isolated) was >1. CONCLUSIONS: Countries can utilize a ‘risk scorecard’ to balance relaxations for travel and domestic activity depending on factors that reduce disease impact, including hospital/ICU capacity, contact tracing, quarantine and vaccination. The tool enabled visualization of the combinations of imported cases and activity levels on the case numbers and the trade-offs required. For vaccination, a reduction factor should be applied both for likelihood of an infected case being present and a close contact getting infected. |
format | Online Article Text |
id | pubmed-8420627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84206272021-09-10 Risk scorecard to minimize impact of COVID-19 when reopening Lim, Shin B Pung, Rachael Tan, Kellie Lang, Jocelyn H S Yong, Dominique Z X Teh, Shi-Hua Quah, Elizabeth Sun, Yinxiaohe Ma, Stefan Lee, Vernon J M J Travel Med Original Article BACKGROUND: We present a novel approach for exiting coronavirus disease 2019 (COVID-19) lockdowns using a ‘risk scorecard’ to prioritize activities to resume whilst allowing safe reopening. METHODS: We modelled cases generated in the community/week, incorporating parameters for social distancing, contact tracing and imported cases. We set thresholds for cases and analysed the effect of varying parameters. An online tool to facilitate country-specific use including the modification of parameters (https://sshsphdemos.shinyapps.io/covid_riskbudget/) enables visualization of effects of parameter changes and trade-offs. Local outbreak investigation data from Singapore illustrate this. RESULTS: Setting a threshold of 0.9 mean number of secondary cases arising from a case to keep R < 1, we showed that opening all activities excluding high-risk ones (e.g. nightclubs) allows cases to remain within threshold; while opening high-risk activities would exceed the threshold and result in escalating cases. An 80% reduction in imported cases per week (141 to 29) reduced steady-state cases by 30% (295 to 205). One-off surges in cases (due to superspreading) had no effect on the steady state if the R remains <1. Increasing the effectiveness of contact tracing (probability of a community case being isolated when infectious) by 33% (0.6 to 0.8) reduced cases by 22% (295 to 231). Cases grew exponentially if the product of the mean number of secondary cases arising from a case and (1—probability of case being isolated) was >1. CONCLUSIONS: Countries can utilize a ‘risk scorecard’ to balance relaxations for travel and domestic activity depending on factors that reduce disease impact, including hospital/ICU capacity, contact tracing, quarantine and vaccination. The tool enabled visualization of the combinations of imported cases and activity levels on the case numbers and the trade-offs required. For vaccination, a reduction factor should be applied both for likelihood of an infected case being present and a close contact getting infected. Oxford University Press 2021-07-23 /pmc/articles/PMC8420627/ /pubmed/34318330 http://dx.doi.org/10.1093/jtm/taab113 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of International Society of Travel Medicine. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Lim, Shin B Pung, Rachael Tan, Kellie Lang, Jocelyn H S Yong, Dominique Z X Teh, Shi-Hua Quah, Elizabeth Sun, Yinxiaohe Ma, Stefan Lee, Vernon J M Risk scorecard to minimize impact of COVID-19 when reopening |
title | Risk scorecard to minimize impact of COVID-19 when reopening |
title_full | Risk scorecard to minimize impact of COVID-19 when reopening |
title_fullStr | Risk scorecard to minimize impact of COVID-19 when reopening |
title_full_unstemmed | Risk scorecard to minimize impact of COVID-19 when reopening |
title_short | Risk scorecard to minimize impact of COVID-19 when reopening |
title_sort | risk scorecard to minimize impact of covid-19 when reopening |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8420627/ https://www.ncbi.nlm.nih.gov/pubmed/34318330 http://dx.doi.org/10.1093/jtm/taab113 |
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