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The Ockham’s razor for estimating the needs of ICU beds during a pandemic
BACKGROUND: It is possible to monitor an epidemic evolution by plotting the number of patients, or its log-transform, as a function of time. However, these representations do not allow quick identifications of significant changes in the outbreak; a key information for estimating the needs for hospit...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer International Publishing
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197605/ https://www.ncbi.nlm.nih.gov/pubmed/34120272 http://dx.doi.org/10.1186/s13613-021-00874-w |
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author | Squara, Pierre |
author_facet | Squara, Pierre |
author_sort | Squara, Pierre |
collection | PubMed |
description | BACKGROUND: It is possible to monitor an epidemic evolution by plotting the number of patients, or its log-transform, as a function of time. However, these representations do not allow quick identifications of significant changes in the outbreak; a key information for estimating the needs for hospital and ICU beds, for decision-making, and resource allocation. Moreover, an epidemic is characterised by a heterogeneous evolution that depends on many unpredictable factors, coming from the virus itself or from its ecosystem. Simulations are very complex and based on hypotheses that are impossible to certify a priori, since each outbreak is different and has specific characteristics. A validation phase is necessary that may delay the usefulness of these tools. We tested a simpler method for monitoring the epidemic and rapidly predicting the peak. RESULTS: We present here a simple and easy-to-draw figure by plotting the daily rate of change in the number of patients as a function of time. This allows: (1) to rapidly identify the changes in the infection growth, (2) to extrapolate the regression lines for predicting the peaks, and (3) to use simple statistical models for identifying the significant inflexions and deriving the uncertainties. This figure predicted confidently the peak epidemic of the three waves in France. CONCLUSION: Plotting the daily rate of change in the number of patients as a function of time is a simple tool for monitoring an epidemic growth, allowing to quickly identify significant changes and to help in predicting the peak of the infection, with its confidence interval. |
format | Online Article Text |
id | pubmed-8197605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-81976052021-06-15 The Ockham’s razor for estimating the needs of ICU beds during a pandemic Squara, Pierre Ann Intensive Care Research BACKGROUND: It is possible to monitor an epidemic evolution by plotting the number of patients, or its log-transform, as a function of time. However, these representations do not allow quick identifications of significant changes in the outbreak; a key information for estimating the needs for hospital and ICU beds, for decision-making, and resource allocation. Moreover, an epidemic is characterised by a heterogeneous evolution that depends on many unpredictable factors, coming from the virus itself or from its ecosystem. Simulations are very complex and based on hypotheses that are impossible to certify a priori, since each outbreak is different and has specific characteristics. A validation phase is necessary that may delay the usefulness of these tools. We tested a simpler method for monitoring the epidemic and rapidly predicting the peak. RESULTS: We present here a simple and easy-to-draw figure by plotting the daily rate of change in the number of patients as a function of time. This allows: (1) to rapidly identify the changes in the infection growth, (2) to extrapolate the regression lines for predicting the peaks, and (3) to use simple statistical models for identifying the significant inflexions and deriving the uncertainties. This figure predicted confidently the peak epidemic of the three waves in France. CONCLUSION: Plotting the daily rate of change in the number of patients as a function of time is a simple tool for monitoring an epidemic growth, allowing to quickly identify significant changes and to help in predicting the peak of the infection, with its confidence interval. Springer International Publishing 2021-06-12 /pmc/articles/PMC8197605/ /pubmed/34120272 http://dx.doi.org/10.1186/s13613-021-00874-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Squara, Pierre The Ockham’s razor for estimating the needs of ICU beds during a pandemic |
title | The Ockham’s razor for estimating the needs of ICU beds during a pandemic |
title_full | The Ockham’s razor for estimating the needs of ICU beds during a pandemic |
title_fullStr | The Ockham’s razor for estimating the needs of ICU beds during a pandemic |
title_full_unstemmed | The Ockham’s razor for estimating the needs of ICU beds during a pandemic |
title_short | The Ockham’s razor for estimating the needs of ICU beds during a pandemic |
title_sort | ockham’s razor for estimating the needs of icu beds during a pandemic |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197605/ https://www.ncbi.nlm.nih.gov/pubmed/34120272 http://dx.doi.org/10.1186/s13613-021-00874-w |
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