Cargando…
Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19
BACKGROUND: During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182758/ https://www.ncbi.nlm.nih.gov/pubmed/37179300 http://dx.doi.org/10.1186/s12913-023-09479-4 |
_version_ | 1785041821088350208 |
---|---|
author | Eze, Peter U. Geard, Nicholas Baker, Christopher M. Campbell, Patricia T. Chades, Iadine |
author_facet | Eze, Peter U. Geard, Nicholas Baker, Christopher M. Campbell, Patricia T. Chades, Iadine |
author_sort | Eze, Peter U. |
collection | PubMed |
description | BACKGROUND: During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts. METHODS: In this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number ([Formula: see text] ), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios. RESULTS: We found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number [Formula: see text] . Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters. CONCLUSION: For the scenarios where the value of information was high enough to justify monitoring, if CS and [Formula: see text] are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09479-4. |
format | Online Article Text |
id | pubmed-10182758 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-101827582023-05-14 Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19 Eze, Peter U. Geard, Nicholas Baker, Christopher M. Campbell, Patricia T. Chades, Iadine BMC Health Serv Res Research BACKGROUND: During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts. METHODS: In this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number ([Formula: see text] ), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios. RESULTS: We found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number [Formula: see text] . Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters. CONCLUSION: For the scenarios where the value of information was high enough to justify monitoring, if CS and [Formula: see text] are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-023-09479-4. BioMed Central 2023-05-13 /pmc/articles/PMC10182758/ /pubmed/37179300 http://dx.doi.org/10.1186/s12913-023-09479-4 Text en © Crown 2023 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Eze, Peter U. Geard, Nicholas Baker, Christopher M. Campbell, Patricia T. Chades, Iadine Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19 |
title | Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19 |
title_full | Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19 |
title_fullStr | Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19 |
title_full_unstemmed | Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19 |
title_short | Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19 |
title_sort | value of information analysis for pandemic response: intensive care unit preparedness at the onset of covid-19 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10182758/ https://www.ncbi.nlm.nih.gov/pubmed/37179300 http://dx.doi.org/10.1186/s12913-023-09479-4 |
work_keys_str_mv | AT ezepeteru valueofinformationanalysisforpandemicresponseintensivecareunitpreparednessattheonsetofcovid19 AT geardnicholas valueofinformationanalysisforpandemicresponseintensivecareunitpreparednessattheonsetofcovid19 AT bakerchristopherm valueofinformationanalysisforpandemicresponseintensivecareunitpreparednessattheonsetofcovid19 AT campbellpatriciat valueofinformationanalysisforpandemicresponseintensivecareunitpreparednessattheonsetofcovid19 AT chadesiadine valueofinformationanalysisforpandemicresponseintensivecareunitpreparednessattheonsetofcovid19 |