Evidence map›Paper›PMID 37179300›Full record

ArticleBMC health services research2023

Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19.

Peter U Eze, Nicholas Geard, Christopher M Baker, Patricia T Campbell, Iadine Chades

Abstract read
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Peter U EzeSchool of Computing and Information Systems, University of Melbourne, Victoria, Australia. peter.eze@unimelb.edu.au.
Nicholas GeardSchool of Computing and Information Systems, University of Melbourne, Victoria, Australia.
Christopher M BakerSchool of Mathematics and Statistics, University of Melbourne, Victoria, Australia.
Patricia T CampbellDepartment of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, University of Melbourne, Victoria, Australia.
Iadine ChadesCSIRO Land and Water Dutton Park, CSIRO, Brisbane, Australia.

Funding

National Health and Medical Research Council APP1170960
6 · The paper itself

Abstract

backgroundDuring 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.

methodsIn 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.

resultsWe 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.

conclusionFor 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.

Indexed as

COVID-19AdultChildHumansIntensive Care UnitsModels, TheoreticalPandemicsSARS-CoV-2COVID-19Mathematical modellingOutbreak preparednessValue of information

Identifiers

PMID37179300
PMCPMC10182758

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.