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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.
  • Eze PU; School of Computing and Information Systems, University of Melbourne, Victoria, Australia. peter.eze@unimelb.edu.au.
  • Geard N; School of Computing and Information Systems, University of Melbourne, Victoria, Australia.
  • Baker CM; School of Mathematics and Statistics, University of Melbourne, Victoria, Australia.
  • Campbell PT; Melbourne Centre for Data Science, University of Melbourne, Victoria, Australia.
  • Chades I; Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Victoria, Australia.
BMC Health Serv Res ; 23(1): 485, 2023 May 13.
Article in English | MEDLINE | ID: covidwho-2314392
ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Adult / Child / Humans Language: English Journal: BMC Health Serv Res Journal subject: Health Services Research Year: 2023 Document Type: Article Affiliation country: S12913-023-09479-4

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Observational study / Prognostic study Limits: Adult / Child / Humans Language: English Journal: BMC Health Serv Res Journal subject: Health Services Research Year: 2023 Document Type: Article Affiliation country: S12913-023-09479-4