Better public decisions on COVID-19: A thought experiment in metrics
Objectives: Poor decision-making is a hallmark of the COVID-19 pandemic. Better metrics would help improve decision-makers' understanding of the scope of the pandemic and allow for better public understanding/review of these decisions. Study design: Two novel metrics of disease impact were comp...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Book |
| Published: |
Elsevier,
2021-11-01T00:00:00Z.
|
| Subjects: | |
| Online Access: | Connect to this object online. |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Objectives: Poor decision-making is a hallmark of the COVID-19 pandemic. Better metrics would help improve decision-makers' understanding of the scope of the pandemic and allow for better public understanding/review of these decisions. Study design: Two novel metrics of disease impact were compared with more commonly used standard metrics. Methods: A multi-criteria decision analysis technique, used previously to support metric selection in solid waste planning, was adapted to compare number of deaths, hospitalisations, positive test results and positivity rates (standard COVID-19 impact metrics) with a simple model that estimates the total number of potentially infectious people in an area and an associated odds ratio for infectious people. Results: The odds ratio and total infectious population estimate metrics scored better in a comparison analysis than number of deaths, hospitalisations, positive test results and positivity rates (in that order). Conclusions: The novel metrics provide a more effective means of communication than other more common measures of the outbreak. These superior metrics should support decision-making processes and result in a more informed population. |
|---|---|
| Item Description: | 2666-5352 10.1016/j.puhip.2021.100208 |