ArticleScientific data2024
Harmonizing government responses to the COVID-19 pandemic.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Local translation of national guidance on discontinuing COVID-19 isolation into hospital policies results in considerable differences.Antimicrobial resistance and infection control · 2025Article
- A Cloud-Based Platform for Harmonized COVID-19 Data: Design and Implementation of the Rapid Acceleration of Diagnostics (RADx) Data Hub.JMIR public health and surveillance · 2025Article
- What nudges you to take a vaccine? Understanding behavioural drivers of COVID-19 vaccinations using large-scale experiments in the G-7 countries.Health psychology and behavioral medicine · 2025Article
- A natural language processing approach to support biomedical data harmonization: Leveraging large language models.PloS one · 2025Article
- A General Primer for Data Harmonization.Scientific data · 2024Article
- A General-Purpose Data Harmonization Framework: Supporting Reproducible and Scalable Data Integration in the RADx Data Hub.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Public health and safety measures (PHSM) made in response to the COVID-19 pandemic have been singular, rapid, and profuse compared to the content, speed, and volume of normal policy-making. Not only can they have a profound effect on the spread of the disease, but they may also have multitudinous secondary effects, in both the social and natural worlds. Unfortunately, despite the best efforts by numerous research groups, existing data on COVID-19 PHSM only partially captures their full geographical scale and policy scope for any significant duration of time. This paper introduces our effort to harmonize data from the eight largest such efforts for policies made before September 21, 2021 into the taxonomy developed by the CoronaNet Research Project in order to respond to the need for comprehensive, high quality COVID-19 data. In doing so, we present a comprehensive comparative analysis of existing data from different COVID-19 PHSM datasets, introduce our novel methodology for harmonizing COVID-19 PHSM data, and provide a clear-eyed assessment of the pros and cons of our efforts.
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Registered trials
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.