Evidence map›Paper›PMID 36433715›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2023

Evaluation of the US Definitions for Coronavirus Disease 2019 Community Risk Levels.

Sina Mokhtar, Alyssa S Parpia, Nathan C Lo, Martial L Ndeffo-Mbah

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 88% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Sina MokhtarDepartment of Statistics, Texas A&M University, College Station, Texas, USA.
Alyssa S ParpiaInstitute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Nathan C LoDivision of HIV, Infectious Diseases, and Global Medicine, University of California, San Francisco, San Francisco, California, USA.
Martial L Ndeffo-MbahDepartment of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, USA.
Texas A&M University · USUniversity of California, San Francisco · USUniversity of Toronto · CA

Funding

Real-time predictive modeling for public health departments to control infectious diseasesDP2AI170485 · NIAID · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Nathan Lo · 2022 to 2026
$2.3M
NIAID NIH HHS DP2 AI170485NIAID NIH HHS L30 AI171975
6 · The paper itself

Abstract

The US Centers for Disease Control and Prevention (CDC) defines a county metric of coronavirus disease 2019 (COVID-19) community levels to inform public health measures. We find that the COVID-19 community levels vary frequently over time, which may not be optimal for decision making. Alternative metric formulations that do not compromise predictive ability are shown to reduce variability.

Indexed as

COVID-19Centers for Disease Control and Prevention, U.S.HumansPublic HealthSARS-CoV-2United Statescommunity risk levelconsistencyCOVID-19

Identifiers

PMID36433715
PMCPMC10110267
OpenAlexW4310010621

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.