Evidence map›Paper›PMID 34642701›Full record

ArticlemedRxiv : the preprint server for health sciences2021

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.

Bradley S Price, Maryam Khodaverdi, Adam Halasz, Brian Hendricks, Wesley Kimble, Gordon S Smith, Sally L Hodder

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2021. 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
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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Bradley S PriceManagement Information Systems Department, West Virginia University, Morgantown, West Virginia.
Maryam KhodaverdiWest Virginia Clinical and Translational Science Institute, Morgantown, West Virginia.
Adam HalaszSchool of Mathematics and Data Science, West Virginia University, Morgantown, West Virginia.
Brian HendricksDepartment of Epidemiology and Biostatistics, West Virginia University, Morgantown, West Virginia.
Wesley KimbleWest Virginia Clinical and Translational Science Institute, Morgantown, West Virginia.
Gordon S SmithDepartment of Epidemiology and Biostatistics, West Virginia University, Morgantown, West Virginia.
Sally L HodderWest Virginia Clinical and Translational Science Institute, Morgantown, West Virginia.

Funding

West Virginia IDEA-CTRU54GM104942 · NIGMS · WEST VIRGINIA UNIVERSITY · PI Sally Lynn Hodder · 2012 to 2026
$81.0M
NIGMS NIH HHS U54 GM104942
6 · The paper itself

Abstract

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CXoV-2 infections. In this study, we describe and compare two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, R

Identifiers

PMID34642701
PMCPMC8509102

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.