Evidence map›Paper›PMID 38293655›Full record

ArticleMDM policy & practice

Predicting COVID-19 Outbreaks in Correctional Facilities Using Machine Learning.

Giovanni S P Malloy, Lisa B Puglisi, Kristofer B Bucklen, Tyler D Harvey, Emily A Wang, Margaret L Brandeau

Open access · goldAbstract read
In one paragraph

Article in MDM policy & practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Reinforcement Learning-Based Control of Epidemics on Networks of Communities and Correctional Facilities.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    Article
  2. Article
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

6 authors at 4 institutions in 1 country.

Giovanni S P MalloyRAND Corporation, Santa Monica, CA, USA.ORCID https://orcid.org/0000-0002-3855-4135
Lisa B PuglisiSEICHE Center for Health and Justice, Department of Internal Medicine, School of Medicine, Yale University, New Haven, CT, USA.
Kristofer B BucklenPennsylvania Department of Corrections, Mechanicsburg, PA, USA.
Tyler D HarveySEICHE Center for Health and Justice, Department of Internal Medicine, School of Medicine, Yale University, New Haven, CT, USA.
Emily A WangSEICHE Center for Health and Justice, Department of Internal Medicine, School of Medicine, Yale University, New Haven, CT, USA.
Margaret L BrandeauDepartment of Management Science and Engineering, School of Engineering, Stanford University, Stanford, CA, USA.
United States Department of Justice · USPennsylvania Department of Education · USRAND Corporation · USStanford University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Transitions Clinic Network: Post Incarceration Addiction Treatment, Healthcare, and Social Support (TCN PATHS) studyUG1DA050072 · NIDA · YALE UNIVERSITY · PI WANG, EMILY AI-HUA · 2019 to 2024
$17.6M
Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
NCATS NIH HHS UL1 TR001863NIDA NIH HHS R37 DA015612NIDA NIH HHS UG1 DA050072
6 · The paper itself

Abstract

Highlights: The risk of infectious disease transmission, including COVID-19, is disproportionately high in correctional facilities.We used machine learning methods with data collected from 24 prison facilities in the Pennsylvania Department of Corrections to determine which sources of data best predict a coming COVID-19 outbreak in a prison facility.Key predictors included county-level measures of COVID-19, facility population, and the test positivity rate in a facility.Fortifying correctional facilities with the ability to monitor local community rates of infection (e.g., though improved interagency collaboration and data sharing) along with continued testing of incarcerated people and staff can help correctional facilities better predict-and respond to-future infectious disease outbreaks.

Indexed as

corrections healthCOVID-19infectious disease predictionmachine learning

Identifiers

PMID38293655
PMCPMC10826393
OpenAlexW4391325706

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.