Evidence map›Paper›PMID 40838608›Full record

ArticleEpidemiology (Cambridge, Mass.)2025

Unexpected Transmission Dynamics in a University Town: Lessons From COVID-19.

Erin Clancey, Matthew S Mietchen, Corrin McMichael, Eric T Lofgren

Abstract read
In one paragraph

Article in Epidemiology (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Erin ClanceyFrom the Paul G. Allen School for Global Health, Washington State University, Pullman, WA.ORCID 0000-0003-4728-4023
Matthew S MietchenCarolina Population Center, University of North Carolina, Chapel Hill, NC.
Corrin McMichaelWhitman County Public Health Department, Colfax, WA.
Eric T LofgrenFrom the Paul G. Allen School for Global Health, Washington State University, Pullman, WA.

Funding

Methods and Tools for Understanding Disease Dynamics in Small, Structured PopulationsR35GM147013 · NIGMS · WASHINGTON STATE UNIVERSITY · PI Eric T Lofgren · 2022 to 2026
$1.8M
NIGMS NIH HHS R35 GM147013
6 · The paper itself

Abstract

Institutions of higher education faced a number of challenges during the COVID-19 pandemic. Chief among them was whether or not to reopen during the second wave of COVID-19 in the fall of 2020, which was controversial because incidence in young adults was on the rise. The migration of students back to campuses worried many that transmission within student populations would spread into surrounding communities. In light of this, many colleges and universities implemented mitigation strategies, with varied degrees of success. Washington State University, located in the city of Pullman in Whitman County, WA, is an example of this type of university-community co-location, where the role of students returning to the area for the fall 2020 semester was contentious. Using COVID-19 incidence in Pullman, WA, reported to the Whitman County Health Department, we retrospectively study the transmission dynamics that occurred between the student and community subpopulations in fall 2020. We develop a two-population ordinary differential equations mechanistic model to infer transmission rates within and across the university student and community subpopulations. We use results from Bayesian parameter estimation to determine if exponential transmission of COVID-19 occurred in Pullman, WA, and the magnitude of cross-transmission from students to community members. We find these results are consistent with the estimation of the time-varying reproductive number that outbreak potential was minimal and resolved quickly, and conclude that the students returning to Washington State University-Pullman did not place the surrounding community at disproportionate risk of COVID-19 during fall 2020 when mitigation efforts were in place.

Indexed as

COVID-19StudentsAdultBayes TheoremFemaleHumansIncidenceMaleRetrospective StudiesSARS-CoV-2UniversitiesWashingtonYoung AdultBayesian inferenceCOVID-19Higher educationInfectious diseaseMathematical modelingStructured populations

Identifiers

PMID40838608
PMCPMC12459142

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.