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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Published on: February 25, 2013

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

Erin Clancey¹, Matthew S Mietchen², Corrin McMichael³

¹From the Paul G. Allen School for Global Health, Washington State University, Pullman, WA.

Epidemiology (Cambridge, Mass.)

|August 21, 2025

Summary

Returning students at Washington State University did not increase COVID-19 risk in the surrounding community during fall 2020. Mitigation strategies were effective in preventing widespread transmission among the university and local populations.

Keywords:

Bayesian inference COVID-19 Higher education Infectious disease Mathematical modeling Structured populations

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Area of Science:

Epidemiology
Mathematical Modeling
Public Health

Background:

Colleges faced challenges reopening during the COVID-19 pandemic's second wave in fall 2020.
Student return to campuses raised concerns about community transmission, particularly in university-towns like Pullman, WA.

Purpose of the Study:

To retrospectively analyze COVID-19 transmission dynamics between students and the Pullman community in fall 2020.
To quantify cross-transmission rates from university students to the general community.

Main Methods:

Developed a two-population ordinary differential equations mechanistic model.
Utilized Bayesian parameter estimation to infer transmission rates.
Analyzed COVID-19 incidence data reported to the Whitman County Health Department.

Main Results:

COVID-19 transmission in Pullman, WA, was not exponential and resolved quickly.
The time-varying reproductive number indicated minimal outbreak potential.
Cross-transmission from students to community members was limited.

Conclusions:

Students returning to Washington State University did not disproportionately risk the surrounding community.
Implemented mitigation efforts were effective in controlling COVID-19 spread.