IMPACT OF UNIVERSITY RE-OPENING ON TOTAL COMMUNITY COVID-19 BURDEN
Bibliographic record
Abstract
ABSTRACT Purpose Post-secondary students have higher than average contacts than the general population due to congregate living, use of public transit, high-density academic and social activities, and employment in the services sector. We evaluated the impact of a large student population returning to a mid-sized city currently experiencing a low rate of COVID-19 on community health outcomes. We consider whether targeted routine or one-time screening in this population can mitigate community COVID-19 impacts. Methods We developed a dynamic transmission model of COVID-19 subdivided into three interacting populations: general population, university students, and long-term care residents. We parameterized the model using the medical literature and expert opinion. We calibrated the model to the observed outcomes in a mid-sized Canadian city between March 1 and August 15, 2020 prior to the arrival of a relatively large post-secondary student population. We evaluated the impact of the student population (20,000 people arriving on September 1) on cumulative COVID-19 infections over the fall semester, the timing of peak infections, the timing and peak level of critical care occupancy, and the timing of re-engaged social and economic restrictions. We consider multiple scenarios with different student and general population COVID-19 prevention behaviours as well as different COVID-19 screening strategies in students. Results In a city with low levels of COVID-19 activity, the return of a relatively large student population substantially increases the total number of COVID-19 infections in the community. In a scenario in which students immediately engage in a 24% contact reduction compared to pre-COVID levels, the total number of infections in the community increases by 87% (from 3,900 without the students to 7,299 infections with the students), with 71% of the incremental infections occurring in the general population, causing social and economic restrictions to be re-engaged 3 weeks earlier and an incremental 17 COVID-19 deaths. Scenarios in which students have an initial, short-term increase in contacts with other students before engaging in contact reduction behaviours can increase infections in the community by 150% or more. In such scenarios, screening asymptomatic students every 5 days reduces the number of infections attributable to the introduction of the university student population by 42% and delays the re-engagement of social and economic restrictions by 1 week. Compared to screening every 5 days, one-time mass screening of students prevents fewer infections, but is highly efficient in terms of infections prevented per screening test performed. Discussion University students are highly inter-connected with the city communities in which they live and go to school, and they have a higher number of contacts than the general population. High density living environments, enthusiasm for the new school year, and relatively high rates of asymptomatic presentation may decrease their self-protective behaviours and contribute to increased community transmission of COVID-19 affecting at-risk members of the city community. Screening targeted at this population provides significant public health benefits to the community through averted infections, critical care admissions, and COVID-19 deaths.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".