School and community reopening during the COVID-19 pandemic: a mathematical modeling study
Bibliographic record
Abstract
Abstract Background The closure of communities, including schools, has been adopted to control the coronavirus disease 2019 (COVID-19) epidemic in most countries. Operating schools safely during the pandemic requires a balance between health risks and the need for in-person learning. We use compartmental models to explore school reopening scenarios. Methods Using demographic and epidemiological data between July 31 and November 23, 2020 from the city of Toronto, we developed a Susceptible-Exposed-Asymptomatic-Infectious-Recovered-Hospitalized-Isolated model. Our model with age, household, and community transmission allow us to study the impact of schools open in September 2020. The model mimics the transmission in households, the community, and schools, accounting for differences in infectiousness between adults and children and youth and adults’ working status. We assessed the extent to which school opening may have contributed to COVID-19 resurgence in the fall and simulated scenarios for the safe reopening of schools up to May 31, 2021. We further considered the impact of the introduction of the new variant of concern. Findings Though a slight increase in infections among adults (2.8%) and children (5.4%) is anticipated by the end of the year, safe school opening is possible with stringent nonpharmaceutical interventions (NPIs) decreasing the risk of transmission in the community and the household. We found that while school reopening was not the key driver in virus resurgence, but rather it was community spread that determined the outbreak trajectory, brief school closures did reduce infections when transmission risk within the home was low. When considered possible cross-infection amongst households, communities, and schools, we found that home transmission was crucial for mitigating the epidemic and safely operating schools. Simulating the introduction of a new strain with higher infectiousness, we observed substantial increases in infections, even when both schools and communities are closed. Interpretation Schools can open safely under strict maintenance of strict public health measures in the community. The gradual opening of schools and communities can only be achieved by maintaining NPIs and mitigating household transmission risk to avoid the broader escape of infections acquired in schools into the community via households. If the new COVID-19 strain is more infectious for children, public spaces, including schools, should be closed, and additional NPIs, including the use of masks, should be extended to toddlers. Funding This research was supported by Canadian Institutes of Health Research (CIHR), Natural Sciences and Engineering Research Council of Canada, and York University Research Chair program. Research in context Evidence before this study The design of a gradual school reopening strategy remains at the heart of decision-making on reopening after shut-downs to control the epidemic. Although available studies have assessed the risk of school reopening by modelling the transmission across schools and communities, it remains unclear whether the risk is due to increased transmission in adults or children and youth.We used GoogleScholar and PubMed searches to identify previous published works. We used te following terms: “school closure”, “covid 19 school closure”, “reopening schools”, “reopening screening school”, “school household second wave model”. The search of the studies ended in January 2021. Papers in other languages than English and letters were excluded from the search. Two modelling studies examined the effects of screening and delayed school reopening, two other agent-based modelling studies explored the epidemic spread across different age groups. Added-value of this study We find that the resurgence of COVID-19 in Toronto in fall 2020 mainly resulted from the increase of contact rate among adults in the community, and that the degree of in-person attendance had the most significant impact on transmission in schools. To our knowledge, our work is the first to investigate the resurgence in infections following school reopening and the impact of risk mitigation measures in schools operation during the pandemic. Our novel and comprehensive model considers the age and household structure, but also considers three different settings, school, household and community. We further examined the effects of self-screening procedures, class size, and schooling days on transmission, which enabled us to compare scenarios of school reopening separately for both adults and children and youth, and model the cross-infection between them to avoid potential underestimation. We found that after schools opened, reducing household transmission was crucial for mitigating the epidemic since it can reduce cross-infection amongst households, communities and schools. Lastly, given the recent report of SARS-CoV-2 variant (VOC202012/01), we investigated the impact of the new variant that may be more infectious in children and youth. Implications of all the available evidence Our analysis can inform policymakers of planning the safe reopening of schools during COVID-19. We suggest that integrating strict NPIs and school control measures are crucial for safe reopening. When schools are open, reducing transmission risk at home and community is paramount in curbing the spread of COVID-19. Lastly, if children are more susceptible to the new COVID-19 VOC, both schools and community must be closed, the time children spend in essential services locations minimized, and NPI’s for those aged less than three years enforced.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".