Model-based projections for COVID-19 outbreak size and student-days lost to closure in Ontario childcare centres and primary schools

Mathematical model-based analysis has proven its potential as a critical tool in the battle against COVID-19 by enabling better understanding of the disease transmission dynamics, deeper analysis of the cost-effectiveness of various scenarios, and more accurate forecast of the trends with and without interventions. However, due to the outpouring of information and disparity between reported mathematical models, there exists a need for a more concise and unified discussion pertaining to the mathematical modeling of COVID-19 to overcome related skepticism. Towards this goal, this paper presents a review of mathematical model-based scenario analysis and interventions for COVID-19 with the main objectives of (1) including a brief overview of the existing reviews on mathematical models, (2) providing an integrated framework to unify models, (3) investigating various mitigation strategies and model parameters that reflect the effect of interventions, (4) discussing different mathematical models used to conduct scenario-based analysis, and (5) surveying active control methods used to combat COVID-19.

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Section: Scenariossupporting

confidence: 90%

“…Similar results are reported in [58] , [62] , [100] . In [100] , the outbreak size and student-days lost due to school closure in Ontario childcare centers and primary schools are studied. Based on the study, it is proposed that for childcare and primary school, reopening safety can be enhanced by switching to lower student ratios.…”

Section: Scenariossupporting

confidence: 90%

A review of mathematical model-based scenario analysis and interventions for COVID-19

Padmanabhan¹,

Abed²,

Meskin³

et al. 2021

Computer Methods and Programs in Biomedicine

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“…We have not extended our simulations beyond the classroom (or high school classrooms) to simulate how each cluster may spread outwards via siblings, parents, teachers and their contacts, other household interactions, friendship groups and the broader community. These factors are complex and other models have explored them [47][48][49][50][51], some also finding that extensive testing or successful test and trace systems are required to PLOS COMPUTATIONAL BIOLOGY Fig 6 . Cluster sizes are greatly reduced with regular universal (e.g. pooled) testing, particularly when that testing is performed on site (in the model, in 2 hours, compared to an assumed 2-day time to result for tests processed off site).…”

Section: Discussionmentioning

confidence: 99%

COVID-19 in schools: Mitigating classroom clusters in the context of variable transmission

Tupper

Colijn

2021

PLoS Comput Biol

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Widespread school closures occurred during the COVID-19 pandemic. Because closures are costly and damaging, many jurisdictions have since reopened schools with control measures in place. Early evidence indicated that schools were low risk and children were unlikely to be very infectious, but it is becoming clear that children and youth can acquire and transmit COVID-19 in school settings and that transmission clusters and outbreaks can be large. We describe the contrasting literature on school transmission, and argue that the apparent discrepancy can be reconciled by heterogeneity, or “overdispersion” in transmission, with many exposures yielding little to no risk of onward transmission, but some unfortunate exposures causing sizeable onward transmission. In addition, respiratory viral loads are as high in children and youth as in adults, pre- and asymptomatic transmission occur, and the possibility of aerosol transmission has been established. We use a stochastic individual-based model to find the implications of these combined observations for cluster sizes and control measures. We consider both individual and environment/activity contributions to the transmission rate, as both are known to contribute to variability in transmission. We find that even small heterogeneities in these contributions result in highly variable transmission cluster sizes in the classroom setting, with clusters ranging from 1 to 20 individuals in a class of 25. None of the mitigation protocols we modeled, initiated by a positive test in a symptomatic individual, are able to prevent large transmission clusters unless the transmission rate is low (in which case large clusters do not occur in any case). Among the measures we modeled, only rapid universal monitoring (for example by regular, onsite, pooled testing) accomplished this prevention. We suggest approaches and the rationale for mitigating these larger clusters, even if they are expected to be rare.

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“…The government actively promoted hand sanitization, wearing surgical masks, social distancing, school holidays, factory shutdowns, home isolation, and reduced social activities. We conclude that these measures decreased ADV and influenza virus infections after February 2019 [ 23 – 27 ]. Reduced industrial production and improved environmental quality also play important roles in decreasing the rate of respiratory virus infections [ 28 , 29 ].…”

Section: Discussionmentioning

confidence: 99%

Exploring the epidemiological changes of common respiratory viruses since the COVID-19 pandemic: a hospital study in Hangzhou, China

Zhu

et al. 2021

Arch Virol

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Adenovirus, respiratory syncytial virus, and influenza virus are common causes of respiratory infections. The COVID-19 pandemic had a significant impact on their prevalence. The aim of this study was to analyze the epidemic changes of common respiratory viruses in the Affiliated Hospital of Hangzhou Normal University in Hangzhou, China, from October of 2017 to February of 2021. We collected statistics from 121,529 patients in the outpatient and inpatient departments of the hospital who had throat or nose swabs collected for testing for four virus antigens by the colloidal gold method. Of these, 13,200 (10.86%) were positive for influenza A virus, 8,402 (6.91%) were positive for influenza B virus, 6,056 (4.98%) were positive for adenovirus, and 4,739 (3.90%) were positive for respiratory syncytial virus. The positivity rates of the influenza A virus (0-14 years old, P = 0.376; over 14 years old, P = 0.197) and respiratory syncytial virus (0-14 years old, P = 0.763; over 14 years old, P = 0.465) did not differ significantly by gender. After January of 2020, influenza virus infection decreased significantly. The positivity rate of respiratory syncytial virus remained high, and its epidemic season was similar to before. Strict respiratory protection and regulation of crowd activities have a great impact on the epidemic characteristics of viruses. After major changes in the public health environment, virus epidemics and their mutations should be monitored closely, extensively, and continuously.

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Model-based projections for COVID-19 outbreak size and student-days lost to closure in Ontario childcare centres and primary schools

Cited by 30 publications

References 37 publications

A review of mathematical model-based scenario analysis and interventions for COVID-19

A review of mathematical model-based scenario analysis and interventions for COVID-19

COVID-19 in schools: Mitigating classroom clusters in the context of variable transmission

Exploring the epidemiological changes of common respiratory viruses since the COVID-19 pandemic: a hospital study in Hangzhou, China

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