Simulation-Based Estimation of SARS-CoV-2 Infections Associated With School Closures and Community-Based Nonpharmaceutical Interventions in Ontario, Canada

Naimark, David; Mishra, Sharmistha; Barrett, Kali; Khan, Yasín A.; Mac, Stephen; Ximenes, Raphael; Sander, Beate

doi:10.1001/jamanetworkopen.2021.3793

Cited by 29 publications

(30 citation statements)

References 34 publications

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“…The possibility of considering diverse kinds of interventions has been important for the evaluation of NPIs to mitigate the COVID-19 pandemic. For example, the ABM of the epidemic in Ontario developed by Naimark et al ( 2021 ) takes into account both community-based NPIs (e.g., restricting gatherings) and non-community-based NPIs (e.g., closing schools), and compares their influence in the number of infections. The main aim of the model is to study if non-community-based NPIs, which have many undesired effects, can be avoided.…”

Section: Agent-based Modelsmentioning

confidence: 99%

Epidemiological models and COVID-19: a comparative view

Iranzo

Pérez-González

2021

HPLS

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Epidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials—some real examples are invoked—we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked.

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Section: Agent-based Modelsmentioning

confidence: 99%

Epidemiological models and COVID-19: a comparative view

Iranzo

Pérez-González

2021

HPLS

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“…14 Data available through these sources include daily cases, decade of age, and deaths by county, starting from January 2020. 4,15 Per CDC guidelines, both con rmed and probable cases and deaths are included. Daily incidence was converted into a weekly incidence for cases and deaths.…”

Section: Community Incidence and Covid-19-related Deathsmentioning

confidence: 99%

“…2 Modeling studies and time series analyses from across the world differ in their assessment of the impact of reopening schools on community SARS-CoV-2 transmission. [3][4][5] Elementary-school aged children are at lower risk of severe illness than other age groups, and their role in transmission remains cloudy. 6,7 However, there are multiple close interactions between individuals from separate households in a school setting, thus interactions that occur in schools, even if each contact is lower risk, may contribute to SARS-CoV-2 spread.…”

Section: Introductionmentioning

confidence: 99%

The Impact of School Opening Model on SARS-CoV-2 Community Incidence and Mortality: A Nationwide Cohort Study

Ertem

Schechter‐Perkins

Oster

et al. 2021

Preprint

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The role that in-person schooling contributes to community incidence of SARS-CoV-2 infections and deaths remains unknown. We conducted an event study evaluating the effect of in-person school on SARS-CoV-2 cases and deaths per 100,000 persons during the 12-weeks following school opening, stratified by US Census region. There was no impact of in-person school opening and COVID-19 deaths. In most regions, COVID-19 incidence rates were not statistically different in counties with in-person versus remote school modes. However, in the South, there was a significant and sustained increase in cases per week among counties that opened for in-person learning versus remote learning, with weekly effects ranging from 7.8 (95% CI: 1.2-14.5) to 18.9 (95% CI: 7.9-29.9) additional cases per 100,000, driven by increases among 0-9 year olds and adults.

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“…However, early data suggest that there has been low vaccine acceptance among LTCF staff. A recent study reported that among the 11,460 LTCFs with at least one vaccination clinic conducted via the CDC Pharmacy Partnership for Long Term Care Program, only 37.5% of staff members received the vaccine as compared to 77.8% of residents. 5 These data are concerning because unvaccinated staff can sustain SARS CoV-2 infection within LTCFs, making infection control extremely difficult, even with higher vaccination rates, new variants (e.g., delta variant) pose a risk because they may be more resistant to the vaccine.…”

Section: Introductionmentioning

confidence: 99%

“…Agent-based models (ABMs) are a powerful tool for understanding complex dynamic processes, such as infectious disease transmission. 10 During the COVID-19 pandemic, these models have been used to assess the impact of nonpharmaceutical interventions on infections within schools 11 , a small town 12 , and France. 13 Researchers further demonstrated that delaying the second dose of mRNA vaccines for those under 65 led to more positive outcomes under certain conditions with an ABM 14 .…”

Section: Introductionmentioning

confidence: 99%

Model-based Evaluation of Continued COVID-19 Risk at Long Term Care Facilities

Fosdick¹,

Bayham

Dilliott

et al. 2021

Preprint

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The COVID-19 pandemic severely impacted long-term care facilities resulting in the death of approximately 8% of residents nationwide. As COVID-19 case rates decline and state and county restrictions are lifted, facility managers, local and state health agencies are challenged with defining their own policies moving forward to appropriately mitigate disease transmission. The continued emergence of variants of concern has highlighted the need for a readily available tool that can be employed at the facility-level to determine best practices for mitigation and ensure resident and staff safety. To assist leadership in determining the impact of various infection surveillance and response strategies, we developed an agent-based model and an online dashboard interface that simulates COVID-19 infection within congregate care settings under various mitigation measures. In this paper, we demonstrate how this dashboard can be used to quantify the continued risk for COVID-19 infections within a facility given a designated testing schedule and vaccine requirements. Our results highlight the critical nature of testing cadence, test sensitivity and specificity, and the impact of removing asymptomatic infected individuals from the workplace. We also show that monthly surveillance testing at long-term care facilities is unlikely to successfully mitigate SARS-CoV-2 outbreaks in congregate care settings.

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Simulation-Based Estimation of SARS-CoV-2 Infections Associated With School Closures and Community-Based Nonpharmaceutical Interventions in Ontario, Canada

Cited by 29 publications

References 34 publications

Epidemiological models and COVID-19: a comparative view

Epidemiological models and COVID-19: a comparative view

The Impact of School Opening Model on SARS-CoV-2 Community Incidence and Mortality: A Nationwide Cohort Study

Model-based Evaluation of Continued COVID-19 Risk at Long Term Care Facilities

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