Background In response to the spread of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and the impact of coronavirus disease 2019 (COVID‐19), governments have implemented a variety of measures to control the spread of the virus and the associated disease. Among these, have been measures to control the pandemic in primary and secondary school settings. Objectives To assess the effectiveness of measures implemented in the school setting to safely reopen schools, or keep schools open, or both, during the COVID‐19 pandemic, with particular focus on the different types of measures implemented in school settings and the outcomes used to measure their impacts on transmission‐related outcomes, healthcare utilisation outcomes, other health outcomes as well as societal, economic, and ecological outcomes. Search methods We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, and the Educational Resources Information Center, as well as COVID‐19‐specific databases, including the Cochrane COVID‐19 Study Register and the WHO COVID‐19 Global literature on coronavirus disease (indexing preprints) on 9 December 2020. We conducted backward‐citation searches with existing reviews. Selection criteria We considered experimental (i.e. randomised controlled trials; RCTs), quasi‐experimental, observational and modelling studies assessing the effects of measures implemented in the school setting to safely reopen schools, or keep schools open, or both, during the COVID‐19 pandemic. Outcome categories were (i) transmission‐related outcomes (e.g. number or proportion of cases); (ii) healthcare utilisation outcomes (e.g. number or proportion of hospitalisations); (iii) other health outcomes (e.g. physical, social and mental health); and (iv) societal, economic and ecological outcomes (e.g. costs, human resources and education). We considered studies that included any population at risk of becoming infected with SARS‐CoV‐2 and/or developing COVID‐19 disease including students, teachers, other school staff, or members of the wider community. Data collection and analysis Two review authors independently screened titles, abstracts and full texts. One review author extracted data and critically appraised each study. One additional review author validated the extracted data. To critically appraise included studies, we used the ROBINS‐I tool for quasi‐experimental and observational studies, the QUADAS‐2 tool for observational screening studies, and a bespoke tool for modelling studies. We synthesised findings narratively. Three review authors made an initial assessment of the certainty of evidence with GRADE, and several review authors discussed and agreed on the ratings. Main results We included 38 unique studies in the analysis, comprising 33 modelling studies, three observational studies, one quasi‐experimental and one experimental study with modelling components. Measures fell into four broad categories: (i) measures reducing the opportunity for contacts; (ii) measures making contacts safer; (iii) surveillance and response measures; and (iv) multicomponent measures. As comparators, we encountered the operation of schools with no measures in place, less intense measures in place, single versus multicomponent measures in place, or closure of schools. Across all intervention categories and all study designs, very low‐ to low‐certainty evidence ratings limit our confidence in the findings. Concerns with the quality of modelling studies related to potentially inappropriate assumptions about the model structure and input parameters, and an inadequate assessment of model uncertainty. Concerns with risk of bias in observational studies related to deviations from intended interventions or missing data. Across all categories, few studies reported on implementation or described how measures were implemented. Where we describe effects as 'positive', the direction of the point estimate of the effect favours the intervention(s); 'negative' effects do not favour the intervention. We found 23 modelling studies assessing measures reducing the opportunity for contacts (i.e. alternating attendance, reduced class size). Most of these studies assessed transmission and healthcare utilisation outcomes, and all of these studies showed a reduction in transmission (e.g. a reduction in the number or proportion of cases, reproduction number) and healthcare utilisation (i.e. fewer hospitalisations) and mixed or negative effects on societal, economic and ecological outcomes (i.e. fewer number of days spent in school). We identified 11 modelling studies and two observational studies assessing measures making contacts safer (i.e. mask wearing, cleaning, handwashing, ventilation). Five studies assessed the impact of combined measures to make contacts safer. They assessed transmission‐related, healthcare utilisation, other health, and societal, economic and ecological outcomes. Most of these studies showed a reduction in transmission, and a reduction in hospitalisations; however, studies showed mixed or negative effects on societal, economic and ecological outcomes (i.e. fewer number of days spent in school). We identified 13 modelling studies and one observational study assessing surveillance and response measures , including testing and isolation, and symptomatic screening and isolation. Twelve studies focused on mass testing and isolation measures, while two looked specifically at symptom‐based screening and isolation. Outcomes included transmission, healthcare utilisation, other health, and societal, economic and ecological outcomes. Most of these studies showed effects in favour of the intervention in terms of reductions in transmission and hospitalisations, however some showed mixed or negative effects on societal, economic and ecological outcomes (e.g. fewer number of days spent in school). We found three studies that reported outcomes relating to multicomponent measures , where it was not possible to disaggregate the effects of each individual intervention, including one modelling, one observational and one quasi‐experimental study. These studies employed interventions, such as physical distancing, modification of school activities, testing, and exemption of high‐risk students, using measures such as hand hygiene and mask wearing. Most of these studies showed a reduction in transmission, however some showed mixed or no effects. As the majority of studies included in the review were modelling studies, there was a lack of empirical, real‐world data, which meant that there were very little data on the actual implementation of interventions. Authors' conclusions Our review suggests that a broad range of measures implemented in the school setting can have positive impacts on the transmission of SARS‐CoV‐2, and on healthcare utilisation outcomes related to COVID‐19. The certainty of the evidence for most intervention‐outcome combinations is very low, and the true effects of these measures are likely to be substantially different from those reported here. Measures implemented in the school setting may limit the number or proportion of cases and deaths, and may delay the progression of the pandemic. However, they may also lead to negative unintended consequences, such as fewer days spent in school (beyond those intended by the intervention). Further, most studies assessed the effects of a combination of interventions, which could not be disentangled to estimate their specific effects. Studies assessing measures to reduce contacts and to make contacts safer consistently predicted positive effects on transmission and healthcare utilisation, but may reduce the number of days students spent at school. Studies assessing surveillance and response measures predicted reductions in hospitalisations and school days missed due to infection or quarantine, however, there was mixed evidence on resources needed for surveillance. Evidence on multicomponent measures was mixed, mostly due to comparators. The magnitude of effects depends on multiple factors. New studies published since the original search date might heavily influence the overall conclusions and interpretation of findings for this review. 

Measures implemented in the school setting to contain the COVID-19 pandemic

We have developed an extended mathematical graph-based spatial-temporal SIR model, allowing a multidimensional analysis of the impact of non-pharmaceutical interventions on the pandemic spread, whi...

Spatio-Temporal influence of Non-Pharmaceutical interventions policies on pandemic dynamics and the economy: the case of COVID-19

Many researchers have tried to predict the impact of the COVID-19 outbreak on morbidity, in order to help policy-makers find optimal isolation policies. However, despite the development and use of many models and sophisticated tools, these forecasting attempts have largely failed. We present a model that considers the severity of the disease and the heterogeneity of contacts between the population in complex space–time dynamics. Using mathematical and computational methods, the applied tool was developed to analyze and manage the COVID-19 pandemic (from an epidemiological point of view), with a particular focus on population heterogeneity in terms of age, susceptibility, and symptom severity. We show improved strategies to prevent an epidemic outbreak. We evaluated the model in three countries, obtaining an average mean square error of 0.067 over a full month of the basic reproduction number (R0). The goal of this study is to create a theoretical framework for crisis management that integrates accumulated epidemiological considerations. An applied result is an open-source program for predicting the outcome of an isolation strategy for future researchers and developers who can use and extend our model. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Nonlinear Sciences & Numerical Simulation is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pandemic management by a spatio–temporal mathematical model

Multi-strain pandemics have emerged as a major concern. We introduce a new model for assessing the connection between multi-strain pandemics and mortality rate, basic reproduction number, and maximum infected individuals. The proposed model provides a general mathematical approach for representing multi-strain pandemics, generalizing for an arbitrary number of strains. We show that the proposed model fits well with epidemiological historical world health data over a long time period. From a theoretical point of view, we show that the increasing number of strains increases logarithmically the maximum number of infected individuals and the mean mortality rate. Moreover, the mean basic reproduction number is statistically identical to the single, most aggressive pandemic strain for multi-strain pandemics. 

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Generic approach for mathematical model of multi-strain pandemics

In a world where pandemics are a matter of time and increasing urbanization of the world9s population, governments should be prepared with pandemic intervention policies (IPs) to minimize the crisis direct and indirect adverse effects while keeping normal life as much as possible. Successful pandemic IPs have to take into consideration the heterogeneous behavior of individuals in different types of buildings and social contexts. In this study, we propose a spatio-temporal, heterogeneous population model and in silico simulation to evaluate pandemic IPs in four types of buildings - home, office, school, and mall. We show that indeed each building type has a unique pandemic spread and therefore a different optimal IP. Moreover, we show that temporal-based IPs (such as mask wearing) have a similar influence on the pandemic spread in all four building types while spatial-based IPs (such as social distance) highly differ.

/pdf/comparison-of-pandemic-intervention-policies-in-several-1yr7afhbla.pdf

Comparison of Pandemic Intervention Policies in Several Building Types Using Heterogeneous Population Model

The new COVID-19 pandemic has challenged policymakers on key issues Most countries have adopted ?lockdown? policies to reduce the spatial spread of COVID-19, but they have damaged the economic and moral fabric of society Mathematical modeling in non-pharmaceutical intervention policy management has proven to be a major weapon in this fight due to the lack of an effective COVID-19 vaccine A new hybrid model for COVID-19 dynamics using both an age-structured mathematical model based on the SIRD model and spatio-temporal model in silico is presented, analyzing the data of COVID-19 in Israel Using the hybrid model, a method for estimating the reproduction number of an epidemic in real-time from the data of daily notification of cases is introduced The results of the proposed model are confirmed by the Israeli Lockdown experience with a mean square error of 0 205 over 2 weeks The use of mathematical models promises to reduce the uncertainty in the choice of ?Lockdown? policies The unique use of contact details from 2 classes (children and adults), the interaction of populations depending on the time of day, and several physical locations, allow a new look at the differential dynamics of the spread and control of infection

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.202000298

Teddy Lazebnik

Papers

The Signature Features of COVID-19 Pandemic in a Hybrid Mathematical Model—Implications for Optimal Work–School Lockdown Policy

Spatio-Temporal influence of Non-Pharmaceutical interventions policies on pandemic dynamics and the economy: the case of COVID-19

Pandemic management by a spatio–temporal mathematical model

Generic approach for mathematical model of multi-strain pandemics

Comparison of Pandemic Intervention Policies in Several Building Types Using Heterogeneous Population Model