School Opening Delay Effect on Transmission Dynamics of Coronavirus Disease 2019 in Korea: Based on Mathematical Modeling and Simulation Study

Class-specific school closures for seasonal influenza: Optimizing timing and duration to prevent disease spread and minimize educational losses

School closures are a safe and important strategy for preventing infectious diseases in schools. However, the effects of school closures have not been fully demonstrated, and prolonged school closures have a negative impact on students and communities. This study evaluated class-specific school closure strategies to prevent the spread of seasonal influenza and determine the optimal timing and duration. We constructed a new model to describe the incidence of influenza in each class based on a stochastic susceptible-exposed-infected-removed model. We collected data on the number of infected absentees and class-specific school closures due to influenza from four high schools and the number of infected cases from the community in a Japanese city over three seasons (2016-2017, 2017-2018, and 2018-2019). The parameters included in the model were estimated using epidemic data. We evaluated the effects of class-specific school closures by measuring the reduced cumulative incidence of class closures per day. The greatest reduction in the cumulative absences per day was observed in the four-day class closure. When class-specific school closures lasted for four days, the reduction in the cumulative number of infections per class closure day was greater when the closure was timed earlier. The highest reduction in the number of class closures per person-day occurred when the threshold was around 5.0%. Large variations in the reduction of cumulative incidence were noted owing to stochastic factors. Reactive, class-specific school closures for seasonal influenza were most efficient when the percentage of newly infected students exceeded around 5.0%, with a closure duration of four days. The optimal strategy of class-specific school closure provides good long-term performance but may be affected by random variations.

National trends in adolescents' mental health by income level in South Korea, pre- and post-COVID-19, 2006-2022

Despite the significant impact of the COVID-19 pandemic on various factors related to adolescent mental health problems such as stress, sadness, suicidal ideation, and suicide attempts, research on this topic has been insufficient to date. This study is based on the Korean Youth Risk Behavior Web-based Survey from 2006 to 2022. We analyzed the mental health problems of adolescents based on questionnaires with medical interviews, within five income groups and compared them with several risk factors. A total of 1,138,804 participants were included in this study, with a mean age (SD) of 15.01 (0.75) years. Of these, 587,256 were male (51.57%). In 2022, the recent period from the study, the weighted prevalence of stress in highest income group was 40.07% (95% CI, 38.67-41.48), sadness was 28.15% (26.82-29.48), suicidal ideation was 13.92% (12.87-14.97), and suicide attempts was 3.42% (2.90-3.93) while the weighted prevalence of stress in lowest income group was 62.77% (59.42-66.13), sadness was 46.83% (43.32-50.34), suicidal ideation was 31.70% (28.44-34.96), and suicide attempts was 10.45% (8.46-12.45). Lower income groups showed a higher proportion with several risk factors. Overall proportion had decreased until the onset of the pandemic. However, a significant increase has been found during the COVID-19 pandemic. Our study showed an association between household income level and the prevalence of mental illness in adolescents. Furthermore, the COVID-19 pandemic has exacerbated mental illness among adolescents from low household income level, underscoring the necessity for heightened public attention and measures targeted at this demographic.

Association between the COVID-19 pandemic and childhood development aged 30 to 36 months in South Korea, based on the National health screening program for infants and children database

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact on the neurodevelopment of children. However, the precise effects of the virus and the social consequences of the pandemic on pediatric neurodevelopment are not yet fully understood. We aimed to compare the neurodevelopment of children between before and during the COVID-19 pandemic, as well as examine the impact of socioeconomic status (SES) and regional differences on the development.

METHODS

The study used the Korean Developmental Screening Test to compare the difference in the risk of neurodevelopmental delay between before and during the COVID-19 pandemic. Multivariable logistic regression analysis was conducted to identify the relationship between experiencing the COVID-19 pandemic and the risk of neurodevelopmental delay. Stratified analyses were performed to determine whether the developmental delays caused by the pandemic's impact varied depending on SES or regional inequality.

RESULTS

This study found an association between the experience of COVID-19 and a higher risk of neurodevelopmental delay in communication (adjusted OR [aOR]: 1.21, 95% confidence interval [CI]: 1.19, 1.22; P-value: < 0.0001) and social interaction (aOR: 1.15, 95% CI: 1.13, 1.17; P-value: < 0.0001) domains among children of 30-36 months' ages. Notably, the observed association in the Medicaid group of children indicates a higher risk of neurodevelopmental delay compared to those in the non-Medicaid group.

CONCLUSIONS

These findings highlight the need to be concerned about the neurodevelopment of children who experienced the COVID-19 pandemic. The study also calls for increased training and support for Medicaid children, parents, teachers, and healthcare practitioners. Additionally, policy programs focused on groups vulnerable to developmental delays are required.

The COVID-19 pandemic in various restriction policy scenarios based on the dynamic social contact rate

The social contact rate has influenced the transmission of COVID-19, with more social contact resulting in more contagion cases. We chose 18 countries with the most confirmed cases in the first 200 days after the Wuhan lockdown. This was the first study using the dynamic social contact rate to simulate the epidemic under diverse restriction policies over 500 days since the COVID-19 outbreak. The developed General Dynamic Model suggested that the probability of contagion ranged from 12.52% to 39.39% in the epidemic. The geometric mean of the social contact rates differed from 18.21% to 96.00% between countries. The restriction policies in developed economies were 3.5 times more efficient than in developing economies. We compare the effectiveness of different policies for disease prevention and discuss the influence of policy adjustment frequency for each country. Maintaining the tightest restriction or alternate tightening and loosening restrictions was recommended, with each having an average 72.45% and 79.78% reduction in maximum active cases, respectively.

Mathematical Modeling of COVID-19 Transmission and Intervention in South Korea: A Review of Literature

South Korea implemented interventions to curb the spread of the novel coronavirus disease 2019 (COVID-19) pandemic with discovery of the first case in early 2020. Mathematical modeling designed to reflect the dynamics of disease transmission has been shown to be an important tool for responding to COVID-19. This study aimed to review publications on the structure, method, and role of mathematical models focusing on COVID-19 transmission dynamics in Korea. In total, 42 papers published between August 7, 2020 and August 21, 2022 were studied and reviewed. This study highlights the construction and utilization of mathematical models to help craft strategies for predicting the course of an epidemic and evaluating the effectiveness of control strategies. Despite the limitations caused by a lack of available epidemiological and surveillance data, modeling studies could contribute to providing scientific evidence for policymaking by simulating various scenarios.

In-person school reopening and the spread of SARS-CoV-2 during the second wave in Spain

We investigate the effects of school reopening on the evolution of COVID-19 infections during the second wave in Spain studying both regional and age-group variation within an interrupted time-series design. Spain's 17 Autonomous Communities reopened schools at different moments in time during September 2020. We find that in-person school reopening correlates with a burst in infections in almost all those regions. Data from Spanish regions gives a further leverage: in some cases, pre-secondary and secondary education started at different dates. The analysis of those cases does not allow to conclude whether reopening one educational stage had an overall stronger impact than the other. To provide a plausible mechanism connecting school reopening with the burst in contagion, we study the Catalan case in more detail, scrutinizing the interrupted time-series patterns of infections among age-groups and the possible connections between them. The stark and sudden increase in contagion among older children (10-19) just after in-person school reopening appears to drag the evolution of other age-groups according to Granger causality. This might be taken as an indirect indication of household transmission from offspring to parents with important societal implications for the aggregate dynamics of infections.

School Virus Infection Simulator for customizing school schedules during COVID-19

Even as the COVID-19 pandemic raged worldwide, schools strived to provide consistent education to their students. In such situations, schools require customized schedules that can address the health concerns and safety of the students to safely reopen and remain open. School schedules can be customized in many ways, and different approaches' impact on education and effectiveness in reducing infectious risks are different. To address this issue, we developed the School Virus Infection Simulation-Model (SVISM) for teachers and education policymakers. By taking into account the students' lesson schedules, classroom volume, air circulation rates in the classrooms, and infectability of the students, SVISM simulates the spread of infection at a school. We demonstrate the impact of several school schedules in self-contained and departmentalized classrooms and evaluate them in terms of the maximum number of students infected simultaneously, and the percentage of face-to-face lessons. The results show that the impact of increasing the classroom ventilation rate is not as stable as that of customizing school schedules. In addition, school schedules can differently impact the maximum number of students infected simultaneously, depending on whether classrooms are self-contained or departmentalized. We found that the maximum number of students infected simultaneously under a certain schedule with 50 percentage of face-to-face lessons in self-contained classrooms is higher than the maximum number of students infected simultaneously having schedules with a higher percentage of face-to-face lessons; this phenomenon was not found in departmentalized classrooms. These results show that the SVISM can help teachers and education policymakers plan school schedules appropriately to reduce the maximum number of students infected simultaneously, while also maintaining a certain rate of face-to-face lessons.

Study on the virulence evolution of SARS-CoV-2 and the trend of the epidemics of COVID-19

This is the first attempt to investigate the effects of the factors related to non-pharmaceutical interventions (NPIs) and the physical condition of the public on virulence evolution of SARS-CoV-2 and the trend of the epidemics of COVID-19 under an adaptive dynamics framework. Qualitative agreement of the prediction on the epidemics of COVID-19 with the actual situations convinced the rationality of the present model. The study showed that enhancing both NPIs (including public vigilance, quarantine measures, and hospitalization) and the physical condition of the public (including susceptibility and recovery speed) contributed to decreasing the prevalence of COVID-19 but only increasing public vigilance and decreasing the susceptibility of the public could also reduce the virulence of SARS-CoV-2. Therefore, controlling the contact rate and infection rate was the key to control not only the epidemic scale of COVID-19 but also the extent of its harm. On the other hand, the best way to control the epidemics was to increase the public vigilance and physical condition because both of them could reduce the prevalence and case fatality rate (CFR) of COVID-19. In addition, the enhancement of quarantine measures and hospitalization could bring the (slight) increase in the CFR of COVID-19.

Multi-Faceted Analysis of COVID-19 Epidemic in Korea Considering Omicron Variant: Mathematical Modeling-Based Study

BACKGROUND

The most recent variant of concern, omicron (B.1.1.529), has caused numerous cases worldwide including the Republic of Korea due to its fast transmission and reduced vaccine effectiveness.

METHODS

A mathematical model considering age-structure, vaccine, antiviral drugs, and influx of the omicron variant was developed. We estimated transmission rates among age groups using maximum likelihood estimation for the age-structured model. The impact of non-pharmaceutical interventions (NPIs; in community and border), quantified by a parameter μ in the force of infection, and vaccination were examined through a multi-faceted analysis. A theory-based endemic equilibrium study was performed to find the manageable number of cases according to omicron- and healthcare-related factors.

RESULTS

By fitting the model to the available data, the estimated values of μ ranged from 0.31 to 0.73, representing the intensity of NPIs such as social distancing level. If μ < 0.55 and 300,000 booster shots were administered daily from February 3, 2022, the number of severe cases was forecasted to exceed the severe bed capacity. Moreover, the number of daily cases is reduced as the timing of screening measures is delayed. If screening measure was intensified as early as November 24, 2021 and the number of overseas entrant cases was contained to 1 case per 10 days, simulations showed that the daily incidence by February 3, 2022 could have been reduced by 87%. Furthermore, we found that the incidence number in mid-December 2021 exceeded the theory-driven manageable number of daily cases.

CONCLUSION

NPIs, vaccination, and antiviral drugs influence the spread of omicron and number of severe cases in the Republic of Korea. Intensive and early screening measures during the emergence of a new variant is key in controlling the epidemic size. Using the endemic equilibrium of the model, a formula for the manageable daily cases depending on the severity rate and average length of hospital stay was derived so that the number of severe cases does not surpass the severe bed capacity.

Transmission of SARS-CoV-2 in educational settings in 2020: a review

OBJECTIVES

School closures have been used as a core non-pharmaceutical intervention (NPI) during the COVID-19 pandemic. This review aims at identifying SARS-CoV-2 transmission in educational settings during the first waves of the pandemic.

METHODS

This literature review assessed studies published between December 2019 and 1 April 2021 in Medline and Embase, which included studies that assessed educational settings from approximately January 2020 to January 2021. The inclusion criteria were based on the PCC framework (P-Population, C-Concept, C-Context). The study Population was restricted to people 1-17 years old (excluding neonatal transmission), the Concept was to assess child-to-child and child-to-adult transmission, while the Context was to assess specifically educational setting transmission.

RESULTS

Fifteen studies met inclusion criteria, ranging from daycare centres to high schools and summer camps, while eight studies assessed the re-opening of schools in the 2020-2021 school year. In principle, although there is sufficient evidence that children can both be infected by and transmit SARS-CoV-2 in school settings, the SAR remain relatively low-when NPI measures are implemented in parallel. Moreover, although the evidence was limited, there was an indication that younger children may have a lower SAR than adolescents.

CONCLUSIONS

Transmission in educational settings in 2020 was minimal-when NPI measures were implemented in parallel. However, with an upsurge of cases related to variants of concern, continuous surveillance and assessment of the evidence is warranted to ensure the maximum protection of the health of students and the educational workforce, while also minimising the numerous negative impacts that school closures may have on children.

Predictors of COVID-19 preventive behaviours in a sample of the Turkish population

AIM

This research aimed to determine predictors of COVID-19 preventive behaviours in a sample of the Turkish population.

METHODS

The study was conducted with 575 individuals. COVID-19 preventive behaviours were evaluated with a 19-item scale scored from 19 to 95. Knowledge on COVID-19 was evaluated with a 22-item scale scored from 0 to 22. General health literacy was evaluated with the Turkey Health Literacy Scale (THLS), which was scored from 0 to 50.

RESULTS

The average COVID-19 preventive behaviours score was moderately high in this sample of the Turkish population. Being female, having a higher level of education, better economic status, being a non-smoker, having a higher level of COVID-19 knowledge and better general health literacy score were significant predictors of COVID-19 preventive behaviours (P < 0.05).

CONCLUSION

Sociodemographic characteristics, knowledge of COVID-19 and general health literacy are crucial in preventing COVID-19 infections in a sample of the Turkish population.

SARS-CoV-2 transmission in schools in Korea: nationwide cohort study

OBJECTIVE

There is an urgent public need to readdress the school closure strategies. We aimed to describe the epidemiology of COVID-19 in schools and school-aged children to understand their roles in transmitting SARS-CoV-2 in Korea.

DESIGN

Retrospective cohort study.

SETTING

All schools in Korea PATIENTS: All school-aged children in Korea.

INTERVENTIONS

None (observational study).

MAIN OUTCOME MEASURES

Incidence rate, proportion of affected schools.

RESULTS

Between February and December 2020, the incidence rate was lower among school-aged children (63.2-79.8 per 100 000) compared with adults aged 19 and above (130.4 per 100 000). Household was the main route of transmission (62.3%), followed by community (21.3%) and school clusters (7.9%). Among the schools in Korea, 52% of secondary schools had COVID-19 cases, followed by 39% of primary schools and 3% of kindergartens.

CONCLUSIONS

We found that schools and school-aged children aged 7-18 years were not the main drivers of COVID-19 transmission. The major sources of transmission were households.

A new threshold reveals the uncertainty about the effect of school opening on diffusion of Covid-19

Studies on the effects of school openings or closures during the Covid-19 pandemic seem to reach contrasting conclusions even in similar contexts. We aim at clarifying this controversy. A mathematical analysis of compartmental models with subpopulations has been conducted, starting from the SIR model, and progressively adding features modeling outbreaks or upsurge of variants, lockdowns, and vaccinations. We find that in all cases, the in-school transmission rates only affect the overall course of the pandemic above a certain context dependent threshold. We provide rigorous proofs and computations of the thresdhold through linearization. We then confirm our theoretical findings through simulations and the review of data-driven studies that exhibit an often unnoticed phase transition. Specific implications are: awareness about the threshold could inform choice of data collection, analysis and release, such as in-school transmission rates, and clarify the reason for divergent conclusions in similar studies; schools may remain open at any stage of the Covid-19 pandemic, including variants upsurge, given suitable containment rules; these rules would be extremely strict and hardly sustainable if only adults are vaccinated, making a compelling argument for vaccinating children whenever possible.

Individual prevention and containment measures in schools in Catalonia, Spain, and community transmission of SARS-CoV-2 after school re-opening

BACKGROUND

Despite their clear lesser vulnerability to COVID-19, the extent by which children are susceptible to getting infected by SARS-CoV-2 and their capacity to transmit the infection to other people remains inadequately characterized. We aimed to evaluate the role of school reopening and the preventive strategies in place at schools in terms of overall risk for children and community transmission, by comparing transmission rates in children as detected by a COVID-19 surveillance platform in place in Catalonian Schools to the incidence at the community level.

METHODS AND FINDINGS

Infections detected in Catalan schools during the entire first trimester of classes (September-December 2020) were analysed and compared with the ongoing community transmission and with the modelled predicted number of infections. There were 30.486 infections (2.12%) documented among the circa 1.5M pupils, with cases detected in 54.0% and 97.5% of the primary and secondary centres, respectively. During the entire first term, the proportion of "bubble groups" (stable groups of children doing activities together) that were forced to undergo confinement ranged between 1 and 5%, with scarce evidence of substantial intraschool transmission in the form of chains of infections, and with ~75% of all detected infections not leading to secondary cases. Mathematical models were also used to evaluate the effect of different parameters related to the defined preventive strategies (size of the bubble group, number of days of confinement required by contacts of an index case). The effective reproduction number inside the bubble groups in schools (R*), defined as the average number of schoolmates infected by each primary case within the bubble, was calculated, yielding a value of 0.35 for primary schools and 0.55 for secondary schools, and compared with the outcomes of the mathematical model, implying decreased transmissibility for children in the context of the applied measures. Relative homogenized monthly cumulative incidence ([Formula: see text]) was assessed to compare the epidemiological dynamics among different age groups and this analysis suggested the limited impact of infections in school-aged children in the context of the overall community incidence.

CONCLUSIONS

During the fall of 2020, SARS-CoV-2 infections and COVID-19 cases detected in Catalan schools closely mirrored the underlying community transmission from the neighbourhoods where they were set and maintaining schools open appeared to be safe irrespective of underlying community transmission. Preventive measures in place in those schools appeared to be working for the early detection and rapid containment of transmission and should be maintained for the adequate and safe functioning of normal academic and face-to-face school activities.

School and community reopening during the COVID-19 pandemic: a mathematical modelling study

Operating schools safely during the COVID-19 pandemic requires a balance between health risks and the need for in-person learning. Using demographic and epidemiological data between 31 July and 23 November 2020 from Toronto, Canada, we developed a compartmental transmission model with age, household and setting structure to study the impact of schools reopening in September 2020. The model simulates transmission in the home, community and schools, accounting for differences in infectiousness between adults and children, and accounting for work-from-home and virtual learning. While we found a slight increase in infections among adults (2.2%) and children (4.5%) within the first eight weeks of school reopening, transmission in schools was not the key driver of the virus resurgence in autumn 2020. Rather, it was community spread that determined the outbreak trajectory, primarily due to increases in contact rates among adults in the community after school reopening. Analyses of cross-infection among households, communities and schools revealed that home transmission is crucial for epidemic progression and safely operating schools, while the degree of in-person attendance has a larger impact than other control measures in schools. This study suggests that safe school reopening requires the strict maintenance of public health measures in the community.

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

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.

Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics

The entire world has been affected by the outbreak of COVID-19 since early 2020. Human carriers are largely the spreaders of this new disease, and it spreads much faster compared to previously identified coronaviruses and other flu viruses. Although vaccines have been invented and released, it will still be a challenge to overcome this disease. To save lives, it is important to better understand how the virus is transmitted from one host to another and how future areas of infection can be predicted. Recently, the second wave of infection has hit multiple countries, and governments have implemented necessary measures to tackle the spread of the virus. We investigated the three phases of COVID-19 research through a selected list of mathematical modeling articles. To take the necessary measures, it is important to understand the transmission dynamics of the disease, and mathematical modeling has been considered a proven technique in predicting such dynamics. To this end, this paper summarizes all the available mathematical models that have been used in predicting the transmission of COVID-19. A total of nine mathematical models have been thoroughly reviewed and characterized in this work, so as to understand the intrinsic properties of each model in predicting disease transmission dynamics. The application of these nine models in predicting COVID-19 transmission dynamics is presented with a case study, along with detailed comparisons of these models. Toward the end of the paper, key behavioral properties of each model, relevant challenges and future directions are discussed.

Non-Pharmacological Infection Prevention and Control Interventions in COVID-19: What Does the Current Evidence Say?

Coronavirus disease-19 (COVID-19), a major global public health emergency has significantly impacted human health and livelihoods. The pandemic continues to spread and treatments and vaccines are at different stages of development. Mass vaccination has been rolled out worldwide. This review article provides a narrative summary of the evidence on various non-pharmacological interventions (NPIs) for COVID-19 containment. The authors reviewed the evidence published by the Norwegian Institute of Public Health map of COVID-19 evidence. Additional literature was identified from PubMed and Google Scholar, preprint sites, and news media. The search terms included "Social distancing measures" and "COVID 19", "Non-pharmacological interventions'' and "COVID 19", "COVID-19", "non-pharmacological interventions", "face mask", etc. The strength of the evidence for most studies on NPIs was 'weak to moderate' for restrictive NPIs. Ascertaining the impact of each NPI as a standalone intervention is difficult since NPIs are implemented simultaneously with other measures. Varying testing and reporting strategies across the countries and classification of deaths directly caused by COVID-19 create challenges in assessing the impact of restrictive NPIs on the case numbers and deaths. Evidence on hygiene measures such as face mask is more robust in design providing credible evidence on prevention of COVID-19 infection. Evidence from modeling studies, natural before-after studies, and anecdotal evidence from the strategies adopted by 'role model' countries suggests that continued use of NPIs is the only containment strategy until 'herd immunity' is achieved to reduce the severe disease and mortality.

Activity-based epidemic propagation and contact network scaling in auto-dependent metropolitan areas

We build on recent work to develop a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages person-trajectories obtained from an activity-based model calibrated for two full-scale prototype cities, consisting of representative synthetic populations and mobility networks for two contrasting auto-dependent city typologies. We simulate the propagation of the COVID-19 epidemic in both cities to analyze spreading patterns in urban networks across various activity types. Investigating the impact of the transit network, we find that its removal dampens disease propagation significantly, suggesting that transit restriction is more critical for mitigating post-peak disease spreading in transit dense cities. In the latter stages of disease spread, we find that the greatest share of infections occur at work locations. A statistical analysis of the resulting activity-based contact networks indicates that transit contacts are scale-free, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We validate our simulation results against existing case and mortality data across multiple cities in their respective typologies. Our framework demonstrates the potential for tracking epidemic propagation in urban networks, analyzing socio-demographic impacts and assessing activity- and mobility-specific implications of both non-pharmaceutical and pharmaceutical intervention strategies.

Family and School Relationship during COVID-19 Pandemic: A Systematic Review

Education systems worldwide have been affected by a sudden interruption in classroom learning because the coronavirus pandemic forced both the closure of all schools in March 2020 and the beginning of distance learning from home, thus compelling families, schools, and students to work together in a more coordinated fashion. The present systematic review was carried out following PRISMA guidelines. The main objective was to present critical information on the relationship between the family and the school in the face of the imposed distance learning scenario caused by COVID-19. A total of 25 articles dealing with the relationships established during the pandemic of any of the three agents involved (family, students, and school) were analysed. The results showed that the relationships between the three groups involved must be improved to some extent to meet the needs that have arisen as a result of distance learning. In conclusion, the educational scenario during the pandemic has been one of the most significant challenges experienced in the recent history of education.

Risk of COVID-19 transmission in heterogeneous age groups and effective vaccination strategy in Korea: a mathematical modeling study

OBJECTIVES

This study aims to analyze the possibility and conditions of maintaining an effective reproductive number below 1 using a mathematical model.

METHODS

The total population was divided into five age groups (0-17, 18-29, 30-59, 60-74, and ≥75 years). Maximum likelihood estimation (MLE) was used to estimate the transmission rate of each age group. Mathematical model simulation was conducted until December 31, 2021, by establishing various strategies for vaccination and social distancing without considering variants.

RESULTS

MLE results revealed that the group aged 0-17 years had a lower risk of transmission than other age groups, and the older age group had relatively high risks of infection. If 70% of the population will be vaccinated by the end of 2021, then simulations showed that even if social distancing was eased, the effective reproductive number would remain below 1 near August if it was not at the level of the third re-spreading period. However, if social distancing was eased and it reached the level of the re-spreading period, the effective reproductive number could be below 1 at the end of 2021.

CONCLUSIONS

Considering both stable and worsened situations, simulation results emphasized that sufficient vaccine supply and control of the epidemic by maintaining social distancing to prevent an outbreak at the level of the re-spreading period are necessary to minimize mortality and maintain the effective reproductive number below 1.

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

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.

Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Prevalence in Blood in a Large School Community Subject to a Coronavirus Disease 2019 Outbreak: A Cross-sectional Study

Background

A SARS-CoV-2 outbreak affecting 52 people from a large school community in Santiago, Chile was identified (March 12), nine days after the first country case. We assessed the magnitude of the outbreak and the role students and staff played using a self-administered antibody detection test and survey.

Methods

The school was closed on March 13, and the entire community was placed under quarantine. We implemented a home-delivery, self-administered, IgG/IgM antibody test and survey to a classroom stratified sample of students and all staff from May 4-19. We aimed to determine overall seroprevalence rates by age group, reported symptoms, contact exposure and to explore dynamics of transmission.

Results

Antibody positivity rates were 9.9% (95%CI: 8.2-11.8) for 1,009 students and 16.6% (95%CI: 12.1-21.9) for 235 staff. Among students, positivity was associated with younger age (P=0.01), lower grade level (P=0.05), prior RT-PCR positivity (P=0.03), and history of contact with a confirmed case (P<0.001). Among staff, positivity was higher in teachers (P=0.01) and in those previously RT-PCR positive (P<0.001). Excluding RT-PCR positive individuals, antibody positivity was associated with fever in adults and children (P=0.02; P=0.002), abdominal pain in children (P=0.001), and chest pain in adults (P=0.02). Within antibody positive individuals, 40% of students and 18% of staff reported no symptoms (P=0.01).

Conclusions

Teachers were more affected during the outbreak and younger children were at higher infection risk, likely because index case(s) were teachers and/or parents from preschool. Self-administered antibody testing, supervised remotely, proved to be a suitable and rapid tool. Our study provides useful information for school re-openings.

School closures during the coronavirus disease 2019 outbreak

School closures during the coronavirus disease 2019 (COVID-19) pandemic have been outlined in studies from different disciplines, including economics, sociology, mathematical modeling, epidemiology, and public health. In this review, we discuss the implications of school closures in the context of the current COVID-19 pandemic. Modeling studies of the effects of school closures, largely derived from the pandemic influenza model, on severe acute respiratory syndrome coronavirus 2 produced conflicting results. Earlier studies assessed the risk of school reopening by modeling transmission across schools and communities; however, it remains unclear whether the risk is due to increased transmission in adults or children. The empirical findings of the impact of school closures on COVID-19 outbreaks suggest no clear effect, likely because of heterogeneity in community infection pressure, differences in school closure strategies, or the use of multiple interventions. The benefits of school closings are unclear and not readily quantifiable; however, they must be weighed against the potential high social costs, which can also negatively affect the health of this generation.

Systematic review of predictive mathematical models of COVID-19 epidemic

BACKGROUND

Various mathematical models were published to predict the epidemiological consequences of the COVID-19 pandemic. This systematic review has studied the initial epidemiological models.

METHODS

Articles published from January to June 2020 were extracted from databases using search strings and those peer-reviewed with full text in English were included in the study. They were analysed as to whether they made definite predictions in terms of time and numbers, or contained only mathematical assumptions and open-ended predictions. Factors such as early vs. late prediction models, long-term vs. curve-fitting models and comparisons based on modelling techniques were analysed in detail.

RESULTS

Among 56,922 hits in 05 databases, screening yielded 434 abstracts, of which 72 articles were included. Predictive models comprised over 70% (51/72) of the articles, with susceptible, exposed, infectious and recovered (SEIR) being the commonest type (mean duration of prediction being 3 months). Common predictions were regarding cumulative cases (44/72, 61.1%), time to reach total numbers (41/72, 56.9%), peak numbers (22/72, 30.5%), time to peak (24/72, 33.3%), hospital utilisation (7/72, 9.7%) and effect of lockdown and NPIs (50/72, 69.4%). The commonest countries for which models were predicted were China followed by USA, South Korea, Japan and India. Models were published by various professionals including Engineers (12.5%), Mathematicians (9.7%), Epidemiologists (11.1%) and Physicians (9.7%) with a third (32.9%) being the result of collaborative efforts between two or more professions.

CONCLUSION

There was a wide diversity in the type of models, duration of prediction and the variable that they predicted, with SEIR model being the commonest type.

COVID-19 Vaccine Priority Strategy Using a Heterogenous Transmission Model Based on Maximum Likelihood Estimation in the Republic of Korea

(1) Background: The vaccine supply is likely to be limited in 2021 due to constraints in manufacturing. To maximize the benefit from the rollout phase, an optimal strategy of vaccine allocation is necessary based on each country's epidemic status. (2) Methods: We first developed a heterogeneous population model considering the transmission matrix using maximum likelihood estimation based on the epidemiological records of individual COVID-19 cases in the Republic of Korea. Using this model, the vaccine priorities for minimizing mortality or incidence were investigated. (3) Results: The simulation results showed that the optimal vaccine allocation strategy to minimize the mortality (or incidence) was to prioritize elderly and healthcare workers (or adults) as long as the reproductive number was below 1.2 (or over 0.9). (4) Conclusion: Our simulation results support the current Korean government vaccination priority strategy, which prioritizes healthcare workers and senior groups to minimize mortality, under the condition that the reproductive number remains below 1.2. This study revealed that, in order to maintain the current vaccine priority policy, it is important to ensure that the reproductive number does not exceed the threshold by concurrently implementing nonpharmaceutical interventions.

COVID-19: breaking down a global health crisis

Coronavirus disease 2019 (COVID-19) is the second pandemic of the twenty-first century, with over one-hundred million infections and over two million deaths to date. It is a novel strain from the Coronaviridae family, named Severe Acute Respiratory Distress Syndrome Coronavirus-2 (SARS-CoV-2); the 7th known member of the coronavirus family to cause disease in humans, notably following the Middle East Respiratory syndrome (MERS), and Severe Acute Respiratory Distress Syndrome (SARS). The most characteristic feature of this single-stranded RNA molecule includes the spike glycoprotein on its surface. Most patients with COVID-19, of which the elderly and immunocompromised are most at risk, complain of flu-like symptoms, including dry cough and headache. The most common complications include pneumonia, acute respiratory distress syndrome, septic shock, and cardiovascular manifestations. Transmission of SARS-CoV-2 is mainly via respiratory droplets, either directly from the air when an infected patient coughs or sneezes, or in the form of fomites on surfaces. Maintaining hand-hygiene, social distancing, and personal protective equipment (i.e., masks) remain the most effective precautions. Patient management includes supportive care and anticoagulative measures, with a focus on maintaining respiratory function. Therapy with dexamethasone, remdesivir, and tocilizumab appear to be most promising to date, with hydroxychloroquine, lopinavir, ritonavir, and interferons falling out of favour. Additionally, accelerated vaccination efforts have taken place internationally, with several promising vaccinations being mass deployed. In response to the COVID-19 pandemic, countries and stakeholders have taken varying precautions to combat and contain the spread of the virus and dampen its collateral economic damage. This review paper aims to synthesize the impact of the virus on a global, micro to macro scale.

Social distancing and testing as optimal strategies against the spread of COVID-19 in the Rio Grande Valley of Texas

At the beginning of August 2020, the Rio Grande Valley (RGV) of Texas experienced a rapid increase of coronavirus disease 2019 (abbreviated as COVID-19) cases and deaths. This study aims to determine the optimal levels of effective social distancing and testing to slow the virus spread at the outset of the pandemic. We use an age-stratified eight compartment epidemiological model to depict COVID-19 transmission in the community and within households. With a simulated 120-day outbreak period data we obtain a post 180-days period optimal control strategy solution. Our results show that easing social distancing between adults by the end of the 180-day period requires very strict testing a month later and then daily testing rates of 5% followed by isolation of positive cases. Relaxing social distancing rates in adults from 50% to 25% requires both children and seniors to maintain social distancing rates of 50% for nearly the entire period while maintaining maximum testing rates of children and seniors for 150 of the 180 days considered in this model. Children have higher contact rates which leads to transmission based on our model, emphasizing the need for caution when considering school reopenings.

Education online in lockdown: limits and possibilities. The vision of families in Spain

Purpose

March 14, 2020, marked the beginning of an unexpected state of emergency in Spain due to the impact of the coronavirus disease 2019 (COVID-19). From that moment on, the educational system had to adapt so that millions of students could continue their education at home. Through a descriptive study, the reality and perceptions that Spanish families have about the educational actions that took place during the compulsory lockdown of the Spanish population is presented.

Design/methodology/approach

3,400 representatives of family units from 17 autonomous communities answered a survey, the data were analyzed using descriptive and frequency statistics.

Findings

Relevant conclusions were drawn from the results. Despite the efforts of the authorities, the economic, cultural, social and digital divides leave many households without access to the fundamental right of education; families value the support of technologies but consider that they should not replace the face-to-face education that is necessary for the development process of minors; it is necessary to adapt the school content for a future postpandemic, discriminating the expendable from the essential in the school curriculum; priority must be given to the integral well-being of people in educational policies and also to the most vulnerable ones.

Originality/value

The study allows progress in the analysis of educational policy proposals in the face of future crisis.

The effect of control measures on COVID-19 transmission in South Korea

Countries around the world have taken control measures to mitigate the spread of COVID-19, including Korea. Social distancing is considered an essential strategy to reduce transmission in the absence of vaccination or treatment. While interventions have been successful in controlling COVID-19 in Korea, maintaining the current restrictions incurs great social costs. Thus, it is important to analyze the impact of different polices on the spread of the epidemic. To model the COVID-19 outbreak, we use an extended age-structured SEIR model with quarantine and isolation compartments. The model is calibrated to age-specific cumulative confirmed cases provided by the Korea Disease Control and Prevention Agency (KDCA). Four control measures—school closure, social distancing, quarantine, and isolation—are investigated. Because the infectiousness of the exposed has been controversial, we study two major scenarios, considering contributions to infection of the exposed, the quarantined, and the isolated. Assuming the transmission rate would increase more than 1.7 times after the end of social distancing, a second outbreak is expected in the first scenario. The epidemic threshold for increase of contacts between teenagers after school reopening is 3.3 times, which brings the net reproduction number to 1. The threshold values are higher in the second scenario. If the average time taken until isolation and quarantine reduces from three days to two, cumulative cases are reduced by 60% and 47% in the first scenario, respectively. Meanwhile, the reduction is 33% and 41%, respectively, for rapid isolation and quarantine in the second scenario. Without social distancing, a second wave is possible, irrespective of whether we assume risk of infection by the exposed. In the non-infectivity of the exposed scenario, early detection and isolation are significantly more effective than quarantine. Furthermore, quarantining the exposed is as important as isolating the infectious when we assume that the exposed also contribute to infection.

Optimal strategies for social distancing and testing to control COVID-19

The coronavirus disease (COVID-19) has infected more than 79 million individuals, with 1.7 million deaths worldwide. Several countries have implemented social distancing and testing policies with contact tracing as a measure to flatten the curve of the ongoing pandemic. Optimizing these control measures is urgent given the substantial societal and economic impacts associated with infection and interventions. To determine the optimal social distancing and testing strategies, we developed a mathematical model of COVID-19 transmission and applied optimal control theory, identifying the best approach to reduce the epidemiological burden of COVID-19 at a minimal cost. The results demonstrate that testing as a standalone optimal strategy does not have a significant effect on the final size of an epidemic, but it would delay the peak of the pandemic. If social distancing is the sole control strategy, it would be optimal to gradually increase the level of social distancing as the incidence curve of COVID-19 grows, and relax the measures after the curve has reached its peak. Compared with a single strategy, combined social distancing and testing strategies are demonstrated to be more efficient at reducing the disease burden, and they can delay the peak of the disease. To optimize these strategies, testing should be maintained at a maximum level in the early phases and after the peak of the epidemic, whereas social distancing should be intensified when the prevalence of the disease is greater than 15%. Accordingly, public health agencies should implement early testing and switch to social distancing when the incidence level begins to increase. After the peak of the pandemic, it would be optimal to gradually relax social distancing and switch back to testing.

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model

Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.

Strategies to exiting the COVID-19 lockdown for workplace and school: A scoping review

In an attempt to curb the COVID-19 pandemic, several countries have implemented various social restrictions, such as closing schools and asking people to work from home. Nevertheless, after months of strict quarantine, a reopening of society is required. Many countries are planning exit strategies to progressively lift the lockdown without leading to an increase in the number of COVID-19 cases. Identifying exit strategies for a safe reopening of schools and places of work is critical in informing decision-makers on the management of the COVID-19 health crisis. This scoping review describes multiple population-wide strategies, including social distancing, testing, and contact tracing. It highlights how each strategy needs to be based on both the epidemiological situation and contextualize at local circumstances to anticipate the possibility of COVID-19 resurgence. However, the retrieved evidence lacks operational solutions and are mainly based on mathematical models and derived from grey literature. There is a need to report the impact of the implementation of country-tailored strategies and assess their effectiveness through high-quality experimental studies.

On the reliability of predictions on Covid-19 dynamics: A systematic and critical review of modelling techniques

Since the emergence of the novel 2019 coronavirus pandemic in December 2019 (COVID-19), numerous modellers have used diverse techniques to assess the dynamics of transmission of the disease, predict its future course and determine the impact of different control measures. In this study, we conducted a global systematic literature review to summarize trends in the modelling techniques used for Covid-19 from January 1st, 2020 to November 30th, 2020. We further examined the accuracy and precision of predictions by comparing predicted and observed values for cumulative cases and deaths as well as uncertainties of these predictions. From an initial 4311 peer-reviewed articles and preprints found with our defined keywords, 242 were fully analysed. Most studies were done on Asian (78.93%) and European (59.09%) countries. Most of them used compartmental models (namely SIR and SEIR) (46.1%) and statistical models (growth models and time series) (31.8%) while few used artificial intelligence (6.7%), Bayesian approach (4.7%), Network models (2.3%) and Agent-based models (1.3%). For the number of cumulative cases, the ratio of the predicted over the observed values and the ratio of the amplitude of confidence interval (CI) or credibility interval (CrI) of predictions and the central value were on average larger than 1 indicating cases of inaccurate and imprecise predictions, and large variation across predictions. There was no clear difference among models used for these two ratios. In 75% of predictions that provided CI or CrI, observed values fall within the 95% CI or CrI of the cumulative cases predicted. Only 3.7% of the studies predicted the cumulative number of deaths. For 70% of the predictions, the ratio of predicted over observed cumulative deaths was less or close to 1. Also, the Bayesian model made predictions closer to reality than classical statistical models, although these differences are only suggestive due to the small number of predictions within our dataset (9 in total). In addition, we found a significant negative correlation (rho = - 0.56, p = 0.021) between this ratio and the length (in days) of the period covered by the modelling, suggesting that the longer the period covered by the model the likely more accurate the estimates tend to be. Our findings suggest that while predictions made by the different models are useful to understand the pandemic course and guide policy-making, some were relatively accurate and precise while other not.

Measures implemented in the school setting to contain the COVID-19 pandemic: a scoping review

BACKGROUND

In response to the spread of SARS-CoV-2 and the impact of COVID-19, national and subnational governments implemented a variety of measures in order to control the spread of the virus and the associated disease. While these measures were imposed with the intention of controlling the pandemic, they were also associated with severe psychosocial, societal, and economic implications on a societal level. One setting affected heavily by these measures is the school setting. By mid-April 2020, 192 countries had closed schools, affecting more than 90% of the world's student population. In consideration of the adverse consequences of school closures, many countries around the world reopened their schools in the months after the initial closures. To safely reopen schools and keep them open, governments implemented a broad range of measures. The evidence with regards to these measures, however, is heterogeneous, with a multitude of study designs, populations, settings, interventions and outcomes being assessed. To make sense of this heterogeneity, we conducted a rapid scoping review (8 October to 5 November 2020). This rapid scoping review is intended to serve as a precursor to a systematic review of effectiveness, which will inform guidelines issued by the World Health Organization (WHO). This review is reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist and was registered with the Open Science Framework.

OBJECTIVES

To identify and comprehensively map the evidence assessing the impacts of measures implemented in the school setting to reopen schools, or keep schools open, or both, during the SARS-CoV-2/COVID-19 pandemic, with particular focus on the types of measures implemented in different school settings, the outcomes used to measure their impacts and the study types used to assess these.

SEARCH METHODS

We searched the Cochrane COVID-19 Study Register, MEDLINE, Embase, the CDC COVID-19 Research Articles Downloadable Database for preprints, and the WHO COVID-19 Global literature on coronavirus disease on 8 October 2020.

SELECTION CRITERIA

We included studies that assessed the impact of measures implemented in the school setting. Eligible populations were populations at risk of becoming infected with SARS-CoV-2, or developing COVID-19 disease, or both, and included people both directly and indirectly impacted by interventions, including students, teachers, other school staff, and contacts of these groups, as well as the broader community. We considered all types of empirical studies, which quantitatively assessed impact including epidemiological studies, modelling studies, mixed-methods studies, and diagnostic studies that assessed the impact of relevant interventions beyond diagnostic test accuracy. Broad outcome categories of interest included infectious disease transmission-related outcomes, other harmful or beneficial health-related outcomes, and societal, economic, and ecological implications.

DATA COLLECTION AND ANALYSIS

We extracted data from included studies in a standardized manner, and mapped them to categories within our a priori logic model where possible. Where not possible, we inductively developed new categories. In line with standard expectations for scoping reviews, the review provides an overview of the existing evidence regardless of methodological quality or risk of bias, and was not designed to synthesize effectiveness data, assess risk of bias, or characterize strength of evidence (GRADE).

MAIN RESULTS

We included 42 studies that assessed measures implemented in the school setting. The majority of studies used mathematical modelling designs (n = 31), while nine studies used observational designs, and two studies used experimental or quasi-experimental designs. Studies conducted in real-world contexts or using real data focused on the WHO European region (EUR; n = 20), the WHO region of the Americas (AMR; n = 13), the West Pacific region (WPR; n = 6), and the WHO Eastern Mediterranean Region (EMR; n = 1). One study conducted a global assessment and one did not report on data from, or that were applicable to, a specific country. Three broad intervention categories emerged from the included studies: organizational measures to reduce transmission of SARS-CoV-2 (n = 36), structural/environmental measures to reduce transmission of SARS-CoV-2 (n = 11), and surveillance and response measures to detect SARS-CoV-2 infections (n = 19). Most studies assessed SARS-CoV-2 transmission-related outcomes (n = 29), while others assessed healthcare utilization (n = 8), other health outcomes (n = 3), and societal, economic, and ecological outcomes (n = 5). Studies assessed both harmful and beneficial outcomes across all outcome categories.

AUTHORS' CONCLUSIONS

We identified a heterogeneous and complex evidence base of measures implemented in the school setting. This review is an important first step in understanding the available evidence and will inform the development of rapid reviews on this topic.

Stepwise School Opening and an Impact on the Epidemiology of COVID-19 in the Children

BACKGROUND

Data on severe acute respiratory syndrome coronavirus-2 transmission from a pediatric index patient to others at the school setting are limited. Epidemiological data on pediatric coronavirus disease 2019 (COVID-19) cases after school opening are warranted.

METHODS

We analyzed data of the pediatric patients with COVID-19 collected from the press release of the Korea Centers for Disease Control and Prevention. Information on the school opening delay and re-opening policies were achieved from the press release of the Korean Ministry of Education.

RESULTS

The school openings were delayed three times in March 2020. Online classes started from April 9, and off-line (in-person) classes started from May 20 to June 8 at four steps in different grades of students. There was no sudden increase in pediatric cases after the school opening, and the proportion of pediatric cases among total confirmed cases in the nation around 7.0%. As of July 31, 44 children from 38 schools and kindergartens were diagnosed with COVID-19 after off-line classes started. More than 13,000 students and staffs were tested; only one additional student was found to be infected in the same classroom. The proportions of pediatric patients without information on infection sources were higher in older age groups than in younger age groups (17.4% vs. 52.4%, P = 0.014). In the younger age group, 78.3% of children were infected by family members, while only 23.8% of adolescents in the older age group were infected by family members (P < 0.001).

CONCLUSION

Korea had a successful transition from school closure to online and off-line school opening, which did not cause significant school-related outbreak among the pediatric population.

COVID-ABS: An agent-based model of COVID-19 epidemic to simulate health and economic effects of social distancing interventions

The COVID-19 pandemic due to the SARS-CoV-2 coronavirus has directly impacted the public health and economy worldwide. To overcome this problem, countries have adopted different policies and non-pharmaceutical interventions for controlling the spread of the virus. This paper proposes the COVID-ABS, a new SEIR (Susceptible-Exposed-Infected-Recovered) agent-based model that aims to simulate the pandemic dynamics using a society of agents emulating people, business and government. Seven different scenarios of social distancing interventions were analyzed, with varying epidemiological and economic effects: (1) do nothing, (2) lockdown, (3) conditional lockdown, (4) vertical isolation, (5) partial isolation, (6) use of face masks, and (7) use of face masks together with 50% of adhesion to social isolation. In the impossibility of implementing scenarios with lockdown, which present the lowest number of deaths and highest impact on the economy, scenarios combining the use of face masks and partial isolation can be the more realistic for implementation in terms of social cooperation. The COVID-ABS model was implemented in Python programming language, with source code publicly available. The model can be easily extended to other societies by changing the input parameters, as well as allowing the creation of a multitude of other scenarios. Therefore, it is a useful tool to assist politicians and health authorities to plan their actions against the COVID-19 epidemic.

Estimating the Effectiveness of Non-Pharmaceutical Interventions on COVID-19 Control in Korea

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has posed significant global public health challenges and created a substantial economic burden. Korea has experienced an extensive outbreak, which was linked to a religion-related super-spreading event. However, the implementation of various non-pharmaceutical interventions (NPIs), including social distancing, spring semester postponing, and extensive testing and contact tracing controlled the epidemic. Herein, we estimated the effectiveness of each NPI using a simulation model.

METHODS

A compartment model with a susceptible-exposed-infectious-quarantined-hospitalized structure was employed. Using the Monte-Carlo-Markov-Chain algorithm with Gibbs' sampling method, we estimated the time-varying effective contact rate to calibrate the model with the reported daily new confirmed cases from February 12th to March 31st (7 weeks). Moreover, we conducted scenario analyses by adjusting the parameters to estimate the effectiveness of NPI.

RESULTS

Relaxed social distancing among adults would have increased the number of cases 27.4-fold until the end of March. Spring semester non-postponement would have increased the number of cases 1.7-fold among individuals aged 0-19, while lower quarantine and detection rates would have increased the number of cases 1.4-fold.

CONCLUSION

Among the three NPI measures, social distancing in adults showed the highest effectiveness. The substantial effect of social distancing should be considered when preparing for the 2nd wave of COVID-19.

Exposure to air pollution and scarlet fever resurgence in China: a six-year surveillance study

Scarlet fever has resurged in China starting in 2011, and the environment is one of the potential reasons. Nationwide data on 655,039 scarlet fever cases and six air pollutants were retrieved. Exposure risks were evaluated by multivariate distributed lag nonlinear models and a meta-regression model. We show that the average incidence in 2011-2018 was twice that in 2004-2010 [RR = 2.30 (4.40 vs. 1.91), 95% CI: 2.29-2.31; p < 0.001] and generally lower in the summer and winter holiday (p = 0.005). A low to moderate correlation was seen between scarlet fever and monthly NO₂ (r = 0.21) and O₃ (r = 0.11). A 10 μg/m³ increase of NO₂ and O₃ was significantly associated with scarlet fever, with a cumulative RR of 1.06 (95% CI: 1.02-1.10) and 1.04 (95% CI: 1.01-1.07), respectively, at a lag of 0 to 15 months. In conclusion, long-term exposure to ambient NO₂ and O₃ may be associated with an increased risk of scarlet fever incidence, but direct causality is not established.

Using an integrated social cognition model to predict COVID-19 preventive behaviours

Objectives

Rates of novel coronavirus disease 2019 (COVID‐19) infections have rapidly increased worldwide and reached pandemic proportions. A suite of preventive behaviours have been recommended to minimize risk of COVID‐19 infection in the general population. The present study utilized an integrated social cognition model to explain COVID‐19 preventive behaviours in a sample from the Iranian general population.

Design

The study adopted a three‐wave prospective correlational design.

Methods

Members of the general public (N = 1,718, M_age = 33.34, SD = 15.77, male = 796, female = 922) agreed to participate in the study. Participants completed self‐report measures of demographic characteristics, intention, attitude, subjective norm, perceived behavioural control, and action self‐efficacy at an initial data collection occasion. One week later, participants completed self‐report measures of maintenance self‐efficacy, action planning and coping planning, and, a further week later, measures of COVID‐19 preventive behaviours. Hypothesized relationships among social cognition constructs and COVID‐19 preventive behaviours according to the proposed integrated model were estimated using structural equation modelling.

Results

The proposed model fitted the data well according to multiple goodness‐of‐fit criteria. All proposed relationships among model constructs were statistically significant. The social cognition constructs with the largest effects on COVID‐19 preventive behaviours were coping planning (β = .575, p < .001) and action planning (β = .267, p < .001).

Conclusions

Current findings may inform the development of behavioural interventions in health care contexts by identifying intervention targets. In particular, findings suggest targeting change in coping planning and action planning may be most effective in promoting participation in COVID‐19 preventive behaviours.

Diabetes and COVID-19: Global and regional perspectives

The coronavirus disease-2019 (COVID-19) has been designated as a highly contagious infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) since December 2019, when an outbreak of pneumonia cases emerged in Wuhan, China. The COVID-19 pandemic has led to a global health crisis, devastating the social, economic and political aspects of life. Many clinicians, health professionals, scientists, organizations, and governments have actively defeated COVID-19 and shared their experiences of the SARS-CoV2. Diabetes is one of the major risk factors for fatal outcomes from COVID-19. Patients with diabetes are vulnerable to infection because of hyperglycemia; impaired immune function; vascular complications; and comorbidities such as hypertension, dyslipidemia, and cardiovascular disease. In addition, angiotensin-converting enzyme 2 (ACE2) is a receptor for SARS-CoV-2 in the human body. Hence, the use of angiotensin-directed medications in patients with diabetes requires attention. The severity and mortality from COVID-19 was significantly higher in patients with diabetes than in those without. Thus, the patients with diabetes should take precautions during the COVID-19 pandemic. Therefore, we review the current knowledge of COVID-19 including the global and regional epidemiology, virology, impact of diabetes on COVID-19, treatment of COVID-19, and standard of care in the management of diabetes during this critical period.

Need to establish a new adolescent suicide prevention programme in South Korea

Adolescent suicide is the leading cause of death among South Korean (Korean) youth. Despite great efforts being made towards suicide prevention in Korea, the suicide rate has not decreased significantly. There is an urgent need for a new adolescent suicide prevention strategy. This paper describes the seriousness of the issue of adolescent suicide in Korea, evaluates its current management by the SWOT analysis (strengths, weaknesses, opportunities and threats) and further recommends a new suicide prevention programme that integrates national/social involvement (State Suicide Intervention Committee, suicide posts’ monitoring, parental divorce information sharing and Adolescence Mental Health Promotion Foundation), school-based programmes (continuous monitoring system, psychology consultation team and mental health educational curricula) and family-based programmes (parental education and family-school communication). In addition, genetic analysis, biochemical tests and psychological disease registration are the indispensable elements that aid in suicidal behaviour prevention and prediction.

Protocol of a population-based prospective COVID-19 cohort study Munich, Germany (KoCo19)

BACKGROUND

Due to the SARS-CoV-2 pandemic, public health interventions have been introduced globally in order to prevent the spread of the virus and avoid the overload of health care systems, especially for the most severely affected patients. Scientific studies to date have focused primarily on describing the clinical course of patients, identifying treatment options and developing vaccines. In Germany, as in many other regions, current tests for SARS-CoV2 are not conducted on a representative basis and in a longitudinal design. Furthermore, knowledge about the immune status of the population is lacking. Nonetheless, these data are needed to understand the dynamics of the pandemic and hence to appropriately design and evaluate interventions. For this purpose, we recently started a prospective population-based cohort in Munich, Germany, with the aim to develop a better understanding of the state and dynamics of the pandemic.

METHODS

In 100 out of 755 randomly selected constituencies, 3000 Munich households are identified via random route and offered enrollment into the study. All household members are asked to complete a baseline questionnaire and subjects ≥14 years of age are asked to provide a venous blood sample of ≤3 ml for the determination of SARS-CoV-2 IgG/IgA status. The residual plasma and the blood pellet are preserved for later genetic and molecular biological investigations. For twelve months, each household member is asked to keep a diary of daily symptoms, whereabouts and contacts via WebApp. If symptoms suggestive for COVID-19 are reported, family members, including children < 14 years, are offered a pharyngeal swab taken at the Division of Infectious Diseases and Tropical Medicine, LMU University Hospital Munich, for molecular testing for SARS-CoV-2. In case of severe symptoms, participants will be transferred to a Munich hospital. For one year, the study teams re-visits the households for blood sampling every six weeks.

DISCUSSION

With the planned study we will establish a reliable epidemiological tool to improve the understanding of the spread of SARS-CoV-2 and to better assess the effectiveness of public health measures as well as their socio-economic effects. This will support policy makers in managing the epidemic based on scientific evidence.