Modeling the impact of school reopening and contact tracing strategies on Covid-19 dynamics in different epidemiologic settings in Brazil

Among the various non–pharmaceutical interventions implemented in response to the Covid–19 pandemic during 2020, school closures have been in place in several countries to reduce infection transmission. Nonetheless, the significant short and long–term impacts of prolonged suspension of in–person classes is a major concern. There is still considerable debate around the best timing for school closure and reopening, its impact on the dynamics of disease transmission, and its effectiveness when considered in association with other mitigation measures. Despite the erratic implementation of mitigation measures in Brazil, school closures were among the first measures taken early in the pandemic in most of the 27 states in the country. Further, Brazil delayed the reopening of schools and stands among the countries in which schools remained closed for the most prolonged period in 2020. To assess the impact of school reopening and the effect of contact tracing strategies in rates of Covid–19 cases and deaths, we model the epidemiological dynamics of disease transmission in 3 large urban centers in Brazil under different epidemiological contexts. We implement an extended SEIR model stratified by age and considering contact networks in different settings – school, home, work, and elsewhere, in which the infection transmission rate is affected by various intervention measures. After fitting epidemiological and demographic data, we simulate scenarios with increasing school transmission due to school reopening. Our model shows that reopening schools results in a non–linear increase of reported Covid-19 cases and deaths, which is highly dependent on infection and disease incidence at the time of reopening. While low rates of within[&ndash]school transmission resulted in small effects on disease incidence (cases/100,000 pop), intermediate or high rates can severely impact disease trends resulting in escalating rates of new cases even if other interventions remain unchanged. When contact tracing and quarantining are restricted to school and home settings, a large number of daily tests is required to produce significant effects of reducing the total number of hospitalizations and deaths. Our results suggest that policymakers should carefully consider the epidemiological context and timing regarding the implementation of school closure and return of in-person school activities. Also, although contact tracing strategies are essential to prevent new infections and outbreaks within school environments, our data suggest that they are alone not sufficient to avoid significant impacts on community transmission in the context of school reopening in settings with high and sustained transmission rates.

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Data-Driven Deep-Learning Algorithm for Asymptomatic COVID-19 Model with Varying Mitigation Measures and Transmission Rate

Epidemiologia ◽

10.3390/epidemiologia2040033 ◽

2021 ◽

Vol 2 (4) ◽

pp. 471-489

Author(s):

K. D. Olumoyin ◽

A. Q. M. Khaliq ◽

K. M. Furati

Keyword(s):

Learning Algorithm ◽

Transmission Rate ◽

The United States ◽

Data Driven ◽

Mitigation Measures ◽

Contact Tracing ◽

Deep Learning Algorithm ◽

The United Kingdom ◽

Neural Network Algorithm ◽

The Impact

Epidemiological models with constant parameters may not capture satisfactory infection patterns in the presence of pharmaceutical and non-pharmaceutical mitigation measures during a pandemic, since infectiousness is a function of time. In this paper, an Epidemiology-Informed Neural Network algorithm is introduced to learn the time-varying transmission rate for the COVID-19 pandemic in the presence of various mitigation scenarios. There are asymptomatic infectives, mostly unreported, and the proposed algorithm learns the proportion of the total infective individuals that are asymptomatic infectives. Using cumulative and daily reported cases of the symptomatic infectives, we simulate the impact of non-pharmaceutical mitigation measures such as early detection of infectives, contact tracing, and social distancing on the basic reproduction number. We demonstrate the effectiveness of vaccination on the transmission of COVID-19. The accuracy of the proposed algorithm is demonstrated using error metrics in the data-driven simulation for COVID-19 data of Italy, South Korea, the United Kingdom, and the United States.

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Schools are not islands: Balancing COVID-19 risk and educational benefits using structural and temporal countermeasures

10.1101/2020.09.08.20190942 ◽

2020 ◽

Cited By ~ 4

Author(s):

Jamie A. Cohen ◽

Dina Mistry ◽

Cliff C. Kerr ◽

Daniel J. Klein

Keyword(s):

Elementary Schools ◽

Disease Transmission ◽

Contact Tracing ◽

Teaching Staff ◽

School Type ◽

School Closures ◽

Educational Benefits ◽

The Face ◽

School Scheduling ◽

The Impact

Background: School closures around the world contributed to reducing the transmission of COVID-19. In the face of significant uncertainty around the epidemic impact of in-person schooling, policymakers, parents, and teachers are weighing the risks and benefits of returning to in-person education. In this context, we examined the impact of different school reopening scenarios on transmission within and outside of schools and on the share of school days that would need to be spent learning at a distance. Methods: We used an agent-based mathematical model of COVID-19 transmission and interventions to quantify the impact of school reopening on disease transmission and the extent to which school-based interventions could mitigate epidemic spread within and outside schools. We compared seven school reopening strategies that vary the degree of countermeasures within schools to mitigate COVID-19 transmission, including the use of face masks, physical distancing, classroom cohorting, screening, testing, and contact tracing, as well as schedule changes to reduce the number of students in school. We considered three scenarios for the size of the epidemic in the two weeks prior to school reopening: 20, 50, or 110 detected cases per 100,000 individuals and assumed the epidemic was slowly declining with full school closures. For each scenario, we calculated the percentage of schools that would have at least one person arriving at school with an active COVID-19 infection on the first day of school; the percentage of in-person school days that would be lost due to scheduled distance learning, symptomatic screening or quarantine; the cumulative infection rate for students, staff and teachers over the first three months of school; and the effective reproduction number averaged over the first three months of school within the community. Findings: In-person schooling poses significant risks to students, teachers, and staff. On the first day of school, 5-42% of schools would have at least one person arrive at school with active COVID-19, depending on the incidence of COVID in the community and the school type. However, reducing class sizes via A/B school scheduling, combined with an incremental approach that returns elementary schools in person and keeps all other students remote, can mitigate COVID transmission. In the absence of any countermeasures in schools, we expect 6-25% of teaching and non-teaching staff and 4-20% of students to be infected with COVID in the first three months of school, depending upon the case detection rate. Schools can lower this risk to as low as 0.2% for staff and 0.1% for students by returning elementary schools with a hybrid schedule while all other grades continue learning remotely. However, this approach would require 60-85% of all school days to be spent at home. Despite the significant risks to the school population, reopening schools would not significantly increase community-wide transmission, provided sufficient countermeasures are implemented in schools. Interpretation: Without extensive countermeasures, school reopening may lead to an increase in infections and a significant number of re-closures as cases are identified among staff and students. Returning elementary schools only with A/B scheduling is the lowest risk school reopening strategy that includes some in-person learning.

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Estimating asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan: a mathematical modeling study

BMC Infectious Diseases ◽

10.1186/s12879-021-06078-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xi Huo ◽

Jing Chen ◽

Shigui Ruan

Keyword(s):

Disease Transmission ◽

Large Scale ◽

Transmission Rate ◽

Control Strategies ◽

Herd Immunity ◽

Antibody Prevalence ◽

Screening Process ◽

Vaccination Programs ◽

Prevalence Level ◽

The Impact

Abstract Background The COVID-19 outbreak in Wuhan started in December 2019 and was under control by the end of March 2020 with a total of 50,006 confirmed cases by the implementation of a series of nonpharmaceutical interventions (NPIs) including unprecedented lockdown of the city. This study analyzes the complete outbreak data from Wuhan, assesses the impact of these public health interventions, and estimates the asymptomatic, undetected and total cases for the COVID-19 outbreak in Wuhan. Methods By taking different stages of the outbreak into account, we developed a time-dependent compartmental model to describe the dynamics of disease transmission and case detection and reporting. Model coefficients were parameterized by using the reported cases and following key events and escalated control strategies. Then the model was used to calibrate the complete outbreak data by using the Monte Carlo Markov Chain (MCMC) method. Finally we used the model to estimate asymptomatic and undetected cases and approximate the overall antibody prevalence level. Results We found that the transmission rate between Jan 24 and Feb 1, 2020, was twice as large as that before the lockdown on Jan 23 and 67.6% (95% CI [0.584,0.759]) of detectable infections occurred during this period. Based on the reported estimates that around 20% of infections were asymptomatic and their transmission ability was about 70% of symptomatic ones, we estimated that there were about 14,448 asymptomatic and undetected cases (95% CI [12,364,23,254]), which yields an estimate of a total of 64,454 infected cases (95% CI [62,370,73,260]), and the overall antibody prevalence level in the population of Wuhan was 0.745% (95% CI [0.693%,0.814%]) by March 31, 2020. Conclusions We conclude that the control of the COVID-19 outbreak in Wuhan was achieved via the enforcement of a combination of multiple NPIs: the lockdown on Jan 23, the stay-at-home order on Feb 2, the massive isolation of all symptomatic individuals via newly constructed special shelter hospitals on Feb 6, and the large scale screening process on Feb 18. Our results indicate that the population in Wuhan is far away from establishing herd immunity and provide insights for other affected countries and regions in designing control strategies and planing vaccination programs.

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The Impact of COVID-19 School Closure on Child and Adolescent Health: A Rapid Systematic Review

Children ◽

10.3390/children8050415 ◽

2021 ◽

Vol 8 (5) ◽

pp. 415

Author(s):

Sonia Chaabane ◽

Sathyanarayanan Doraiswamy ◽

Karima Chaabna ◽

Ravinder Mamtani ◽

Sohaila Cheema

Keyword(s):

Systematic Review ◽

Children And Adolescents ◽

Adolescent Health ◽

Children With Disabilities ◽

Child And Adolescent ◽

Emergency Department Visits ◽

School Closure ◽

School Closures ◽

Child And Adolescent Health ◽

The Impact

School closures during pandemics raise important concerns for children and adolescents. Our aim is synthesizing available data on the impact of school closure during the coronavirus disease 2019 (COVID-19) pandemic on child and adolescent health globally. We conducted a rapid systematic review by searching PubMed, Embase, and Google Scholar for any study published between January and September 2020. We included a total of ten primary studies. COVID-19-related school closure was associated with a significant decline in the number of hospital admissions and pediatric emergency department visits. However, a number of children and adolescents lost access to school-based healthcare services, special services for children with disabilities, and nutrition programs. A greater risk of widening educational disparities due to lack of support and resources for remote learning were also reported among poorer families and children with disabilities. School closure also contributed to increased anxiety and loneliness in young people and child stress, sadness, frustration, indiscipline, and hyperactivity. The longer the duration of school closure and reduction of daily physical activity, the higher was the predicted increase of Body Mass Index and childhood obesity prevalence. There is a need to identify children and adolescents at higher risk of learning and mental health impairments and support them during school closures.

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How did Autistic Children, and their Parents, Experience School Transition during the COVID-19 Pandemic?

10.31234/osf.io/8kzsn ◽

2021 ◽

Author(s):

Aimee Code ◽

Umar Toseeb ◽

Kathryn Asbury ◽

Laura Fox

Keyword(s):

School Transition ◽

School Closure ◽

School Transitions ◽

Autistic Children ◽

Structured Interviews ◽

School Closures ◽

Social Distancing ◽

New School ◽

The Uk ◽

The Impact

Due to the COVID-19 pandemic and resultant school closures, social distancing measures, and restrictions placed on routine activities, the start of the academic year in September 2020 was a unique time for those transitioning to a new school. This study aimed to explore the experiences of parents who supported autistic children making a school transition in 2020, and to examine what impact parents perceived the COVID-19 pandemic had on their child’s school transition. Emphasis was placed on identifying facilitating factors that had benefitted school transitions, and barriers, which had negatively impacted these experiences. Semi-structured interviews were carried out with 13 parents of autistic children in the UK. Reflexive thematic analysis was carried out to identify themes in interview data. Parents reported a variety of experiences, and factors that were perceived as facilitatory to some were observed to be barriers by others. For some parents, the COVID-19 pandemic negatively impacted aspects of school transitions. For example, school closure in March 2020, being unable to visit their child’s new school, and social distancing measures were discussed as being barriers to an easy transition. However, other parents identified these factors as being facilitatory for their child or reported that these circumstances created opportunities to approach the school transition in a unique, improved manner. This paper sheds light on the heterogeneity of experiences and perceptions of parents of autistic children, and highlights the need to examine the impact of COVID-19 on school transitions, including practices which may be advantageous to retain.

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Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China

Science ◽

10.1126/science.abb8001 ◽

2020 ◽

Vol 368 (6498) ◽

pp. 1481-1486 ◽

Cited By ~ 202

Author(s):

Juanjuan Zhang ◽

Maria Litvinova ◽

Yuxia Liang ◽

Yan Wang ◽

Wei Wang ◽

...

Keyword(s):

Confidence Interval ◽

Odds Ratio ◽

School Closure ◽

Hunan Province ◽

Contact Tracing ◽

Transmission Model ◽

Social Distancing ◽

Susceptibility To Infection ◽

Contact Patterns ◽

The Impact

Intense nonpharmaceutical interventions were put in place in China to stop transmission of the novel coronavirus disease 2019 (COVID-19). As transmission intensifies in other countries, the interplay between age, contact patterns, social distancing, susceptibility to infection, and COVID-19 dynamics remains unclear. To answer these questions, we analyze contact survey data for Wuhan and Shanghai before and during the outbreak and contact-tracing information from Hunan province. Daily contacts were reduced seven- to eightfold during the COVID-19 social distancing period, with most interactions restricted to the household. We find that children 0 to 14 years of age are less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than adults 15 to 64 years of age (odds ratio 0.34, 95% confidence interval 0.24 to 0.49), whereas individuals more than 65 years of age are more susceptible to infection (odds ratio 1.47, 95% confidence interval 1.12 to 1.92). Based on these data, we built a transmission model to study the impact of social distancing and school closure on transmission. We find that social distancing alone, as implemented in China during the outbreak, is sufficient to control COVID-19. Although proactive school closures cannot interrupt transmission on their own, they can reduce peak incidence by 40 to 60% and delay the epidemic.

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Epidemiological trend of COVID-19 in Nepal and the importance of social distancing to contain the virus

Applied Science and Technology Annals ◽

10.3126/asta.v1i1.30268 ◽

2020 ◽

Vol 1 (1) ◽

pp. 15-25

Author(s):

Amod K. Pokhrel ◽

Yadav P. Joshi ◽

Sopnil Bhattarai

Keyword(s):

Mitigation Measures ◽

Contact Tracing ◽

Limited Information ◽

Social Distancing ◽

Mobility Data ◽

Epidemiological Trend ◽

Reproduction Numbers ◽

The Government ◽

Testing Case ◽

The Impact

There is limited information on the epidemiology and the effects of mitigation measures on the spread of COVID-19 in Nepal. Using publicly available databases, we analyzed the epidemiological trend, the people's movement trends at different intervals across different categories of places and evaluated implications of social mobility on COVID-19. We also estimated the epidemic peak. As of June 9, 2020, Provinces 2 and 5 have most of the cases. People between 15 and 54 years are vulnerable to becoming infected, and more males than females are affected. The cases are growing exponentially. The growth rate of 0.13 and >1 reproduction numbers (R0) over time (median: 1.48; minimum: 0.58, and maximum: 3.71) confirms this trend. The case doubling time is five days. Google's community mobility data suggest that people strictly followed social distancing measures for one month after the lockdown. By around the 4th week of April, the individual's movement started rising, and social contacts increased. The number of cases peaked on May 12, with 83 confirmed cases in one day. The Susceptible-Exposed-Infectious-Removed (SEIR) model suggests that the epidemic will peak approximately on day 41 (July 21, 2020), and start to plateau after day 80. To contain the spread of the virus, people should maintain social distancing. The Government needs to continue active surveillance, more PCR-based testing, case detection, contact tracing, isolation, and quarantine. The Government should also provide financial support and safety-nets to the citizen to limit the impact of COVID-19.

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Reactive school closure weakens the network of social interactions and reduces the spread of influenza

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1821298116 ◽

2019 ◽

Vol 116 (27) ◽

pp. 13174-13181 ◽

Cited By ~ 37

Author(s):

Maria Litvinova ◽

Quan-Hui Liu ◽

Evgeny S. Kulikov ◽

Marco Ajelli

Keyword(s):

Social Interactions ◽

Influenza Season ◽

School Closure ◽

Transmission Model ◽

Modeling Framework ◽

School Closures ◽

Student Absenteeism ◽

Contact Survey ◽

Closure Policy ◽

The Impact

School-closure policies are considered one of the most promising nonpharmaceutical interventions for mitigating seasonal and pandemic influenza. However, their effectiveness is still debated, primarily due to the lack of empirical evidence about the behavior of the population during the implementation of the policy. Over the course of the 2015 to 2016 influenza season in Russia, we performed a diary-based contact survey to estimate the patterns of social interactions before and during the implementation of reactive school-closure strategies. We develop an innovative hybrid survey-modeling framework to estimate the time-varying network of human social interactions. By integrating this network with an infection transmission model, we reduce the uncertainty surrounding the impact of school-closure policies in mitigating the spread of influenza. When the school-closure policy is in place, we measure a significant reduction in the number of contacts made by students (14.2 vs. 6.5 contacts per day) and workers (11.2 vs. 8.7 contacts per day). This reduction is not offset by the measured increase in the number of contacts between students and nonhousehold relatives. Model simulations suggest that gradual reactive school-closure policies based on monitoring student absenteeism rates are capable of mitigating influenza spread. We estimate that without the implemented reactive strategies the attack rate of the 2015 to 2016 influenza season would have been 33% larger. Our study sheds light on the social mixing patterns of the population during the implementation of reactive school closures and provides key instruments for future cost-effectiveness analyses of school-closure policies.

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Closed, Bused, Mixed: A Literature Review on the Impact of Rural School Closure; Effects of Busing on Rural Students; and Student Performance in Small Schools and Mixed-Age Classes

Rural Review: Ontario Rural Planning, Development, and Policy ◽

10.21083/ruralreview.v2i1.6074 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Tatiana Zakharova

Keyword(s):

Literature Review ◽

Student Performance ◽

Rural Schools ◽

Large Scale ◽

Small Schools ◽

Rural School ◽

School Closure ◽

Rural Students ◽

School Closures ◽

The Impact

In 2000, Lauzon and Leahy completed a literature review on rural schools and educational reform, concluding that rural schools were indeed worth saving. In 2017, I conducted a literature review with the goal of offering an update to that article, investigating the post-2001 research on the impact of rural school closures; the effects of bussing of rural students to/from school; and student performance in small schools and mixed-grade classes. The results were mixed and contradictory, equal in their puzzling to the complexity of defining what is “rural” and what is “small school”. While some of the researchers continue to point out the unique place of local schools in rural settings, many also note the lack of large-scale studies into the impact of rural school closures, especially the impact on students – even "who pays and the price they pay, is always of interest" (bell hooks)… or is it?

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Clumpiness: Modeling the Impact of Social Dynamics on COVID-19 Spread

10.1101/2021.04.12.21255149 ◽

2021 ◽

Author(s):

Ben Goertzel ◽

Cassio Pennachin ◽

Deborah Duong ◽

Matthew Iklé ◽

Michael Duncan ◽

...

Keyword(s):

Social Networks ◽

Disease Transmission ◽

Social Dynamics ◽

Demographic Data ◽

Herd Immunity ◽

Control Policy ◽

Policy Formation ◽

Packet Transmission ◽

Physical Proximity ◽

The Impact

We present an agent based simulation supplemented with two novel social network interconnectivity measures, `clumpiness' and `hoprank,' that are the same concept defined at global and local levels, respectively. The measures may be computed from samples of readily available demographic data, and are useful for measuring probabilistic packet transmission through social networks. For simplicity, agents in our simulation group together through homophily, the principle of `like attracts like'. In three studies we apply clumpiness to measure the effects, on disease transmission, caused by social networks of both homophilic physical proximity and homophilic information replication. The particular characteristic we are interested in about disease transmission is herd immunity, the percentage of a population that has to be immune in order to prevent infection from spreading to those who are not. Two studies demonstrate innovations measuring herd immunity levels and predicting future outbreak locations, procedures relevant to epidemiological control policy. In the first study, we look at how homophilic physical proximity networks form natural bubbles that act as frictive surfaces that affect the speed of transmission of packets and influence herd immunity levels. In the second study, we test clumpiness in homophilic proximity social networks as a predictor of future infection outbreaks at the level of individual schools, restaurants, and workplaces. Our third study demonstrates that protective social bubbles form naturally from homophilic information replication networks, and enhance the natural bubbles that come from the homophilic physical proximity networks. Accurate description of this information environment lays the foundation for epidemiological messaging policy formation.

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