School Virus Infection Simulator for Customizing School Schedules During COVID-19

During the Coronavirus 2019 (the covid-19) pandemic, schools continuously strive to provide consistent education to their students. Teachers and education policymakers are seeking ways to re-open schools, as it is necessary for community and economic development. However, in light of the pandemic, schools require customized schedules that can address the health concerns and safety of the students considering classroom sizes, air conditioning equipment, classroom systems, e.g., self-contained or compartmentalized. To solve this issue, we developed the School-Virus-Infection-Simulator (SVIS) for teachers and education policymakers. SVIS simulates the spread of infection at a school considering the students' lesson schedules, classroom volume, air circulation rates in classrooms, and infectability of the students. Thus, teachers and education policymakers can simulate how their school schedules can impact current health concerns. We then 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 increasing classroom ventilation rate is effective, however, the impact is not stable compared to customizing school schedules, in addition, school schedules can differently impact the maximum number of students infected depending on whether classrooms are self-contained or compartmentalized. It was found that one of school schedules had a higher maximum number of students infected, compared to schedules with a higher percentage of face-to-face lessons. SVIS and the simulation results can help teachers and education policymakers plan school schedules appropriately in order to reduce the maximum number of students infected, while also maintaining a certain percentage of face-to-face lessons.

[1]  Syafruddin Side,et al.  Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia , 2020, Chaos, Solitons & Fractals.

[2]  Lynne Hamill,et al.  Designing and Building an Agent-Based Model , 2012 .

[3]  World Health Organization,et al.  Transmission of SARS-CoV-2: implications for infection prevention precautions , 2020 .

[4]  L. Morawska,et al.  Quantitative assessment of the risk of airborne transmission of SARS-CoV-2 infection: Prospective and retrospective applications , 2020, Environment International.

[5]  G Murphy,et al.  Airborne spread of measles in a suburban elementary school. , 1978, American journal of epidemiology.

[6]  Shaobo He,et al.  SEIR modeling of the COVID-19 and its dynamics , 2020, Nonlinear dynamics.

[7]  Erik Cuevas,et al.  An agent-based model to evaluate the COVID-19 transmission risks in facilities , 2020, Computers in Biology and Medicine.

[8]  P. V. Van Caeseele,et al.  Predicting Infectious Severe Acute Respiratory Syndrome Coronavirus 2 From Diagnostic Samples , 2020, Clinical Infectious Diseases.

[9]  Ryan P. Barbaro,et al.  Extracorporeal membrane oxygenation support in COVID-19: an international cohort study of the Extracorporeal Life Support Organization registry , 2020, The Lancet.

[10]  Methodology to Perform Clean Air Delivery Rate Type Determinations with Microbiological Aerosols , 1999 .

[11]  E. Cruz,et al.  Simulation-based evaluation of school reopening strategies during COVID-19: A case study of São Paulo, Brazil , 2021, Epidemiology and Infection.

[12]  P. Tupper,et al.  COVID-19 in schools: Mitigating classroom clusters in the context of variable transmission , 2021, PLoS Comput. Biol..

[13]  Eunok Jung,et al.  School Opening Delay Effect on Transmission Dynamics of Coronavirus Disease 2019 in Korea: Based on Mathematical Modeling and Simulation Study , 2020, Journal of Korean medical science.

[14]  P. Griffin,et al.  Assessing school-based policy actions for COVID-19: An agent-based analysis of incremental infection risk , 2021, Computers in Biology and Medicine.

[15]  Asymptomatic COVID-19 screening tests to facilitate full-time school attendance: model-based analysis of cost and impact , 2021, medRxiv.

[16]  M. Keeling,et al.  Quantifying within-school SARS-CoV-2 transmission and the impact of lateral flow testing in secondary schools in England , 2021, medRxiv.

[17]  Stephen M. Kofsky,et al.  COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society , 2021, Medical decision making : an international journal of the Society for Medical Decision Making.

[18]  F. Dutra,et al.  Airborne Contagion and Air Hygiene: An Ecological Study of Droplet Infections , 1955 .

[19]  Navid Hooshangi,et al.  Spatio-temporal simulation of the novel coronavirus (COVID-19) outbreak using the agent-based modeling approach (case study: Urmia, Iran) , 2020, Informatics in Medicine Unlocked.

[20]  W. Liang,et al.  Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions , 2020, Journal of thoracic disease.

[21]  W. C. Adams,et al.  Measurement of breathing rate and volume in routinely performed daily activities , 1993 .

[22]  Jia-ping Liu,et al.  Evidence for lack of transmission by close contact and surface touch in a restaurant outbreak of COVID-19 , 2021, Journal of Infection.

[23]  C. Brom,et al.  Rotation-based schedules in elementary schools to prevent COVID-19 spread: A simulation study , 2021, medRxiv.

[24]  Yiu Chung Lau,et al.  Temporal dynamics in viral shedding and transmissibility of COVID-19 , 2020, Nature Medicine.

[25]  Rachel Waema Mbogo,et al.  SEIR model for COVID-19 dynamics incorporating the environment and social distancing , 2020, BMC Research Notes.

[26]  Navid Ghaffarzadegan,et al.  Simulation-based what-if analysis for controlling the spread of Covid-19 in universities , 2020, PloS one.