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Duong KN, Nguyen DT, Kategeaw W, Liang X, Khaing W, Visnovsky LD, Veettil SK, McFarland MM, Nelson RE, Jones BE, Pavia AT, Coates E, Khader K, Love J, Vega Yon GG, Zhang Y, Willson T, Dorsan E, Toth DJ, Jones MM, Samore MH, Chaiyakunapruk N. Incorporating social determinants of health into transmission modeling of COVID-19 vaccine in the US: a scoping review. LANCET REGIONAL HEALTH. AMERICAS 2024; 35:100806. [PMID: 38948323 PMCID: PMC11214325 DOI: 10.1016/j.lana.2024.100806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 05/22/2024] [Accepted: 05/23/2024] [Indexed: 07/02/2024]
Abstract
During COVID-19 in the US, social determinants of health (SDH) have driven health disparities. However, the use of SDH in COVID-19 vaccine modeling is unclear. This review aimed to summarize the current landscape of incorporating SDH into COVID-19 vaccine transmission modeling in the US. Medline and Embase were searched up to October 2022. We included studies that used transmission modeling to assess the effects of COVID-19 vaccine strategies in the US. Studies' characteristics, factors incorporated into models, and approaches to incorporate these factors were extracted. Ninety-two studies were included. Of these, 11 studies incorporated SDH factors (alone or combined with demographic factors). Various sets of SDH factors were integrated, with occupation being the most common (8 studies), followed by geographical location (5 studies). The results show that few studies incorporate SDHs into their models, highlighting the need for research on SDH impact and approaches to incorporating SDH into modeling. Funding This research was funded by the Centers for Disease Control and Prevention (CDC).
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Affiliation(s)
- Khanh N.C. Duong
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Danielle T. Nguyen
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Warittakorn Kategeaw
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Xi Liang
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Win Khaing
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
| | - Lindsay D. Visnovsky
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Sajesh K. Veettil
- International Medical University, School of Pharmacy, Department of Pharmacy Practice, Kuala Lumpur, Malaysia
| | - Mary M. McFarland
- Spencer S. Eccles Health Sciences Library, University of Utah, Salt Lake City, UT, USA
| | - Richard E. Nelson
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Barbara E. Jones
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Pulmonary & Critical Care, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Andrew T. Pavia
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Pediatric Infectious Diseases, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Emma Coates
- Department of Mathematics & Statistics, McMaster University, Ontario, Canada
| | - Karim Khader
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Jay Love
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - George G. Vega Yon
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Yue Zhang
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Tina Willson
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Egenia Dorsan
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Damon J.A. Toth
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Makoto M. Jones
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Matthew H. Samore
- Division of Epidemiology, Department of Internal Medicine, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Nathorn Chaiyakunapruk
- Department of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA
- IDEAS Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
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Espinosa O, Mora L, Sanabria C, Ramos A, Rincón D, Bejarano V, Rodríguez J, Barrera N, Álvarez-Moreno C, Cortés J, Saavedra C, Robayo A, Franco OH. Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review. Syst Rev 2024; 13:30. [PMID: 38229123 PMCID: PMC10790449 DOI: 10.1186/s13643-023-02411-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 12/04/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19). METHODOLOGY PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542. FINDINGS In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias. INTERPRETATION The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.
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Affiliation(s)
- Oscar Espinosa
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia.
| | - Laura Mora
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Cristian Sanabria
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Antonio Ramos
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Duván Rincón
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Valeria Bejarano
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Jhonathan Rodríguez
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS) & Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Nicolás Barrera
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | | | - Jorge Cortés
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Carlos Saavedra
- Faculty of Medicine, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Adriana Robayo
- Directorate of Analytical, Economic and Actuarial Studies in Health, Instituto de Evaluación Tecnológica en Salud (IETS), Bogotá, Colombia
| | - Oscar H Franco
- University Medical Center Utrecht, Utrecht University & Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, USA
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3
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Brom C, Diviák T, Drbohlav J, Korbel V, Levínský R, Neruda R, Kadlecová G, Šlerka J, Šmíd M, Trnka J, Vidnerová P. Rotation-based schedules in elementary schools to prevent COVID-19 spread: a simulation study. Sci Rep 2023; 13:19156. [PMID: 37932281 PMCID: PMC10628146 DOI: 10.1038/s41598-023-45788-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 10/24/2023] [Indexed: 11/08/2023] Open
Abstract
Rotations of schoolchildren were considered as a non-pharmacological intervention in the COVID-19 pandemic. This study investigates the impact of different rotation and testing schedules.We built an agent-based model of interactions among pupils and teachers based on a survey in an elementary school in Prague, Czechia. This model contains 624 schoolchildren and 55 teachers and about 27 thousands social contacts in 10 layers. The layers reflect different types of contacts (classroom, cafeteria, etc.) in the survey. On this multi-graph structure we run a modified SEIR model of covid-19 infection. The parameters of the model are calibrated on data from the outbreak in the Czech Republic in spring 2020. Weekly rotations of in-class and distance learning are an effective preventative measure in schools reducing the spread of covid-19 by 75-81% . Antigen testing twice a week or PCR once a week significantly reduces infections even when using tests with a lower sensitivity. The structure of social contacts between pupils and teachers strongly influences the transmission. While the density of contact graphs for older pupils is 1.5 times higher than for younger pupils, the teachers' network is an order of magnitude denser. Teachers moreover act as bridges between groups of children, responsible for 14-18% of infections in the secondary school compared to 8-11% in the primary school. Weekly rotations with regular testing are a highly effective non-pharmacological intervention for the prevention of covid-19 spread in schools and a way to keep schools open during an epidemic.
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Affiliation(s)
- Cyril Brom
- Faculty of Mathematics and Physics, Charles University, Ke Karlovu 2027/3, 121 16, Praha 2, Czech Republic
| | - Tomáš Diviák
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00, Praha 9, Czech Republic
- Department of Criminology and Mitchell Centre for Social Network Analysis, School of Social Sciences, University of Manchester, Oxford Rd, Manchester, UK
| | - Jakub Drbohlav
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00, Praha 9, Czech Republic
| | - Václav Korbel
- CERGE-EI, Politických vězňů 7, 11121, Praha 1, Czech Republic
- PAQ Research, 28. pluku 458/7, 101 00, Praha 10, Czech Republic
| | - René Levínský
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00, Praha 9, Czech Republic
- CERGE-EI, Politických vězňů 7, 11121, Praha 1, Czech Republic
- New Media Studies, Faculty of Arts, Charles University, Na Příkopě 29, 110 00, Praha 1, Czech Republic
| | - Roman Neruda
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00, Praha 9, Czech Republic
- Institute of Computer Science, The Czech Academy of Sciences, Pod Vodárenskou věží-2, 18200, Praha 8, Czech Republic
| | - Gabriela Kadlecová
- Faculty of Mathematics and Physics, Charles University, Ke Karlovu 2027/3, 121 16, Praha 2, Czech Republic
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00, Praha 9, Czech Republic
- Institute of Computer Science, The Czech Academy of Sciences, Pod Vodárenskou věží-2, 18200, Praha 8, Czech Republic
| | - Josef Šlerka
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00, Praha 9, Czech Republic
- New Media Studies, Faculty of Arts, Charles University, Na Příkopě 29, 110 00, Praha 1, Czech Republic
| | - Martin Šmíd
- Faculty of Mathematics and Physics, Charles University, Ke Karlovu 2027/3, 121 16, Praha 2, Czech Republic
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00, Praha 9, Czech Republic
- Institute of Information Theory and Automation, The Czech Academy of Sciences, Pod Vodárenskou věží-4, 18200, Praha 8, Czech Republic
| | - Jan Trnka
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00, Praha 9, Czech Republic.
- Department of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University, Ruská 87, 100 00, Praha 10, Czech Republic.
| | - Petra Vidnerová
- Centre for Modelling of Biological and Social Processes, Na Břehu 497/15, 190 00, Praha 9, Czech Republic
- Institute of Computer Science, The Czech Academy of Sciences, Pod Vodárenskou věží-2, 18200, Praha 8, Czech Republic
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4
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Paduano S, Facchini MC, Borsari L, D’Alterio A, Iacuzio L, Greco A, Fioretti E, Creola G, Kahfian Z, Zona S, Bargellini A, Filippini T. Health surveillance for SARS-CoV-2: infection spread and vaccination coverage in the schools of Modena province, Italy. Front Public Health 2023; 11:1240315. [PMID: 37965518 PMCID: PMC10641794 DOI: 10.3389/fpubh.2023.1240315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 10/09/2023] [Indexed: 11/16/2023] Open
Abstract
Introduction In Italy, over 4.8 million individuals aged 0-19 years have been infected with SARS-CoV-2. This study aims to evaluate the spread of SARS-CoV-2 within schools in Modena province and the influence of anti-SARS-CoV-2 vaccination coverage. Methods We performed a survey in the period 1 September-15 December 2021, involving student population aged 0-19 years and related teachers screened for SARS-CoV-2 infection using nasopharyngeal swab after the detection of an index case within their class. During the study period, vaccination against SARS-CoV-2 was actively offered to all subjects aged ≥12 years. Results A total of 13,934 subjects were tested, 12,534 students and 1,400 teachers (594 classes). We identified a total of 594 and 779 index and secondary cases, respectively. We found that 9.8% of students and 10.6% of teachers were positive for SARS-CoV-2. Overall at the test time, 32.5% were vaccinated with at least one dose of anti-SARS-CoV-2 vaccine. Among secondary cases, 7.8% were vaccinated compared to 34.9% among negative tested subjects. A higher secondary attack rate was for non-vaccinated subjects rather than vaccinated ones (8.1% vs. 1.4%). Higher secondary attack rates were reported for subjects attending infant and primary school (5.9 and 9.6%, respectively). Lower secondary attack rates were for those who attended middle school (4.9%) and especially high school (1.7%). Conclusion Our results highlight the differential spread of the infection within various educational settings and that the vaccination, available in the study period for the population aged ≥12, have mitigated SARS-CoV-2 spread in high and middle schools.
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Affiliation(s)
- Stefania Paduano
- Department of Biomedical, Metabolic and Neural Sciences – Section of Public Health, University of Modena and Reggio Emilia, Modena, Italy
| | - Maria Chiara Facchini
- Department of Biomedical, Metabolic and Neural Sciences – Section of Public Health, University of Modena and Reggio Emilia, Modena, Italy
| | - Lucia Borsari
- Department of Public Health – Public Hygiene Service, Local Health Authority of Modena, Modena, Italy
| | - Alessandra D’Alterio
- Department of Biomedical, Metabolic and Neural Sciences – Section of Public Health, University of Modena and Reggio Emilia, Modena, Italy
| | - Laura Iacuzio
- Department of Public Health – Public Hygiene Service, Local Health Authority of Modena, Modena, Italy
| | - Antonella Greco
- Department of Public Health – Public Hygiene Service, Local Health Authority of Modena, Modena, Italy
| | - Elisabetta Fioretti
- Department of Public Health – Public Hygiene Service, Local Health Authority of Modena, Modena, Italy
| | - Giacomo Creola
- Department of Public Health – Public Hygiene Service, Local Health Authority of Modena, Modena, Italy
| | - Zaynalabedin Kahfian
- Department of Public Health – Public Hygiene Service, Local Health Authority of Modena, Modena, Italy
| | - Stefano Zona
- Infection Control Strategic Group, Local Health Authority of Modena, Modena, Italy
| | - Annalisa Bargellini
- Department of Biomedical, Metabolic and Neural Sciences – Section of Public Health, University of Modena and Reggio Emilia, Modena, Italy
| | - Tommaso Filippini
- Department of Biomedical, Metabolic and Neural Sciences – Section of Public Health, University of Modena and Reggio Emilia, Modena, Italy
- School of Public Health, University of California, Berkeley, Berkeley, CA, United States
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5
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Molina Grané C, Mancuso P, Vicentini M, Venturelli F, Djuric O, Manica M, Guzzetta G, Marziano V, Zardini A, d'Andrea V, Trentini F, Bisaccia E, Larosa E, Cilloni S, Cassinadri MT, Pezzotti P, Ajelli M, Rossi PG, Merler S, Poletti P. SARS-CoV-2 transmission patterns in educational settings during the Alpha wave in Reggio-Emilia, Italy. Epidemics 2023; 44:100712. [PMID: 37567090 DOI: 10.1016/j.epidem.2023.100712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 07/17/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Different monitoring and control policies have been implemented in schools to minimize the spread of SARS-CoV-2. Transmission in schools has been hard to quantify due to the large proportion of asymptomatic carriers in young individuals. We applied a Bayesian approach to reconstruct the transmission chains between 284 SARS-CoV-2 infections ascertained during 87 school outbreak investigations conducted between March and April 2021 in Italy. Under the policy of reactive quarantines, we found that 42.5% (95%CrI: 29.5-54.3%) of infections among school attendees were caused by school contacts. The mean number of secondary cases infected at school by a positive individual during in-person education was estimated to be 0.33 (95%CrI: 0.23-0.43), with marked heterogeneity across individuals. Specifically, we estimated that only 26.0% (95%CrI: 17.6-34.1%) of students and school personnel who tested positive during in-person education caused at least one secondary infection at school. Positive individuals who attended school for at least 6 days before being isolated or quarantined infected on average 0.49 (95%CrI: 0.14-0.83) secondary cases. Our findings provide quantitative insights on the contribution of school transmission to the spread of SARS-CoV-2 in young individuals. Identifying positive cases within 5 days after exposure to their infector could reduce onward transmission at school by at least 30%.
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Affiliation(s)
- Carla Molina Grané
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy; Department of Mathematics, University of Trento, Trento, Italy
| | - Pamela Mancuso
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Massimo Vicentini
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Francesco Venturelli
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Olivera Djuric
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy; Department of Biomedical, Metabolic and Neural Sciences, Centre for Environmental, Nutritional and Genetic Epidemiology (CREAGEN), Public Health Unit, University of Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Mattia Manica
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Giorgio Guzzetta
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | | | - Agnese Zardini
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Valeria d'Andrea
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Filippo Trentini
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy; Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy
| | - Eufemia Bisaccia
- Public Health Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Elisabetta Larosa
- Public Health Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Silvia Cilloni
- Public Health Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Maria Teresa Cassinadri
- Public Health Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Patrizio Pezzotti
- Department of Infectious Diseases, Istituto Superiore di Sanità, Rome, Italy
| | - Marco Ajelli
- Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA
| | - Paolo Giorgi Rossi
- Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Stefano Merler
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy
| | - Piero Poletti
- Center for Health Emergencies, Bruno Kessler Foundation, Trento, Italy.
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6
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Kiene SM, McDaniels-Davidson C, Lin CD, Rodriguez T, Chris N, Bravo R, Moore V, Snyder T, Arechiga-Romero M, Famania-Martinez L, Carbuccia J, Pinuelas-Morineau R, Oren E. At-Home Versus Onsite COVID-19 School-based Testing: A Randomized Noninferiority Trial. Pediatrics 2023; 152:e2022060352F. [PMID: 37394511 PMCID: PMC10312284 DOI: 10.1542/peds.2022-060352f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/04/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVES Equitable access to coronavirus 2019 (COVID-19) screening is important to reduce transmission and maintain in-person learning for middle school communities, particularly in disadvantaged schools. Rapid antigen testing, and at-home testing in particular, could offer substantial advantages over onsite testing from a school district's perspective, but it is unknown if engagement in at-home testing can be initiated and sustained. We hypothesized that an at-home COVID-19 school testing program would be noninferior to an onsite school COVID-19 testing program with regard to school participation rates and adherence to a weekly screening testing schedule. METHODS We enrolled 3 middle schools within a large, predominantly Latinx-serving, independent school district into a noninferiority trial from October 2021 to March 2022. Two schools were randomized to onsite and 1 school to at-home COVID-19 testing programs. All students and staff were eligible to participate. RESULTS Over the 21-week trial, at-home weekly screening testing participation rates were not inferior to onsite testing. Similarly, adherence to the weekly testing schedule was not inferior in the at-home arm. Participants in the at-home testing arm were able to test more consistently during and before returning from school breaks than those in the onsite arm. CONCLUSIONS Results support the noninferiority of at-home testing versus onsite testing both in terms of participation in testing and adherence to weekly testing. Implementation of at-home COVID-19 screening testing should be part of schools' routine COVID-19 prevention efforts nationwide; however, adequate support is essential to ensure participation and persistence in regular at-home testing.
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Affiliation(s)
- Susan M. Kiene
- Division of Epidemiology and Biostatistics, School of Public Health
| | | | - Chii-Dean Lin
- Department of Mathematics and Statistics, San Diego State University, San Diego, California
| | - Tasi Rodriguez
- Communities Fighting COVID! Returning Our Kids Back to School Safely, San Diego State University Research Foundation, San Diego, California
| | - Nicole Chris
- Communities Fighting COVID! Returning Our Kids Back to School Safely, San Diego State University Research Foundation, San Diego, California
| | - Rebecca Bravo
- Sweetwater Union High School District, Chula Vista, California
| | - Vernon Moore
- Sweetwater Union High School District, Chula Vista, California
| | | | - Marisela Arechiga-Romero
- Communities Fighting COVID! Returning Our Kids Back to School Safely, San Diego State University Research Foundation, San Diego, California
| | | | | | | | - Eyal Oren
- Division of Epidemiology and Biostatistics, School of Public Health
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7
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Goldman JL, Kalu IC, Schuster JE, Erickson T, Mast DK, Zimmerman K, Benjamin DK, Kalb LG, Gurnett C, Newland JG, Sherby M, Godambe M, Shinde N, Watterson T, Walsh T, Foxe J, Zand M, Dewhurst S, Coller R, DeMuri GP, Archuleta S, Ko LK, Inkelas M, Manuel V, Lee R, Oh H, Murugan V, Kramer J, Okihiro M, Gwynn L, Pulgaron E, McCulloh R, Broadhurst J, McDaniels-Davidson C, Kiene S, Oren E, Wu Y, Wetter DW, Stump T, Brookhart MA, Fist A, Haroz E. Building School-Academic Partnerships to Implement COVID-19 Testing in Underserved Populations. Pediatrics 2023; 152:e2022060352C. [PMID: 37394512 PMCID: PMC10312280 DOI: 10.1542/peds.2022-060352c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/04/2023] [Indexed: 07/04/2023] Open
Abstract
OBJECTIVE In April 2021, the US government made substantial investments in students' safe return to school by providing resources for school-based coronavirus disease 2019 (COVID-19) mitigation strategies, including COVID-19 diagnostic testing. However, testing uptake and access among vulnerable children and children with medical complexities remained unclear. METHODS The Rapid Acceleration of Diagnostics Underserved Populations program was established by the National Institutes of Health to implement and evaluate COVID-19 testing programs in underserved populations. Researchers partnered with schools to implement COVID-19 testing programs. The authors of this study evaluated COVID-19 testing program implementation and enrollment and sought to determine key implementation strategies. A modified Nominal Group Technique was used to survey program leads to identify and rank testing strategies to provide a consensus of high-priority strategies for infectious disease testing in schools for vulnerable children and children with medical complexities. RESULTS Among the 11 programs responding to the survey, 4 (36%) included prekindergarten and early care education, 8 (73%) worked with socioeconomically disadvantaged populations, and 4 focused on children with developmental disabilities. A total of 81 916 COVID-19 tests were performed. "Adapting testing strategies to meet the needs, preferences, and changing guidelines," "holding regular meetings with school leadership and staff," and "assessing and responding to community needs" were identified as key implementation strategies by program leads. CONCLUSIONS School-academic partnerships helped provide COVID-19 testing in vulnerable children and children with medical complexities using approaches that met the needs of these populations. Additional work is needed to develop best practices for in-school infectious disease testing in all children.
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Affiliation(s)
- Jennifer L. Goldman
- Division of Pediatric Infectious Diseases, Children’s Mercy Kansas City, Kansas City, Missouri
- Department of Pediatrics, University of Missouri-Kansas City, Kansas City, Missouri
| | - Ibukunoluwa C. Kalu
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina
| | - Jennifer E. Schuster
- Division of Pediatric Infectious Diseases, Children’s Mercy Kansas City, Kansas City, Missouri
- Department of Pediatrics, University of Missouri-Kansas City, Kansas City, Missouri
| | - Tyler Erickson
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
| | | | - Kanecia Zimmerman
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina
| | - Daniel K. Benjamin
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina
| | - Luther G. Kalb
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Neuroscience, University of Rochester School of Medicine and Dentistry, Rochester, New York
| | - Christina Gurnett
- Department of Pediatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Jason G. Newland
- Department of Pediatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Michael Sherby
- Department of Pediatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Maya Godambe
- Department of Pediatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Nidhi Shinde
- Department of Pediatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Treymayne Watterson
- Department of Pediatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - Tyler Walsh
- Department of Pediatrics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin
| | - John Foxe
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 31 Baltimore, Maryland
| | - Martin Zand
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 31 Baltimore, Maryland
| | - Stephen Dewhurst
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 31 Baltimore, Maryland
| | - Ryan Coller
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Washington
| | - Gregory P. DeMuri
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Washington
| | - Shannon Archuleta
- Department of Pediatrics, Washington University in St Louis, St Louis, Missouri
| | - Linda K. Ko
- Fred Hutchinson Cancer Research Center, Seattle, Washington
- UCLA Clinical and Translational Science Institute, Los Angeles, California
| | - Moira Inkelas
- Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, Arizona
| | - Vladimir Manuel
- Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, Phoenix, Arizona
| | | | - Hyunsung Oh
- Center for Personalized Diagnostics, ASU Biodesign Clinical Testing Laboratory, Biodesign Institute, Arizona State University, Tempe, Arizona
| | - Vel Murugan
- Division of Primary, Complex, and Adolescent Medicine, Phoenix Children’s Hospital, Phoenix, Arizona
| | | | - May Okihiro
- Department of Pediatrics, University of Miami Miller School of Medicine, Miami, Florida
| | - Lisa Gwynn
- University of Nebraska Medical Center, Omaha, Nebraska
| | | | - Russell McCulloh
- Division of Epidemiology and Biostatistics, San Diego State University School of Public Health, San Diego, California
| | - Jana Broadhurst
- Division of Epidemiology and Biostatistics, San Diego State University School of Public Health, San Diego, California
| | | | - Susan Kiene
- Department of Dermatology, University of Utah, Salt Lake City, Utah
| | - Eyal Oren
- Department of Dermatology, University of Utah, Salt Lake City, Utah
| | - Yelena Wu
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
| | - David W. Wetter
- Department of Population Health Sciences, University of Utah, Salt Lake City, Utah
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Tammy Stump
- Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah
| | | | - Alex Fist
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina
| | - Emily Haroz
- Johns Hopkins Center for Indigenous Health, Baltimore, Maryland
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8
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Kalu IC, Zimmerman KO, Goldman JL, Keener Mast D, Blakemore AM, Moorthy G, Boutzoukas AE, Campbell MM, Uthappa D, DeLaRosa J, Potts JM, Edwards LJ, Selvarangan R, Benjamin DK, Mann TK, Schuster JE. SARS-CoV-2 Screening Testing Programs for Safe In-person Learning in K-12 Schools. J Pediatric Infect Dis Soc 2023; 12:64-72. [PMID: 36412278 PMCID: PMC9969331 DOI: 10.1093/jpids/piac119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 11/21/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) screening testing is a recommended mitigation strategy for schools, although few descriptions of program implementation are available. METHODS Kindergarten through 12th grade (K-12) students and staff practicing universal masking during the delta and omicron variant waves from five schools in Durham, North Carolina and eight schools in Kansas City, Missouri participated; Durham's program was structured as a public health initiative facilitated by school staff, and Kansas City's as a research study facilitated by a research team. Tests included school-based rapid antigen or polymerase chain reaction testing, at-home rapid antigen testing, and off-site nucleic acid amplification testing. RESULTS We performed nearly 5700 screening tests on more than 1600 K-12 school students and staff members. The total cost for the Durham testing program in 5 public charter K-12 schools, each with 500-1000 students, was $246 587 and approximately 752 h per semester; cost per test was $70 and cost per positive result was $7076. The total cost for the Kansas City program in eight public K-12 schools was $292 591 and required approximately 537 h in personnel time for school-based testing; cost per test was $132 and cost per positive result was $4818. SARS-CoV-2 positivity rates were generally lower (0-16.16%) than rates in the community (2.7-36.47%) throughout all testing weeks. CONCLUSIONS AND RELEVANCE Voluntary screening testing programs in K-12 schools are costly and rarely detect asymptomatic positive persons, particularly in universally masked settings. CLINICAL TRIAL REGISTRATION NCT04831866.
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Affiliation(s)
- Ibukunoluwa C Kalu
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Kanecia O Zimmerman
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
- The ABC Science Collaborative, Durham, North Carolina, USA
| | | | - Dana Keener Mast
- Department of Pediatrics, Division of Pediatric Infectious Diseases, Children’s Mercy Kansas City, University of Missouri, Kansas City, Kansas City, Missouri, USA
| | - Ashley M Blakemore
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Ganga Moorthy
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Angelique E Boutzoukas
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Melissa M Campbell
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Diya Uthappa
- Duke University School of Medicine, Doctor of Medicine Program, Durham, North Carolina, USA
| | - Jesse DeLaRosa
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | | | - Laura J Edwards
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Rangaraj Selvarangan
- Department of Pediatrics, Division of Pediatric Infectious Diseases, Children’s Mercy Kansas City, University of Missouri, Kansas City, Kansas City, Missouri, USA
| | - Daniel K Benjamin
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina, USA
- The ABC Science Collaborative, Durham, North Carolina, USA
| | - Tara K Mann
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jennifer E Schuster
- Department of Pediatrics, Division of Pediatric Infectious Diseases, Children’s Mercy Kansas City, University of Missouri, Kansas City, Kansas City, Missouri, USA
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9
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Schuster JE, Kalu IC, Goldman JL. Modeling Cost and Outcomes of SARS-CoV-2 School Testing Programs. JAMA Pediatr 2022; 176:1050. [PMID: 35994276 DOI: 10.1001/jamapediatrics.2022.2976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- Jennifer E Schuster
- Division of Infectious Diseases, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, Missouri
| | - Ibukunoluwa C Kalu
- Division of Infectious Diseases, Department of Pediatrics, Duke University School of Medicine, Durham, North Carolina
| | - Jennifer L Goldman
- Division of Infectious Diseases, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, Missouri
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10
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Bilinski A, Ciaranello A. Modeling Cost and Outcomes of SARS-CoV-2 School Testing Programs-Reply. JAMA Pediatr 2022; 176:1050-1051. [PMID: 35994274 DOI: 10.1001/jamapediatrics.2022.2979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- Alyssa Bilinski
- Department of Health Services, Policy, and Practice, Department of Biostatistics, Brown School of Public Health, Providence, Rhode Island.,Department of Biostatistics, Brown School of Public Health, Providence, Rhode Island
| | - Andrea Ciaranello
- Medical Practice Evaluation Center, Division of Infectious Disease, Massachusetts General Hospital, Harvard Medical School, Boston
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11
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Chung E, Magedson A, Emanuels A, Luiten K, Pfau B, Truong M, Chow EJ, Hughes JP, Uyeki TM, Englund JA, Nickerson DA, Lockwood CM, Shendure J, Starita LM, Chu HY. SARS-CoV-2 Screening Testing in Schools: A Comparison of School- Vs. Home-Based Collection Methods. J Pediatric Infect Dis Soc 2022; 11:522-524. [PMID: 36082698 PMCID: PMC9494399 DOI: 10.1093/jpids/piac097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
We implemented a voluntary SARS-CoV-2 screening testing study for kindergarten-2nd grade students in a Washington School district. Weekly SARS-CoV-2 testing participation was higher for students with staff-collected nasal swabs at school than for students with parent-collected swabs at home.
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Affiliation(s)
- Erin Chung
- Corresponding Author: Erin Chung, MD, Department of Pediatrics, University of Washington, Seattle Children’s Hospital, Seattle, UW Medicine Box 358061, Chu Lab Room E691,750 Republican Street, Seattle, WA 98109, USA. E-mal:
| | | | - Anne Emanuels
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Kyle Luiten
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Brian Pfau
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, USA
| | - Melissa Truong
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, USA,Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Eric J Chow
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - James P Hughes
- Department of Biostatistics, University of Washington, Seattle, Washington, USA,Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Timothy M Uyeki
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Janet A Englund
- Department of Pediatrics, University of Washington, Seattle Children’s Hospital, Seattle, Washington, USA,Seattle Children’s Research Institute, Seattle, Washington, USA
| | - Deborah A Nickerson
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, USA,Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Christina M Lockwood
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, USA,Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | - Jay Shendure
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, USA
| | - Lea M Starita
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, Washington, USA,Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Helen Y Chu
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, USA
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12
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School Virus Infection Simulator for customizing school schedules during COVID-19. INFORMATICS IN MEDICINE UNLOCKED 2022; 33:101084. [PMID: 36120392 PMCID: PMC9468052 DOI: 10.1016/j.imu.2022.101084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 11/22/2022] Open
Abstract
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.
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