COVID-19 Prevention Strategies for Victoria Students within Educational Facilities: An AI-Based Modelling Study

Lyu, Shiyang, Adegboye, Oyelola, Adhinugraha, Kiki, Emeto, Theophilus I., and Taniar, David (2023) COVID-19 Prevention Strategies for Victoria Students within Educational Facilities: An AI-Based Modelling Study. Healthcare, 11 (6).

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Educational institutions play a significant role in the community spread of SARS-CoV-2 in Victoria. Despite a series of social restrictions and preventive measures in educational institutions implemented by the Victorian Government, confirmed cases among people under 20 years of age accounted for more than a quarter of the total infections in the state. In this study, we investigated the risk factors associated with COVID-19 infection within Victoria educational institutions using an incremental deep learning recurrent neural network-gated recurrent unit (RNN-GRU) model. The RNN-GRU model simulation was built based on three risk dimensions: (1) school-related risk factors, (2) student-related community risk factors, and (3) general population risk factors. Our data analysis showed that COVID-19 infection cases among people aged 10–19 years were higher than those aged 0–9 years in the Victorian region in 2020–2022. Within the three dimensions, a significant association was identified between school-initiated contact tracing (0.6110), vaccination policy for students and teachers (0.6100), testing policy (0.6109), and face covering (0.6071) and prevention of COVID-19 infection in educational settings. Furthermore, the study showed that different risk factors have varying degrees of effectiveness in preventing COVID-19 infection for the 0–9 and 10–19 age groups, such as state travel control (0.2743 vs. 0.3390), international travel control (0.2757 vs. 0.3357) and school closure (0.2738 vs. 0.3323), etc. More preventive support is suggested for the younger generation, especially for the 10–19 age group

Item ID: 77996
Item Type: Article (Research - C1)
ISSN: 2227-9032
Copyright Information: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Date Deposited: 03 Apr 2023 02:51
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320211 Infectious diseases @ 30%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 40%
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 30%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 70%
20 HEALTH > 2004 Public health (excl. specific population health) > 200401 Behaviour and health @ 30%
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