Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

Valtchev, Svetozar Zarko; Asgary, Ali; Chen, Michael; Cronemberger, Felippe; Najafabadi, Mahdi M.; Cojocaru, M; Wu, Jianhong

doi:10.3390/electronics10141626

Cited by 8 publications

(6 citation statements)

References 86 publications

(101 reference statements)

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“…The study also showed good use of non‐Markovian models to better capture the spreading dynamics (Yang et al, 2020 ). In a school environment setting, a study proposed an artificial intelligence (AI)‐powered ABM (Valtchev et al, 2021 ) to examine the challenges anticipated for preventative testing of COVID‐19. Two studies combined machine‐learning algorithms with ABM to model the COVID‐19 transmission (Ozik et al, 2021 ) and calculated the effects of the COVID‐19 pandemic on the banking system and the real economy (Polyzos et al, 2021 ), respectively.…”

Section: Resultsmentioning

confidence: 99%

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

et al. 2022

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This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent‐based model (ABM) and discrete event simulation (DES), and their hybrids in COVID‐19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID‐19 transmission dynamics, 204 evaluated both pharmaceutical and non‐pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID‐19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID‐19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio‐economic systems involved.

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Section: Resultsmentioning

confidence: 99%

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

et al. 2022

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“…There are several advantages of using AI to fight against pathogens such as viruses and bacteria that cause a complex reaction in the host [32]. DNN can accelerate the discovery of valuable vaccines or drugs to prevent pandemics and facilitate the diagnosis of diseases [33,34].…”

Section: Discussionmentioning

confidence: 99%

Use of a Deep Learning Approach for the Sensitive Prediction of Hepatitis B Surface Antigen Levels in Inactive Carrier Patients

Kamimura

Nonaka

Mori

et al. 2022

JCM

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Deep learning is a subset of machine learning that can be employed to accurately predict biological transitions. Eliminating hepatitis B surface antigens (HBsAgs) is the final therapeutic endpoint for chronic hepatitis B. Reliable predictors of the disappearance or reduction in HBsAg levels have not been established. Accurate predictions are vital to successful treatment, and corresponding efforts are ongoing worldwide. Therefore, this study aimed to identify an optimal deep learning model to predict the changes in HBsAg levels in daily clinical practice for inactive carrier patients. We identified patients whose HBsAg levels were evaluated over 10 years. The results of routine liver biochemical function tests, including serum HBsAg levels for 1, 2, 5, and 10 years, and biometric information were obtained. Data of 90 patients were included for adaptive training. The predictive models were built based on algorithms set up by SONY Neural Network Console, and their accuracy was compared using statistical analysis. Multiple regression analysis revealed a mean absolute percentage error of 58%, and deep learning revealed a mean absolute percentage error of 15%; thus, deep learning is an accurate predictive discriminant tool. This study demonstrated the potential of deep learning algorithms to predict clinical outcomes.

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“…From the above, it can be seen that AI is effectively used in various aspects of COVID-19 countermeasures. Due to space limitations, we were unable to describe them in this review, but notable contributions have also been made through the use of distributed artificial intelligence and agent-based models [138][139][140]. We note that, from the perspective of preventing infectious diseases, it is necessary to reduce human contact and unnecessary human flow, which is why information and communication technology in social life is advancing worldwide [141][142][143].…”

Section: Discussionmentioning

confidence: 99%

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

Asada

Komatsu

Shimoyama

et al. 2021

JPM

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The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.

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Managing SARS-CoV-2 Testing in Schools with an Artificial Intelligence Model and Application Developed by Simulation Data

Cited by 8 publications

References 86 publications

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

Using simulation modelling and systems science to help contain COVID‐19: A systematic review

Use of a Deep Learning Approach for the Sensitive Prediction of Hepatitis B Surface Antigen Levels in Inactive Carrier Patients

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

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