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An analysis of the waning effect of COVID-19 vaccinations
Bogyeom Lee,Hanbyul Song,Catherine Apio,Kyulhee Han,Jiwon Park,Zhe Liu,Hu Xuwen,Taesung Park Korea Genome Organization 2023 Genomics & informatics Vol.21 No.4
Vaccine development is one of the key efforts to control the spread of coronavirus disease 2019 (COVID-19). However, it has become apparent that the immunity acquired through vaccination is not permanent, known as the waning effect. Therefore, monitoring the proportion of the population with immunity is essential to improve the forecasting of future waves of the pandemic. Despite this, the impact of the waning effect on forecasting accuracies has not been extensively studied. We proposed a method for the estimation of the effective immunity (EI) rate which represents the waning effect by integrating the second and booster doses of COVID-19 vaccines. The EI rate, with different periods to the onset of the waning effect, was incorporated into three statistical models and two machine learning models. Stringency Index, omicron variant BA.5 rate (BA.5 rate), booster shot rate (BSR), and the EI rate were used as covariates and the best covariate combination was selected using prediction error. Among the prediction results, Generalized Additive Model showed the best improvement (decreasing 86% test error) with the EI rate. Furthermore, we confirmed that South Korea's decision to recommend booster shots after 90 days is reasonable since the waning effect onsets 90 days after the last dose of vaccine which improves the prediction of confirmed cases and deaths. Substituting BSR with EI rate in statistical models not only results in better predictions but also makes it possible to forecast a potential wave and help the local community react proactively to a rapid increase in confirmed cases.
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Sabin Lee,Bogyeom Park,Daejung Kim,Kyoungwon Seo 한국HCI학회 2024 한국HCI학회 학술대회 Vol.2024 No.1
Heritage trees are highly valued and protected by national laws because of their cultural and historical significance. However, due to the financial and time constraints associated with the continuous monitoring of the heritage trees, unnecessary losses are reported annually. As a solution, this study suggests an artificial intelligence(AI)-based heritage tree disease diagnosis system on Zelkova serrata. We have compared several state-of-the-art deep learning models with transfer learning on the Zelkova Serrata Dataset which consists of 680 images. All models achieved outstanding classification results even only using pre-trained weights of ImageNet, with F1 scores ranging from 92.00% to 96.26%. Particularly when additionally leveraging the plant disease datasets, model performances improved to a range of 93.78% to 99.45%. Through this research, we proposed the concept of AI-based heritage tree disease diagnosis using transfer learning. This system is expected to reduce the aforementioned financial and time constraints.
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