Abstract
Covid-19 is a global disaster that needs computing power to analyze, predict and interpret. So far, there have been several models doing the job. With a huge amount of daily data, deep learning models can be trained to achieve highly accurate forecasts but their mechanism lacks explainability. Epidemiological models, e.g. SIR, on the other hand, can provide insightful analyses, but they require appropriate parameter values, which might be complicated in certain locations.
The fourth wave of the pandemic in Ho Chi Minh City (HCMC), Vietnam in 2021, brought valuable lessons along with accurate and specific data. Hence, we introduce an explainable AI model, known as BeCaked+, to predict and analyze the pandemic situation efficiently from the collected data. BeCaked+ combined deep learning and epidemiological models enhanced by specific parameters related to the policies endorsed by the government. Such a combination makes BeCaked+ so accurate and a tool that provides information for policymakers to respond appropriately. One take a try BeCaked+ at http://www.cse.hcmut.edu.vn/BeCaked.
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Acknowledgement
This research is funded by the Ho Chi Minh City University of Technology (HCMUT), VNU-HCM, under grant number SVKSTN-2021-KH&KTMT-41. We acknowledge the support of time and facilities from HCMUT, VNU-HCM for this study. We also would like to express our sincerest gratitude to Mr. Hong Son Bui, Prof. Hung Son Nguyen, Dr. Truong-Minh Vu, Dr. Thu Anh Nguyen, Dr. Viet-Cuong Nguyen, and Mr. Minh Canh Khuu for giving us the useful and valuable actual data, servers, and advice.
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Nguyen, C. et al. (2022). BeCaked+: An Explainable AI Model to Forecast Delta-Spreading Covid-19 Situations for Ho Chi Minh City. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the ICR’22 International Conference on Innovations in Computing Research. ICR 2022. Advances in Intelligent Systems and Computing, vol 1431. Springer, Cham. https://doi.org/10.1007/978-3-031-14054-9_6
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DOI: https://doi.org/10.1007/978-3-031-14054-9_6
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