Two applications of machine learning on COVID-19

Yeqian Liu; Xingyi Tao; Songjia Hu

doi:10.1117/12.2628779

7 March 2022 Two applications of machine learning on COVID-19

Yeqian Liu, Xingyi Tao, Songjia Hu

Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 1216739 (2022) https://doi.org/10.1117/12.2628779
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China

Abstract

In December 2019, a new virus called COVID-19 broke out, and in 2020, it rapidly spread all over the world. The fast rate of the spread of the virus and high mortality have brought severe harm to the health of people and the economy of almost all countries around the world. Therefore, the virus has become the object of much researches. As the study moving on, treatment and vaccine have become the leading research directions at present. For treatment, measures should be taken to protect the most severe patients to reduce the death rate, and thus we are supposed to find patients with more serious illnesses. The decision tree and Xgboost are used to get the mathematical model about protease (an essential index in judging the severity of the disease) and realize the visualization of protease data. For vaccine, we solve the problem of predicting COVID-19 Vaccination Progress in the world in 2021 using the ARIMA model, which is obtained through the mean of time-series. Eventually, we got 10-day and 3-month vaccination forecasts.

Citation Download Citation

Yeqian Liu, Xingyi Tao, and Songjia Hu "Two applications of machine learning on COVID-19", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 1216739 (7 March 2022); https://doi.org/10.1117/12.2628779

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