LSTM-based Model for Forecasting of COVID-19 Vaccines in Pakistan

被引:1
|
作者
Bashir, Saba [1 ]
Rohail, Kinza [1 ]
Qureshi, Rizwan [2 ]
机构
[1] Natl Univ Comp & Emerging Sci, Fast Sch Comp, Karachi Campus, Karachi, Pakistan
[2] Hamad Bin Khalifa Univ, Coll Sci & Engn, Doha, Qatar
关键词
Times Series Prediction; Long Short Term Memory; Deep learning; COVID19; DIAGNOSIS;
D O I
10.1109/ICAI55435.2022.9773668
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
COVID-9 has infected nearly every country on the planet. As a result, vaccinations that can reduce our risk of contracting and spreading the COVID19 virus have been developed. As a result, each government must determine how long it will take to properly vaccinate all of its population. In this study, we built an LSTM-based prediction model to anticipate vaccination coverage in Pakistan and India. The dataset contains records of vaccine updated till January 2022. To measure the losses, we have used mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE) and Root mean squared error (RMSE). The model performs very well on training and testing datasets. This model can help government in the vaccination campaign.
引用
收藏
页码:94 / 99
页数:6
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