基于lstm的巴基斯坦COVID-19疫苗预测模型

Saba Bashir, Kinza Rohail, Rizwan Qureshi
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引用次数: 0

摘要

COVID-9几乎感染了地球上的每个国家。因此,可以降低我们感染和传播covid - 19病毒风险的疫苗已经开发出来。因此,各国政府必须确定需要多长时间才能为其所有人口接种疫苗。在这项研究中,我们建立了一个基于lstm的预测模型来预测巴基斯坦和印度的疫苗接种覆盖率。该数据集包含更新至2022年1月的疫苗记录。为了测量损失,我们使用了平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、均方误差(MSE)和均方根误差(RMSE)。该模型在训练和测试数据集上表现良好。这种模式可以帮助政府开展疫苗接种运动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LSTM-based Model for Forecasting of COVID-19 Vaccines in Pakistan
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.
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