利用多元线性回归分析 COVID-19 病毒在雅加达的传播情况

Na'il Muta'aly Muhtar, Putu Harry Gunawan
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引用次数: 0

摘要

COVID-19 于 2019 年 12 月在中国武汉首次发现,并迅速在全球范围内传播,世卫组织于 2020 年 3 月宣布其为大流行病。印度尼西亚于2020年3月2日报告了首例病例,该流行病对该国的经济、社会和卫生部门产生了重大影响。本研究旨在使用多元线性回归法预测雅加达 COVID-19 的死亡率。从 Andra Farm - Go Green 网站收集的数据集包括 2023 年 11 月 1 日雅加达所有分区记录的 COVID-19 病例。为提高模型的质量和准确性,对数据进行了预处理。使用的方法是多元线性回归。分析结果表明,总行程和废弃行程等变量对预测阳性病例数量有显著影响。研究发现,降低选择自变量的相关性阈值可减少均方误差(MSE),提高模型性能,突出了变量选择在开发精确预测模型中的重要性。这些发现为政府在大流行后的医疗保健方面做出明智决策提供了重要启示。这项研究强调了稳健的数据处理和变量选择技术在提高公共卫生规划预测准确性方面的价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of COVID-19 Virus Spread in Jakarta Using Multiple Linear Regression
COVID-19, first identified in Wuhan, China in December 2019, quickly spread worldwide and was declared a pandemic by WHO in March 2020. Indonesia reported its first case on March 2, 2020, and the pandemic has had a significant impact on the country's economic, social, and health sectors. This study aims to predict the death rate due to COVID-19 in Jakarta using multiple linear regression method. The dataset collected from Andra Farm - Go Green website includes COVID-19 cases recorded in all sub-districts in Jakarta on November 1, 2023. Pre-processing was performed to improve the quality and accuracy of the model. The method used was multiple linear regression. The analysis results show that variables such as total travel and discarded trip have a significant influence in predicting the number of positive cases. The study found that lowering the correlation threshold for selecting independent variables reduced the mean squared error (MSE) and improved model performance, highlighting the importance of variable selection in developing accurate predictive models. These findings provide important insights for the government in making informed decisions regarding post-pandemic healthcare. This research underscores the value of robust data processing and variable selection techniques in enhancing predictive accuracy for public health planning.
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