基于新冠肺炎预测模型选择及防疫能力评价的研究与分析

Research and Analysis on the Selection of COVID-19 Prediction Models and Evaluation of Epidemic Prevention Capability

随着2020年新冠肺炎疫情在全球范围内的爆发,全球面临了前所未有的挑战。本文旨在研究各国采取的防疫措施的有效性,并探讨有效的防疫措施对疫情控制、经济复苏和人民生命安全的重要性。本文基于新冠肺炎预测模型选择及防疫能力评价,采用多种分析方法进行研究和分析。首先,本文采用时间序列ARIMA模型对疫情进行分析预测,并利用经典的传染病动力学模型,加入潜伏患者和死亡概率,建立SEIRD模型对疫情进行预测。接着,采用多种机器学习模型与滚动预测方法相结合,提升模型的精确度,并通过平均绝对误差(MAE)度量模型的预测精度。然后,利用多变量LSTM对疫情进行预测,并基于LSTM深度学习和多种机器学习的基础上建立赋权组合模型的方法,得到更为精准的预测模型。最后,本文基于多元线性回归模型分析感染率的影响因素,考虑人口密度分布的情况下,量化评价我国各个区域的防疫能力,并从不同角度提出疫情防控工作的可行性建议,形成精细的防疫模式。本文研究结果表明,有效的防疫措施可以有效控制疫情的传播,减少患者数量和死亡率,并为经济复苏提供了条件。此外,基于多种分析方法和模型选择,本文提出了更为有效的疫情预测模型和防疫能力评价模型,为应对未来的传染病疫情提供了重要的参考依据。

The outbreak of COVID-19 in 2020 has posed unprecedented challenges to the world on a global scale. This article aims to investigate the effectiveness of epidemic prevention measures taken by different countries, and explore the importance of effective epidemic prevention measures in con-trolling the epidemic, promoting economic recovery, and ensuring people’s safety. This article con-ducts research and analysis through various analytical methods based on the selection of COVID-19 forecasting models and the evaluation of epidemic prevention capabilities. Firstly, this article uti-lizes a time series ARIMA model to analyze and predict the epidemic, and incorporates latent pa-tients and mortality rates into the classical infectious disease dynamics model to establish the SEIRD model for epidemic forecasting. Furthermore, a combination of various machine learning models and rolling forecasting methods is used to improve the accuracy of the model, and the Mean Absolute Error (MAE) is used to measure the predictive accuracy of the model. Subsequently, the epidemic is predicted using multivariate LSTM, and a weighted combination model is established based on LSTM deep learning and various machine learning methods to obtain a more accurate predictive model. Finally, this paper analyzes the influencing factors of infection rate based on the multiple linear regression model, quantitatively evaluates the epidemic prevention capabilities of various regions in China considering population density distribution, and proposes feasible sugges-tions for epidemic prevention and control from different perspectives, forming a refined epidemic prevention mode. The research results of this paper indicate that effective epidemic prevention measures can effectively control the spread of the epidemic, reduce the number of patients and mortality, and provide conditions for economic recovery. In addition, based on multiple analysis methods and model selection, this paper proposes more effective epidemic prediction models and epidemic prevention capacity evaluation models, providing important reference for coping with fu-ture infectious disease epidemics.