[论文解读] Prediction of Zoonosis Incidence in Human using Seasonal Auto Regressive Integrated Moving Average (SARIMA)

Adhistya Erna Permanasari, Dayang Rohaya Awang Rambli+1 more|ArXiv.org|Oct 5, 2009

Zoonotic diseases and public health参考文献 5被引用 10

一句话总结

本研究提出了一种SARIMA模型，利用美国1990年至2003年的月度结核病数据预测人畜共患病发病率。SARIMA(9,0,14)(12,1,24)12模型的Theil's U值为0.062，表明其具有较高的预测准确性和对历史数据的良好拟合度，证明SARIMA在季节性人畜共患病预测中的有效性。

ABSTRACT

Zoonosis refers to the transmission of infectious diseases from animal to human. The increasing number of zoonosis incidence makes the great losses to lives, including humans and animals, and also the impact in social economic. It motivates development of a system that can predict the future number of zoonosis occurrences in human. This paper analyses and presents the use of Seasonal Autoregressive Integrated Moving Average (SARIMA) method for developing a forecasting model that able to support and provide prediction number of zoonosis human incidence. The dataset for model development was collected on a time series data of human tuberculosis occurrences in United States which comprises of fourteen years of monthly data obtained from a study published by Centers for Disease Control and Prevention (CDC). Several trial models of SARIMA were compared to obtain the most appropriate model. Then, diagnostic tests were used to determine model validity. The result showed that the SARIMA(9,0,14)(12,1,24)12 is the fittest model. While in the measure of accuracy, the selected model achieved 0.062 of Theils U value. It implied that the model was highly accurate and a close fit. It was also indicated the capability of final model to closely represent and made prediction based on the tuberculosis historical dataset.

研究动机与目标

• 使用时间序列分析开发预测人畜共患病发病率的预测模型。
• 评估SARIMA在建模人畜共患病数据中季节性模式方面的表现。
• 确定用于结核病发病率预测的最佳SARIMA模型配置。
• 通过诊断检验和Theil's U等准确度指标验证模型。

提出的方法

• 本研究使用美国1990年至2003年的人类结核病月度时间序列数据，数据来源为CDC。
• 应用季节性自回归积分滑动平均（SARIMA）模型以捕捉数据中的季节性和非季节性成分。
• 通过模型选择标准测试并比较多个SARIMA模型。
• 通过残差分析和准确度指标等诊断检验验证最终模型。
• 优化模型参数以最小化预测误差，最终选定SARIMA(9,0,14)(12,1,24)12配置。
• 使用Theil's U统计量评估模型准确度，数值越低表示性能越好。

实验结果

研究问题

• RQ1 SARIMA能否有效建模并预测人畜共患病发病率的季节性模式？
• RQ2 用于预测美国结核病发病率的最佳SARIMA配置是什么？
• RQ3 所选SARIMA模型在预测未来人畜共患病病例方面的准确度如何？
• RQ4 该模型是否对历史结核病数据表现出良好的拟合度？

主要发现

• 基于诊断检验和模型比较，SARIMA(9,0,14)(12,1,24)12模型被确定为最佳拟合模型。
• 该模型的Theil's U值为0.062，表明具有较高的预测准确度，并与观测数据高度吻合。
• Theil's U值较低表明该模型有效捕捉了结核病发病率时间序列中的潜在模式。
• 残差分析证实模型误差呈随机分布，支持模型的有效性。
• 该模型展示了利用历史数据对季节性人畜共患病趋势进行强预测能力。
• 结果证实SARIMA是预测人类群体中人畜共患病发病率的可行方法。