Using Google Trend data in forecasting number of dengue fever cases with ARIMAX method case study: Surabaya, Indonesia

Wiwik Anggraeni, Laras Aristiani
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引用次数: 39

Abstract

Indonesia has the highest number of dengue fever cases in Southeast Asia. Early detection of the disease is required in order to be able to prepare preventive measures against dengue fever. Previous research has shown that certain query search related to communicable disease on Google Trends are highly correlated with number of communicable disease cases in South Korea. Based on previous research, Google Trends search index shows potential to be included as external variable in a multivariate quantitative forecasting model. Using time series model, the role of Google Trends on epidemiology of dengue fever transmissions in Surabaya will be analyzed. This research uses several data (1) Number of dengue fever cases obtained from general local hospital of Dr. Soetomo (2) Google Trends search index of certain queries related to dengue fever. All of the data spans from December 2010 – August 2015. Interpolation and extrapolation techniques are used to handle the missing data. ARIMA and ARIMAX model with Google Trends data are implemented in order to forecast the number of dengue fever cases. The research shows that the addition of Google Trends into ARIMAX model improves forecasting performance. The best ARIMAX with Google Trends model improves MAPE value by 3%.
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用ARIMAX方法预测登革热病例数的Google趋势数据案例研究:印度尼西亚泗水
印度尼西亚是东南亚登革热病例最多的国家。必须及早发现这种疾病,以便能够制定预防登革热的措施。此前的研究表明,在谷歌趋势上,与传染病相关的某些查询搜索与韩国的传染病病例数高度相关。基于以往的研究,Google Trends搜索指数显示出作为外部变量纳入多元定量预测模型的潜力。利用时间序列模型,分析Google Trends在泗水登革热流行病学传播中的作用。本研究使用了几个数据(1)从Soetomo博士所在的当地综合医院获得的登革热病例数(2)与登革热相关的某些查询的Google Trends搜索索引。所有的数据跨度从2010年12月到2015年8月。插值和外推技术用于处理缺失数据。采用ARIMA和ARIMAX模型,结合Google Trends数据预测登革热病例数。研究表明,在ARIMAX模型中加入Google趋势可以提高预测性能。使用Google趋势模型的最佳ARIMAX将MAPE值提高了3%。
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