Can Google Trends data improve forecasting of Lyme disease incidence?
Can Google Trends data improve forecasting of Lyme disease incidence?
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DOI：
10.1111/zph.12539
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发表时间：
2019-02-01
影响因子：
2.4
通讯作者：
Sulyok, Mihaly
中科院分区：
文献类型：
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作者：
Kapitany-Foveny, Mate;Ferenci, Tamas;Sulyok, Mihaly
背景基于活动的在线流行病学监测和预测正受到越来越多的关注。到目前为止，还没有对谷歌搜索量进行评估，以预测壁虱传播的疾病。因此，我们基于谷歌趋势扩展的传统数据对莱姆病的发病率进行了预测分析。方法从罗伯特·科赫研究所的数据库中获取2013年6月16日至2018年5月27日德国莱姆病每周发病率的数据。互联网搜索的数据来自Google Trends搜索德国过去5年的时间跨度类别。数据分为训练(2013年6月16日至2017年6月11日)和验证(2017年6月12日至2018年5月27日)数据集。采用季节自回归滑动平均模型SARIMA(0，1，1)(0，1，1)[52]描述莱姆病周发病率的时间序列。在此之后，我们添加了Google Trends数据作为外部回归变量，并确定SARIMA(0，1，1)(0，1，1)[52]模型为最优模型。我们使用这两个模型对验证间隔进行了预测，并将预测结果与验证数据集的值进行了比较。结果对验证时间跨度的预测导致了模型的相似值。将预测值与报道值进行比较，得到的残差均方误差为0.3763；对于没有谷歌搜索的模型，平均绝对百分比误差为8.233，RMSE为0.3732；对于Google Trends Value-Expansion模型，MAPE为8.17495。结论Google Trends数据与德国莱姆病报告发病率有很好的相关性，但未能显著提高基于传统数据的模型的预测精度。
BackgroundOnline activity-based epidemiological surveillance and forecasting is getting more and more attention. To date, Google search volumes have not been assessed for forecasting of tick-borne diseases. Thus, we performed an analysis of forecasting of the Lyme disease incidence based on the traditional data extended with Google Trends.MethodsData on the weekly incidence of Lyme disease in Germany from 16 June 2013 to 27 May 2018 were obtained from the database of the Robert Koch Institute. Data of Internet searches were obtained from Google Trends searching Borreliose in Germany for the last 5years as a timespan category. Data were split into the training (from 16 June 2013 to 11 June 2017) and validation (from 12 June 2017, to 27 May 2018) data sets. A seasonal autoregressive moving average model, SARIMA (0,1,1) (0,1,1) [52] model was selected to describe the time series of the weekly Lyme incidence. After this, we added the Google Trends data as an external regressor and identified the SARIMA (0,1,1) (0,1,1) [52] model as optimal. We made predictions for the validation interval using these two models and compared predictions with the values of the validation data set.ResultsForecasting for the validation timespan resulted in similar values for the models. Comparing the forecasted values with the reported ones resulted in an residual mean squared error (RMSE) of 0.3763; the mean absolute percentage error (MAPE) was 8.233 for the model without Google searches with an RMSE of 0.3732; and the MAPE was 8.17495 for the Google Trends values-expanded model. The difference between the predictive performances was insignificant (Diebold-Mariano Test, p-value=0.4152).ConclusionGoogle Trends data are a good correlate of the reported incidence of Lyme disease in Germany, but it failed to significantly improve the forecasting accuracy in models based on traditional data.