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Using internet search trends to forecast short term drug overdose deaths: A case study on Connecticut

机译：使用互联网搜索趋势预测短期药物过量死亡：康涅狄格州的案例研究

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In the United States, the opioid epidemic is a serious public health crisis which claimed over 130 lives per day in 2018, according to the CDC. While there are many efforts to design effective interventions to prevent drug related deaths, much of them are focused around better prescribing practices. A promising line of inquiry has focused on utilizing machine learning tools to predict addiction or overdose related hospital admission using prior health record information. However, these are strongly reliant on the private health record information of individuals. Here, we propose using publicly available historic death records along with publicly available internet search trends of drug related search terms to predict the number of overdose deaths in the upcoming week. Our model is able to predict both the number of, and spikes in drug overdose deaths with good accuracy compared to several baselines, demonstrating the utility of search data in forecasting overdose deaths. While we demonstrate this approach as a case study in the State of Connecticut, which collects and publishes overdose data, our findings could encourage other state governments to similarly invest in collection, publication and analysis of such data.
机译：根据CDC的数据，在美国，阿片类疫情是一项严重的公共卫生危机，宣称，2018年每天索赔130多名生命。虽然有许多努力设计有效的干预措施来防止药物相关的死亡，但其中大部分都集中在更好的处方实践。有希望的查询线专注于利用机器学习工具使用先前健康记录信息预测成瘾或过量相关的医院入学。然而，这些强烈依赖个人的私人健康记录信息。在这里，我们建议使用公开的历史死亡记录以及可公开的互联网搜索趋势的药物相关的搜索术语，以预测即将到来的一周内的过量死亡人数。与若干基线相比，我们的模型能够预测药物过量死亡的数量和尖峰以良好的准确性，展示搜索数据在预测过量死亡中的效用。虽然我们展示了这种方法，以康涅狄格州的案例研究，它收集和发布过量数据，我们的调查结果可以鼓励其他州政府同样投入收集，出版和分析这些数据。

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