Lymelight: forecasting Lyme disease risk using web search data.,npj Digital Medicine

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Lymelight: forecasting Lyme disease risk using web search data.
npj Digital Medicine ( IF 12.4 ) Pub Date : 2020-02-04 , DOI: 10.1038/s41746-020-0222-x
Adam Sadilek ₁ , Yulin Hswen _{2,

3} , Shailesh Bavadekar ₁ , Tomer Shekel ₁ , John S Brownstein _{3,

4} , Evgeniy Gabrilovich ₁

Affiliation

Lyme disease is the most common tick-borne disease in the Northern Hemisphere. Existing estimates of Lyme disease spread are delayed a year or more. We introduce Lymelight-a new method for monitoring the incidence of Lyme disease in real-time. We use a machine-learned classifier of web search sessions to estimate the number of individuals who search for possible Lyme disease symptoms in a given geographical area for two years, 2014 and 2015. We evaluate Lymelight using the official case count data from CDC and find a 92% correlation (p < 0.001) at county level. Importantly, using web search data allows us not only to assess the incidence of the disease, but also to examine the appropriateness of treatments subsequently searched for by the users. Public health implications of our work include monitoring the spread of vector-borne diseases in a timely and scalable manner, complementing existing approaches through real-time detection, which can enable more timely interventions. Our analysis of treatment searches may also help reduce misdiagnosis of the disease.

中文翻译：

Lymelight：使用网络搜索数据预测莱姆病风险。

莱姆病是北半球最常见的蜱传疾病。目前对莱姆病传播的估计被推迟了一年或更长时间。我们推出Lymelight——一种实时监测莱姆病发病率的新方法。我们使用网络搜索会话的机器学习分类器来估计 2014 年和 2015 年两年内在给定地理区域搜索可能的莱姆病症状的人数。我们使用 CDC 的官方病例计数数据评估 Lymelight，并发现县级相关性为 92% (p < 0.001)。重要的是，使用网络搜索数据不仅可以让我们评估疾病的发病率，还可以检查用户随后搜索的治疗方法是否合适。我们的工作对公共卫生的影响包括及时、可扩展地监测媒介传播疾病的传播，通过实时检测补充现有方法，从而能够更及时地采取干预措施。我们对治疗搜索的分析也可能有助于减少对该疾病的误诊。

更新日期：2020-02-04

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