Data Science Lab, Behavioural Science, Warwick Business School, University of Warwick, Coventry, United Kingdom.
The Alan Turing Institute, London, United Kingdom.
PLoS Negl Trop Dis. 2022 Jun 9;16(6):e0010441. doi: 10.1371/journal.pntd.0010441. eCollection 2022 Jun.
Chikungunya, a mosquito-borne disease, is a growing threat in Brazil, where over 640,000 cases have been reported since 2017. However, there are often long delays between diagnoses of chikungunya cases and their entry in the national monitoring system, leaving policymakers without the up-to-date case count statistics they need. In contrast, weekly data on Google searches for chikungunya is available with no delay. Here, we analyse whether Google search data can help improve rapid estimates of chikungunya case counts in Rio de Janeiro, Brazil. We build on a Bayesian approach suitable for data that is subject to long and varied delays, and find that including Google search data reduces both model error and uncertainty. These improvements are largest during epidemics, which are particularly important periods for policymakers. Including Google search data in chikungunya surveillance systems may therefore help policymakers respond to future epidemics more quickly.
基孔肯雅热是一种由蚊子传播的疾病，在巴西日益构成威胁，自 2017 年以来，巴西已报告超过 64 万例病例。然而，基孔肯雅热病例的诊断与进入国家监测系统之间往往存在长时间的延迟，使决策者无法获得他们所需的最新病例统计数据。相比之下，谷歌搜索基孔肯雅热的每周数据是实时的。在这里，我们分析了谷歌搜索数据是否有助于改善巴西里约热内卢基孔肯雅热病例数的快速估计。我们基于一种适合于受长时间和各种延迟影响的数据的贝叶斯方法，发现包括谷歌搜索数据可以减少模型误差和不确定性。这些改进在疫情期间最大，而疫情是决策者特别重要的时期。因此，在基孔肯雅热监测系统中纳入谷歌搜索数据可能有助于决策者更快地应对未来的疫情。
