Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon, Hong Kong SAR, China.
City University of Hong Kong Shenzhen Research Institute, Shenzhen, China.
BMJ Open. 2018 Oct 17;8(10):e018335. doi: 10.1136/bmjopen-2017-018335.
Internet data are important sources of abundant information regarding HIV epidemics and risk factors. A number of case studies found an association between internet searches and outbreaks of infectious diseases, including HIV. In this research, we examined the feasibility of using search query data to predict the number of new HIV diagnoses in China.
We identified a set of search queries that are associated with new HIV diagnoses in China. We developed statistical models (negative binomial generalised linear model and its Bayesian variants) to estimate the number of new HIV diagnoses by using data of search queries (Baidu) and official statistics (for the entire country and for Guangdong province) for 7 years (2010 to 2016).
Search query data were positively associated with the number of new HIV diagnoses in China and in Guangdong province. Experiments demonstrated that incorporating search query data could improve the prediction performance in nowcasting and forecasting tasks.
Baidu data can be used to predict the number of new HIV diagnoses in China up to the province level. This study demonstrates the feasibility of using search query data to predict new HIV diagnoses. Results could potentially facilitate timely evidence-based decision making and complement conventional programmes for HIV prevention.
互联网数据是有关 HIV 流行和危险因素的丰富信息的重要来源。一些案例研究发现，互联网搜索与传染病（包括 HIV）的爆发之间存在关联。在这项研究中，我们检验了使用搜索查询数据预测中国新增 HIV 诊断数量的可行性。
我们确定了一组与中国新增 HIV 诊断相关的搜索查询。我们开发了统计模型（负二项广义线性模型及其贝叶斯变体），使用 7 年（2010 年至 2016 年）的搜索查询（百度）和官方统计数据（全国和广东省）数据来估计新增 HIV 诊断数量。
搜索查询数据与中国和广东省新增 HIV 诊断数量呈正相关。实验表明，纳入搜索查询数据可以提高即时预测和预测任务的预测性能。
百度数据可用于预测中国新增 HIV 诊断数量，甚至可以预测到省级水平。本研究证明了使用搜索查询数据预测新增 HIV 诊断的可行性。研究结果有可能有助于及时做出基于证据的决策，并补充 HIV 预防的常规计划。
