Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance.
Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance.
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使用Web搜索查询数据监测登革热流行：一种被忽视的热带疾病监测的新模型。
DOI：
10.1371/journal.pntd.0001206
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发表时间：
2011-05
影响因子：
3.8
通讯作者：
Brownstein JS
中科院分区：
文献类型：
--
作者：
Chan EH;Sahai V;Conrad C;Brownstein JS
在许多流行国家，包括官僚主义和缺乏资源在内的各种障碍阻碍了登革热病例的及时发现和报告。监测工作已转向现代数据源，例如互联网搜索查询，这些数据已被证明可以有效监测流感样疾病。然而，很少有人评估网络搜索查询数据对其他疾病的效用，尤其是那些高发病率和死亡率或可能不存在疫苗的疾病。在这项研究中，我们旨在评估网络搜索查询是否是早期发现和监测登革热流行病的可行数据源。根据现有数据和足够的搜索量，选择玻利维亚、巴西、印度、印度尼西亚和新加坡进行分析。然后，针对每个国家/地区，通过将该国家/地区特定登革热相关查询的 Google 搜索查询量比例的时间序列与 2003 年至 2010 年期间官方登革热病例计数的时间序列进行拟合，建立单变量线性模型。选择所使用的特定查询组合是为了最大化模型拟合。在模型拟合之前，数据中的虚假尖峰也被去除。最终模型使用数据的训练子集进行拟合，并针对整个数据集和数据的保留子集进行交叉验证。所有模型都与数据拟合得很好，验证相关性范围为 0.82 到 0.99。网络搜索查询数据被发现能够跟踪玻利维亚、巴西、印度、印度尼西亚和新加坡的登革热活动。来自官方来源的传统登革热数据通常要经过一段长时间的延迟才能获得，而网络搜索查询数据则可以近乎实时地获得。这些数据是对传统登革热监测的宝贵补充。许多国家都存在各种障碍，包括官僚主义和缺乏资源、延迟发现和报告登革热，该疾病是该疾病的主要公共卫生威胁。监控工作已转向现代数据源，例如互联网使用数据。人们经常在网上寻找与健康相关的信息，并且已经发现，例如，流感相关网络搜索的频率总体上随着流感患者人数的增加而增加。已经开发出一些工具，通过查找某些网络搜索活动的模式来帮助跟踪流感流行。然而，很少有人评估这种方法是否也对其他疾病有效，尤其是那些影响许多人、造成严重后果或没有疫苗的疾病。在这项研究中，我们发现聚合的匿名谷歌搜索查询数据也能够跟踪玻利维亚、巴西、印度、印度尼西亚和新加坡的登革热活动。来自官方来源的传统登革热数据通常要经过很长时间的延迟才能获得，而网络搜索查询数据可以在一天内进行分析。因此，由于这些数据有可能提供早期预警，因此是对传统登革热监测的宝贵补充。
A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics. Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003–2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99. Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance. A variety of obstacles, including bureaucracy and lack of resources, delay detection and reporting of dengue and exist in many countries where the disease is a major public health threat. Surveillance efforts have turned to modern data sources such as Internet usage data. People often seek health-related information online and it has been found that the frequency of, for example, influenza-related web searches as a whole rises as the number of people sick with influenza rises. Tools have been developed to help track influenza epidemics by finding patterns in certain web search activity. However, few have evaluated whether this approach would also be effective for other diseases, especially those that affect many people, that have severe consequences, or for which there is no vaccine. In this study, we found that aggregated, anonymized Google search query data were also capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after a long delay, web search query data is available for analysis within a day. Therefore, because it could potentially provide earlier warnings, these data represent a valuable complement to traditional dengue surveillance.
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影响因子：
3.3
作者：
Oum, S;Chandramohan, D;Cairncross, S
通讯作者：
Cairncross, S
影响因子：
2.7
作者：
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影响因子：
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作者：
Yih WK;Teates KS;Abrams A;Kleinman K;Kulldorff M;Pinner R;Harmon R;Wang S;Platt R
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网络查询是综合症监测的来源。
影响因子：
3.7
作者：
Hulth A;Rydevik G;Linde A
通讯作者：
Linde A
影响因子：
4.5
作者：
Besculides M;Heffernan R;Mostashari F;Weiss D
通讯作者：
Weiss D