Evaluation of Internet-based dengue query data: Google Dengue Trends.
Evaluation of Internet-based dengue query data: Google Dengue Trends.
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DOI：
10.1371/journal.pntd.0002713
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发表时间：
2014-02
影响因子：
3.8
通讯作者：
Brownstein JS
中科院分区：
文献类型：
--
作者：
Gluskin RT;Johansson MA;Santillana M;Brownstein JS
登革热是一种常见且日益严重的全球性问题，估计每年有7000万至1.4亿例病例。传统的、以卫生保健为基础的、政府实施的登革热监测是资源密集型的，而且速度缓慢。随着全球互联网使用的增加，出现了新颖的基于互联网的疾病监测工具。谷歌登革热趋势（GDT）使用近实时的搜索查询数据来创建登革热发病率指数，这是传统监测的线性代理。研究表明，在大的空间尺度上，GDT与多个国家的登革热发病率高度相关。本研究解决了GDT在较小的空间尺度的异质性，评估其准确性在墨西哥的国家一级，并确定与其准确性相关的因素。我们使用皮尔逊相关性来估计墨西哥国家一级和17个墨西哥州的GDT和传统登革热监测数据之间的关联。在全国范围内，GDT捕获了9个研究年内报告病例中约83%的变异性。GDT和报告病例之间的相关性因州而异，从下加利福尼亚的1%到恰帕斯州的88%不等，在登革热平均年发病率较高的州准确性更高。包括年平均最高气温、降水量及其相互作用的模型占各州之间GDT准确性变异性的81%。这个气候模型是GDT准确性的最佳指标，表明GDT在传播强度高的地区效果最好，特别是在当地气候非常适合传播的地区。互联网可访问性（平均36%）似乎并不影响GDT的准确性。虽然GDT似乎是低发病率和气候不利地区当地传播的一个不太可靠的指标，但它可能表明这些地区旅行者中的病例。确定新型监测的优势和局限性对于这些类型的数据用于公共卫生决策和预测模型至关重要。登革热是一个全球性的常见和日益严重的问题。传统监测系统的延迟限制了公共卫生机构有效识别和应对登革热疫情的能力。互联网搜索查询提供了传染病活动的近实时指标，并已证明在一些国家对监测疾病活动是有效的，但尚未在较小的地理区域进行评估。我们将墨西哥17个州的谷歌登革热趋势数据与这些州的传统监测数据进行了比较。我们发现，谷歌登革热趋势在州一级的效用是高度可变的，并取决于支持登革热病毒传播的气候条件。像谷歌登革热趋势这样的新型监测工具可以为公共卫生机构提供及时的信息，但要在当地范围内发挥作用，必须在登革热传播的当地背景下考虑这些工具。
Dengue is a common and growing problem worldwide, with an estimated 70–140 million cases per year. Traditional, healthcare-based, government-implemented dengue surveillance is resource intensive and slow. As global Internet use has increased, novel, Internet-based disease monitoring tools have emerged. Google Dengue Trends (GDT) uses near real-time search query data to create an index of dengue incidence that is a linear proxy for traditional surveillance. Studies have shown that GDT correlates highly with dengue incidence in multiple countries on a large spatial scale. This study addresses the heterogeneity of GDT at smaller spatial scales, assessing its accuracy at the state-level in Mexico and identifying factors that are associated with its accuracy. We used Pearson correlation to estimate the association between GDT and traditional dengue surveillance data for Mexico at the national level and for 17 Mexican states. Nationally, GDT captured approximately 83% of the variability in reported cases over the 9 study years. The correlation between GDT and reported cases varied from state to state, capturing anywhere from 1% of the variability in Baja California to 88% in Chiapas, with higher accuracy in states with higher dengue average annual incidence. A model including annual average maximum temperature, precipitation, and their interaction accounted for 81% of the variability in GDT accuracy between states. This climate model was the best indicator of GDT accuracy, suggesting that GDT works best in areas with intense transmission, particularly where local climate is well suited for transmission. Internet accessibility (average ∼36%) did not appear to affect GDT accuracy. While GDT seems to be a less robust indicator of local transmission in areas of low incidence and unfavorable climate, it may indicate cases among travelers in those areas. Identifying the strengths and limitations of novel surveillance is critical for these types of data to be used to make public health decisions and forecasting models. Dengue is a common and growing problem worldwide. Delays in traditional surveillance systems limit the ability of public health agencies to identify and respond to dengue outbreaks efficiently. Internet search queries provide near real-time indicators of infectious disease activity and have proven effective for monitoring disease activity in some countries, but have not been assessed on smaller geographic areas. We compared Google Dengue Trends data for 17 states in Mexico to traditional surveillance data from those states. We found that the utility of Google Dengue Trends at the state-level is highly variable and depends on climatic conditions supporting dengue virus transmission. Novel surveillance tools like Google Dengue Trends can provide timely information to public health agencies, but to be useful on a local scale, they must be considered within the local context of dengue transmissibility.
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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。
影响因子：
3.8
作者：
Chan EH;Sahai V;Conrad C;Brownstein JS
通讯作者：
Brownstein JS
埃及伊蚊超级繁殖与人类密度在登革热传播中的交互作用。
影响因子：
3.8
作者：
Padmanabha H;Durham D;Correa F;Diuk-Wasser M;Galvani A
通讯作者：
Galvani A
DOI：
10.1073/pnas.1006219107
发表时间：
2010-12-14
影响因子：
11.1
作者：
Chan, Emily H.;Brewer, Timothy F.;Brownstein, John S.
通讯作者：
Brownstein, John S.
DOI：
10.4269/ajtmh.2011.10-0521
发表时间：
2011-03
期刊：
The American journal of tropical medicine and hygiene
影响因子：
--
作者：
Beatty ME;Beutels P;Meltzer MI;Shepard DS;Hombach J;Hutubessy R;Dessis D;Coudeville L;Dervaux B;Wichmann O;Margolis HS;Kuritsky JN
通讯作者：
Kuritsky JN
DOI：
10.4269/ajtmh.2012.11-0597
发表时间：
2012-01-01
影响因子：
3.3
作者：
Chunara, Rumi;Andrews, Jason R.;Brownstein, John S.
通讯作者：
Brownstein, John S.