Pollett Simon, Boscardin W John, Azziz-Baumgartner Eduardo, Tinoco Yeny O, Soto Giselle, Romero Candice, Kok Jen, Biggerstaff Matthew, Viboud Cecile, Rutherford George W
Department of Epidemiology & Biostatistics, University of California at San Francisco.
Marie Bashir Institute for Infectious Diseases & Biosecurity, University of Sydney.
Clin Infect Dis. 2017 Jan 1;64(1):34-41. doi: 10.1093/cid/ciw657. Epub 2016 Sep 26.
Latin America has a substantial burden of influenza and rising Internet access and could benefit from real-time influenza epidemic prediction web tools such as Google Flu Trends (GFT) to assist in risk communication and resource allocation during epidemics. However, there has never been a published assessment of GFT's accuracy in most Latin American countries or in any low- to middle-income country. Our aim was to evaluate GFT in Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay.
Weekly influenza-test positive proportions for the eight countries were obtained from FluNet for the period January 2011-December 2014. Concurrent weekly Google-predicted influenza activity in the same countries was abstracted from GFT. Pearson correlation coefficients between observed and Google-predicted influenza activity trends were determined for each country. Permutation tests were used to examine background seasonal correlation between FluNet and GFT by country.
There were frequent GFT prediction errors, with correlation ranging from r = -0.53 to 0.91. GFT-predicted influenza activity best correlated with FluNet data in Mexico follow by Uruguay, Argentina, Chile, Brazil, Peru, Bolivia and Paraguay. Correlation was generally highest in the more temperate countries with more regular influenza seasonality and lowest in tropical regions. A substantial amount of autocorrelation was noted, suggestive that GFT is not fully specific for influenza virus activity.
We note substantial inaccuracies with GFT-predicted influenza activity compared with FluNet throughout Latin America, particularly among tropical countries with irregular influenza seasonality. Our findings offer valuable lessons for future Internet-based biosurveillance tools.
拉丁美洲流感负担沉重，且互联网接入率不断上升，实时流感疫情预测网络工具（如谷歌流感趋势（GFT））有助于在疫情期间进行风险沟通和资源分配，拉丁美洲可能从中受益。然而，在大多数拉丁美洲国家或任何低收入和中等收入国家，从未有过关于GFT准确性的公开评估。我们的目的是在阿根廷、玻利维亚、巴西、智利、墨西哥、巴拉圭、秘鲁和乌拉圭评估GFT。
从FluNet获取2011年1月至2014年12月期间八个国家每周流感检测呈阳性的比例。从GFT中提取同一国家同期每周谷歌预测的流感活动情况。确定每个国家观察到的流感活动趋势与谷歌预测的流感活动趋势之间的皮尔逊相关系数。采用排列检验按国家检查FluNet和GFT之间的背景季节相关性。
GFT预测错误频繁，相关性范围为r = -0.53至0.91。GFT预测的流感活动与墨西哥的FluNet数据相关性最好，其次是乌拉圭、阿根廷、智利、巴西、秘鲁、玻利维亚和巴拉圭。在流感季节性更规律的温带国家，相关性通常最高，而在热带地区则最低。注意到大量自相关性，这表明GFT并非完全针对流感病毒活动。
我们注意到，与整个拉丁美洲的FluNet相比，GFT预测的流感活动存在重大不准确之处，尤其是在流感季节性不规律的热带国家。我们的研究结果为未来基于互联网的生物监测工具提供了宝贵的经验教训。
