Abstract
The goal of influenza-like illness (ILI) surveillance is to determine the timing, location and magnitude of outbreaks by monitoring the frequency and progression of clinical case incidence. Advances in computational and information technology have allowed for automated collection of higher volumes of electronic data and more timely analyses than previously possible. Novel surveillance systems, including those based on internet search query data like Google Flu Trends (GFT), are being used as surrogates for clinically-based reporting of influenza-like-illness (ILI). We investigated the reliability of GFT during the last decade (2003 to 2013), and compared weekly public health surveillance with search query data to characterize the timing and intensity of seasonal and pandemic influenza at the national (United States), regional (Mid-Atlantic) and local (New York City) levels. We identified substantial flaws in the original and updated GFT models at all three geographic scales, including completely missing the first wave of the 2009 influenza A/H1N1 pandemic, and greatly overestimating the intensity of the A/H3N2 epidemic during the 2012/2013 season. These results were obtained for both the original (2008) and the updated (2009) GFT algorithms. The performance of both models was problematic, perhaps because of changes in internet search behavior and differences in the seasonality, geographical heterogeneity and age-distribution of the epidemics between the periods of GFT model-fitting and prospective use. We conclude that GFT data may not provide reliable surveillance for seasonal or pandemic influenza and should be interpreted with caution until the algorithm can be improved and evaluated. Current internet search query data are no substitute for timely local clinical and laboratory surveillance, or national surveillance based on local data collection. New generation surveillance systems such as GFT should incorporate the use of near-real time electronic health data and computational methods for continued model-fitting and ongoing evaluation and improvement.
Highlights
Influenza remains a paradox for public health: While influenza epidemics are expected seasonally in temperate climates, their exact timing and severity remain largely unpredictable, making them a challenge to ongoing preparedness, surveillance and response efforts [1]
Engineered as a system for early detection and daily monitoring of the intensity of seasonal influenza epidemics, Google Flu Trends uses internet search data and a proprietary algorithm to provide a surrogate measure of influenza-like illness in the population
In New York City, over 780,000 influenza-like illness (ILI) and 38 million total emergency department (ED) visits were recorded in the Department of Health and Mental Hygiene (DOHMH) syndromic surveillance system, with coverage increasing from 88% of all ED visits that occurred citywide during 2003/2004 to .95% of all visits since 2008
Summary
Influenza remains a paradox for public health: While influenza epidemics are expected seasonally in temperate climates, their exact timing and severity remain largely unpredictable, making them a challenge to ongoing preparedness, surveillance and response efforts [1]. Surveillance efforts for influenza seek to determine the timing and impact of disease through characterizing information on reported illnesses, hospitalizations, deaths, and circulating influenza viruses [2]. Since establishment of the first computerized disease surveillance network nearly three decades ago [3,4,5], the use of information and communications technology for public health disease monitoring has progressed and expanded. The use of electronic syndromic surveillance systems have allowed for automated, detailed, high volume data collection and analysis in near-real time [6,7,8,9]. The public health utility of such systems for prospective monitoring and forecasting of influenza activity, remains unclear [17,18,19,20,21], as occurred during the 2009 pandemic and the 2012/2013 epidemic season [22,23,24]
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