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Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in the United States in Near Real-Time
Circulating levels of both seasonal and pandemic influenza require constant surveillance to ensure the health and safety of the population. While up-to-date information is critical, traditional surveillance systems can have data availability lags of up to two weeks. We introduce a novel method of estimating, in near-real time, the level of influenza-like illness (ILI) in the United States (US) by monitoring the rate of particular Wikipedia article views on a daily basis. We calculated the number of times certain influenza- or health-related Wikipedia articles were accessed each day between December 2007 and August 2013 and compared these data to official ILI activity levels provided by the Centers for Disease Control and Prevention (CDC). We developed a Poisson model that accurately estimates the level of ILI activity in the American population, up to two weeks ahead of the CDC, with an absolute average difference between the two estimates of just 0.27% over 294 weeks of data. Wikipedia-derived ILI models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012–2013 influenza season). Wikipedia usage accurately estimated the week of peak ILI activity 17% more often than Google Flu Trends data and was often more accurate in its measure of ILI intensity. With further study, this method could potentially be implemented for continuous monitoring of ILI activity in the US and to provide support for traditional influenza surveillance tools
Global disease monitoring and forecasting with Wikipedia
Infectious disease is a leading threat to public health, economic stability,
and other key social structures. Efforts to mitigate these impacts depend on
accurate and timely monitoring to measure the risk and progress of disease.
Traditional, biologically-focused monitoring techniques are accurate but costly
and slow; in response, new techniques based on social internet data such as
social media and search queries are emerging. These efforts are promising, but
important challenges in the areas of scientific peer review, breadth of
diseases and countries, and forecasting hamper their operational usefulness.
We examine a freely available, open data source for this use: access logs
from the online encyclopedia Wikipedia. Using linear models, language as a
proxy for location, and a systematic yet simple article selection procedure, we
tested 14 location-disease combinations and demonstrate that these data
feasibly support an approach that overcomes these challenges. Specifically, our
proof-of-concept yields models with up to 0.92, forecasting value up to
the 28 days tested, and several pairs of models similar enough to suggest that
transferring models from one location to another without re-training is
feasible.
Based on these preliminary results, we close with a research agenda designed
to overcome these challenges and produce a disease monitoring and forecasting
system that is significantly more effective, robust, and globally comprehensive
than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein
and adjust novelty claims accordingly; revise title; various revisions for
clarit
Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance
We present a machine learning-based methodology capable of providing
real-time ("nowcast") and forecast estimates of influenza activity in the US by
leveraging data from multiple data sources including: Google searches, Twitter
microblogs, nearly real-time hospital visit records, and data from a
participatory surveillance system. Our main contribution consists of combining
multiple influenza-like illnesses (ILI) activity estimates, generated
independently with each data source, into a single prediction of ILI utilizing
machine learning ensemble approaches. Our methodology exploits the information
in each data source and produces accurate weekly ILI predictions for up to four
weeks ahead of the release of CDC's ILI reports. We evaluate the predictive
ability of our ensemble approach during the 2013-2014 (retrospective) and
2014-2015 (live) flu seasons for each of the four weekly time horizons. Our
ensemble approach demonstrates several advantages: (1) our ensemble method's
predictions outperform every prediction using each data source independently,
(2) our methodology can produce predictions one week ahead of GFT's real-time
estimates with comparable accuracy, and (3) our two and three week forecast
estimates have comparable accuracy to real-time predictions using an
autoregressive model. Moreover, our results show that considerable insight is
gained from incorporating disparate data streams, in the form of social media
and crowd sourced data, into influenza predictions in all time horizon
Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge
Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)
Tracking and predicting U.S. influenza activity with a real-time surveillance network
Each year in the United States, influenza causes illness in 9.2 to 35.6 million individuals and is responsible for 12,000 to 56,000 deaths. The U.S. Centers for Disease Control and Prevention (CDC) tracks influenza activity through a national surveillance network. These data are only available after a delay of 1 to 2 weeks, and thus influenza epidemiologists and transmission modelers have explored the use of other data sources to produce more timely estimates and predictions of influenza activity. We evaluated whether data collected from a national commercial network of influenza diagnostic machines could produce valid estimates of the current burden and help to predict influenza trends in the United States. Quidel Corporation provided us with de-identified influenza test results transmitted in real-time from a national network of influenza test machines called the Influenza Test System (ITS). We used this ITS dataset to estimate and predict influenza-like illness (ILI) activity in the United States over the 2015-2016 and 2016-2017 influenza seasons. First, we developed linear logistic models on national and regional geographic scales that accurately estimated two CDC influenza metrics: the proportion of influenza test results that are positive and the proportion of physician visits that are ILI-related. We then used our estimated ILI-related proportion of physician visits in transmission models to produce improved predictions of influenza trends in the United States at both the regional and national scale. These findings suggest that ITS can be leveraged to improve "nowcasts" and short-term forecasts of U.S. influenza activity
Google Trends relacionado a la influenza
The recent paper on Using Google Trends (GT) to Estimate the Incidence of Influenza-Like Illness in Argentina 1 is very interesting. Orellano et al. studied Google Flu Trends (GFT) and GT with a conclusion regarding “the utility of GT to complement influenza surveillance”. Indeed, the usefulness of GFT and GT has been mentioned in some earlier reports 2,3. However, as a computational model, there are several things to be considered in the simulation 4. Under- or over-estimation can be expected and this is still the present problem in using the Google system for predicting influenza 4. There is a need for modifications of GT and GFT into a more specific tool that is appropriate for each context. A good example of this is the development of FluBreaks by Pervaiz et al.Fil: Wiwanitkit, Viroj. Wiwanitkit House; Tailandia. Surin Rajabhat University; Tailandia. Hainan Medical College; ChinaFil: Orellano, Pablo Wenceslao. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Surin Rajabhat University; TailandiaFil: Reynoso, Julieta Itati. Wiwanitkit House; TailandiaFil: Antman, Julián. Hainan Medical College; ChinaFil: Argibay, Osvaldo. Hainan Medical College; Chin
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