Using Web and Social Media for Influenza Surveillance

Corley, Courtney D.; Cook, Diane J.; Mikler, Armin R.; Singh, Karan P.

doi:10.1007/978-1-4419-5913-3_61

Cited by 52 publications

(39 citation statements)

References 5 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, novel surveillance methods using internet search queries through other forms of social media can also provide timely estimates, and perhaps, even forecasts of influenza activity. [11–14]…”

Section: Discussionmentioning

confidence: 99%

Predicting Chronic Obstructive Pulmonary Disease Hospitalizations Based on Concurrent Influenza Activity

Gerke¹,

Tang²,

Yang³

et al. 2013

COPD: Journal of Chronic Obstructive Pulmonary Disease

View full text Add to dashboard Cite

Although influenza has been associated with chronic obstructive pulmonary disease (COPD) exacerbations, it is not clear the extent to which this association affects healthcare use in the United States. The first goal of this project was to determine to what extent the incidence of COPD hospitalizations is associated with seasonal influenza. Second, as a natural experiment, we used influenza activity to help predict COPD admissions during the 2009 H1N1 influenza pandemic. To do this, we identified all hospitalizations between 1998 and 2010 in the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project (HCUP) during which a primary diagnosis of COPD was recorded. Separately, we identified all hospitalizations during which a diagnosis of influenza was recorded. We formulated time series regression models to investigate the association of monthly COPD admissions with influenza incidence. Finally, we applied these models, fit using 1998–2008 data, to forecast monthly COPD admissions during the 2009 pandemic. Based on time series regression models, a strong, significant association exists between concurrent influenza activity and incidence of COPD hospitalizations (p-value<0.0001). The association is especially strong among older patients requiring mechanical ventilation. Use of influenza data to predict COPD admissions during the 2009 H1N1 pandemic reduced the mean-squared prediction error by 29.9%. We conclude that influenza activity is significantly associated with COPD hospitalizations in the United States and influenza activity can be exploited to more accurately forecast COPD admissions. Our results suggest that improvements in influenza surveillance, prevention, and treatment may decrease hospitalizations of patients diagnosed with COPD.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting Chronic Obstructive Pulmonary Disease Hospitalizations Based on Concurrent Influenza Activity

Gerke¹,

Tang²,

Yang³

et al. 2013

COPD: Journal of Chronic Obstructive Pulmonary Disease

View full text Add to dashboard Cite

show abstract

“…Another example is the use of Google Flu Trends for influenza (Google, 2013). Social media is also increasingly used to identify influenza-like illnesses to detect potential outbreaks before formal diagnoses are even made (Corley, 2010). The use of analytics tools allow patterns indicative of future outbreaks to be detected earlier before the situation gets out of hand, costs spiral, and lives are lost.…”

Section: Specific Examplesmentioning

confidence: 99%

Applications of business analytics in healthcare

Ward¹,

2014

View full text Add to dashboard Cite

The American healthcare system is at a crossroads, and analytics, as an organizational skill, figures to play a pivotal role in its future. As more healthcare systems capture information electronically and as they begin to collect more novel forms of data, such as human DNA, how will we leverage these resources and use them to improve human health at a manageable cost? In this article, we argue that analytics will play a fundamental role in the transformation of the American healthcare system. However, there are numerous challenges to the application and use of analytics, namely the lack of data standards, barriers to the collection of high-quality data, and a shortage of qualified personnel to conduct such analyses. There are also multiple managerial issues, such as how to get end users of electronic data to employ it consistently for improving healthcare delivery, and how to manage the public reporting and sharing of data. In this article, we explore applications of analytics in healthcare, barriers and facilitators to its widespread adoption, and how analytics can help us achieve the goals of the modern healthcare system: high-quality, responsive, affordable, and efficient care.

show abstract

“…Paul et al [2] and Signorini et al [3] traced the trends of the attack of diseases during outbreaks and analyzed the correlation between the trends and public responses. Corley et al [4] reported that there was high correlation between the prevalence of influenza that occurred in autumn 2008 in the United States and the quantity of influenzarelated personal blogs. Armaki et al [5] proposed a support vector machine-based method to classify whether a Twitter user was infected by influenza or not based on the tweets of the user.…”

Section: Related Workmentioning

confidence: 98%

Mining internet media for monitoring changes of public emotions about infectious diseases

Choi

Lee

Pack

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW)

View full text Add to dashboard Cite

The Internet encompasses websites, email, social media, and Internet-based television. Given the widespread use of networked computers and mobile devices, it has become possible to monitor the behavior of Internet users by examining their access logs and queries. Based on large-scale web and text mining of Internet media articles and associated user comments, we propose a framework to rapidly monitor how the emotion of the public changes over time and apply the framework to a real case of an infectious disease. The proposed methodology will be helpful for performing cost-effective and time-efficient public health monitoring that otherwise would take orders-of-magnitude more time and resources if traditional epidemiology techniques were used.

show abstract

Using Web and Social Media for Influenza Surveillance

Cited by 52 publications

References 5 publications

Predicting Chronic Obstructive Pulmonary Disease Hospitalizations Based on Concurrent Influenza Activity

Predicting Chronic Obstructive Pulmonary Disease Hospitalizations Based on Concurrent Influenza Activity

Applications of business analytics in healthcare

Mining internet media for monitoring changes of public emotions about infectious diseases

Contact Info

Product

Resources

About