↓ Skip to main content

Comparison of crowd-sourced, electronic health records based, and traditional health-care based influenza-tracking systems at multiple spatial resolutions in the United States of America

Overview of attention for article published in BMC Infectious Diseases, August 2018
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)

Mentioned by

news
2 news outlets
twitter
36 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
35 Mendeley
Title
Comparison of crowd-sourced, electronic health records based, and traditional health-care based influenza-tracking systems at multiple spatial resolutions in the United States of America
Published in
BMC Infectious Diseases, August 2018
DOI 10.1186/s12879-018-3322-3
Pubmed ID
Authors

Kristin Baltrusaitis, John S. Brownstein, Samuel V. Scarpino, Eric Bakota, Adam W. Crawley, Giuseppe Conidi, Julia Gunn, Josh Gray, Anna Zink, Mauricio Santillana

Abstract

Influenza causes an estimated 3000 to 50,000 deaths per year in the United States of America (US). Timely and representative data can help local, state, and national public health officials monitor and respond to outbreaks of seasonal influenza. Data from cloud-based electronic health records (EHR) and crowd-sourced influenza surveillance systems have the potential to provide complementary, near real-time estimates of influenza activity. The objectives of this paper are to compare two novel influenza-tracking systems with three traditional healthcare-based influenza surveillance systems at four spatial resolutions: national, regional, state, and city, and to determine the minimum number of participants in these systems required to produce influenza activity estimates that resemble the historical trends recorded by traditional surveillance systems. We compared influenza activity estimates from five influenza surveillance systems: 1) patient visits for influenza-like illness (ILI) from the US Outpatient ILI Surveillance Network (ILINet), 2) virologic data from World Health Organization (WHO) Collaborating and National Respiratory and Enteric Virus Surveillance System (NREVSS) Laboratories, 3) Emergency Department (ED) syndromic surveillance from Boston, Massachusetts, 4) patient visits for ILI from EHR, and 5) reports of ILI from the crowd-sourced system, Flu Near You (FNY), by calculating correlations between these systems across four influenza seasons, 2012-16, at four different spatial resolutions in the US. For the crowd-sourced system, we also used a bootstrapping statistical approach to estimate the minimum number of reports necessary to produce a meaningful signal at a given spatial resolution. In general, as the spatial resolution increased, correlation values between all influenza surveillance systems decreased. Influenza-like Illness rates in geographic areas with more than 250 crowd-sourced participants or with more than 20,000 visit counts for EHR tracked government-lead estimates of influenza activity. With a sufficient number of reports, data from novel influenza surveillance systems can complement traditional healthcare-based systems at multiple spatial resolutions.

Twitter Demographics

The data shown below were collected from the profiles of 36 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 23%
Student > Ph. D. Student 8 23%
Researcher 4 11%
Student > Bachelor 2 6%
Student > Doctoral Student 2 6%
Other 4 11%
Unknown 7 20%
Readers by discipline Count As %
Computer Science 5 14%
Mathematics 4 11%
Biochemistry, Genetics and Molecular Biology 3 9%
Medicine and Dentistry 3 9%
Environmental Science 3 9%
Other 9 26%
Unknown 8 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 35. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 October 2019.
All research outputs
#501,619
of 13,726,653 outputs
Outputs from BMC Infectious Diseases
#65
of 5,115 outputs
Outputs of similar age
#18,832
of 266,931 outputs
Outputs of similar age from BMC Infectious Diseases
#1
of 1 outputs
Altmetric has tracked 13,726,653 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,115 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 266,931 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them