Difference between revisions of "Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in Near Real-Time"
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| − | '''Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in Near Real-Time''' - scientific work related to Wikipedia quality published in 2015, written by David J McIver and John S. Brownstein. | + | '''Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in Near Real-Time''' - scientific work related to [[Wikipedia quality]] published in 2015, written by [[David J McIver]] and [[John S. Brownstein]]. |
== Overview == | == Overview == | ||
| − | Wikipedia usage data has been harnessed to estimate the prevalence of influenza-like illness (ILI) in the US population. By observing the number of times certain key Wikipedia articles are viewed each day, a model was developed that accurately estimated ILI, within 0.27% of official Centers for Disease Control and Prevention data. Additionally, this method was able to accurately determine the week in which ILI peaked 17% more often than Google Flu Trends. This work demonstrates the power of open, freely available data to aid in disease surveillance. | + | Wikipedia usage data has been harnessed to estimate the prevalence of influenza-like illness (ILI) in the US population. By observing the number of times certain key [[Wikipedia]] articles are viewed each day, a model was developed that accurately estimated ILI, within 0.27% of official Centers for Disease Control and Prevention data. Additionally, this method was able to accurately determine the week in which ILI peaked 17% more often than [[Google]] Flu Trends. This work demonstrates the power of open, freely available data to aid in disease surveillance. |
Revision as of 19:09, 19 October 2019
Wikipedia Usage Estimates Prevalence of Influenza-Like Illness in Near Real-Time - scientific work related to Wikipedia quality published in 2015, written by David J McIver and John S. Brownstein.
Overview
Wikipedia usage data has been harnessed to estimate the prevalence of influenza-like illness (ILI) in the US population. By observing the number of times certain key Wikipedia articles are viewed each day, a model was developed that accurately estimated ILI, within 0.27% of official Centers for Disease Control and Prevention data. Additionally, this method was able to accurately determine the week in which ILI peaked 17% more often than Google Flu Trends. This work demonstrates the power of open, freely available data to aid in disease surveillance.
