Estimating Disease Burden Using Google Trends and Wikipedia Data

Estimating Disease Burden Using Google Trends and Wikipedia Data - scientific work related to Wikipedia quality published in 2017, written by Riyi Qiu, Mirsad Hadzikadic, Lixia Yao and Lixia Yao.

Overview

Data on disease burden is often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. Authors investigated whether Internet usage data, particularly the search volume on Google and page view counts on Wikipedia, are correlated with the disease burden, measured by prevalence and treatment cost, for 1,633 diseases over an 11-year period. Authors also applied the method of least absolute shrinkage and selection operator (LASSO) to predict the burden of diseases, using those Internet data together with three other variables authors quantified previously. Authors found a relatively strong correlation for 39 of 1,633 diseases, including viral hepatitis, diabetes mellitus, other headache syndromes, multiple sclerosis, sleep apnea, hemorrhoids, and disaccharidase deficiency. However, an accurate analysis must consider each condition’s characteristics, including acute/chronic nature, severity, familiarity to the public, and presence of stigma.