Abstract
Data on disease burden is often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. We 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. We 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 we quantified previously. We 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.
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Qiu, R., Hadzikadic, M., Yao, L. (2017). Estimating Disease Burden Using Google Trends and Wikipedia Data. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_39
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DOI: https://doi.org/10.1007/978-3-319-60045-1_39
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