» Articles » PMID: 25115873

Using Clinicians' Search Query Data to Monitor Influenza Epidemics

Overview
Journal Clin Infect Dis
Date 2014 Aug 14
PMID 25115873
Citations 41
Authors
Affiliations
Soon will be listed here.
Abstract

Search query information from a clinician's database, UpToDate, is shown to predict influenza epidemics in the United States in a timely manner. Our results show that digital disease surveillance tools based on experts' databases may be able to provide an alternative, reliable, and stable signal for accurate predictions of influenza outbreaks.

Citing Articles

Can the number of confirmed COVID-19 cases be predicted more accurately by including lifestyle data? An exploratory study for data-driven prediction of COVID-19 cases in metropolitan cities using deep learning models.

Jung S Digit Health. 2025; 11:20552076251314528.

PMID: 39872000 PMC: 11770724. DOI: 10.1177/20552076251314528.


Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking.

Ning S, Hussain A, Wang Q PLoS One. 2024; 19(8):e0305579.

PMID: 39186560 PMC: 11346739. DOI: 10.1371/journal.pone.0305579.


Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK.

Mellor J, Overton C, Fyles M, Chawner L, Baxter J, Baird T Epidemiol Infect. 2023; 151:e172.

PMID: 37664991 PMC: 10600913. DOI: 10.1017/S0950268823001449.


Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives.

Sun H, Zhang Y, Gao G, Wu D Front Public Health. 2022; 10:958835.

PMID: 36544794 PMC: 9760721. DOI: 10.3389/fpubh.2022.958835.


Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches.

Lu F, Nguyen A, Link N, Molina M, Davis J, Chinazzi M PLoS Comput Biol. 2021; 17(6):e1008994.

PMID: 34138845 PMC: 8241061. DOI: 10.1371/journal.pcbi.1008994.


References
1.
Chan E, Sahai V, Conrad C, Brownstein J . Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Negl Trop Dis. 2011; 5(5):e1206. PMC: 3104029. DOI: 10.1371/journal.pntd.0001206. View

2.
Santillana M, Zhang D, Althouse B, Ayers J . What can digital disease detection learn from (an external revision to) Google Flu Trends?. Am J Prev Med. 2014; 47(3):341-7. DOI: 10.1016/j.amepre.2014.05.020. View

3.
Yuan Q, Nsoesie E, Lv B, Peng G, Chunara R, Brownstein J . Monitoring influenza epidemics in china with search query from baidu. PLoS One. 2013; 8(5):e64323. PMC: 3667820. DOI: 10.1371/journal.pone.0064323. View

4.
Madoff L, Fisman D, Kass-Hout T . A new approach to monitoring dengue activity. PLoS Negl Trop Dis. 2011; 5(5):e1215. PMC: 3104030. DOI: 10.1371/journal.pntd.0001215. View

5.
Cowen P, Garland T, Hugh-Jones M, Shimshony A, Handysides S, Kaye D . Evaluation of ProMED-mail as an electronic early warning system for emerging animal diseases: 1996 to 2004. J Am Vet Med Assoc. 2006; 229(7):1090-9. DOI: 10.2460/javma.229.7.1090. View