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Influenza forecasting with Google Flu Trends.
PLoS One. 2013; 8(2):e56176.Plos

Abstract

BACKGROUND

We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy.

METHODS

Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information.

RESULTS

A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets.

CONCLUSIONS

Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases.

Authors+Show Affiliations

Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America. adugas1@jhmi.eduNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

23457520

Citation

Dugas, Andrea Freyer, et al. "Influenza Forecasting With Google Flu Trends." PloS One, vol. 8, no. 2, 2013, pp. e56176.
Dugas AF, Jalalpour M, Gel Y, et al. Influenza forecasting with Google Flu Trends. PLoS ONE. 2013;8(2):e56176.
Dugas, A. F., Jalalpour, M., Gel, Y., Levin, S., Torcaso, F., Igusa, T., & Rothman, R. E. (2013). Influenza forecasting with Google Flu Trends. PloS One, 8(2), e56176. https://doi.org/10.1371/journal.pone.0056176
Dugas AF, et al. Influenza Forecasting With Google Flu Trends. PLoS ONE. 2013;8(2):e56176. PubMed PMID: 23457520.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Influenza forecasting with Google Flu Trends. AU - Dugas,Andrea Freyer, AU - Jalalpour,Mehdi, AU - Gel,Yulia, AU - Levin,Scott, AU - Torcaso,Fred, AU - Igusa,Takeru, AU - Rothman,Richard E, Y1 - 2013/02/14/ PY - 2012/10/02/received PY - 2013/01/07/accepted PY - 2013/3/5/entrez PY - 2013/3/5/pubmed PY - 2013/8/27/medline SP - e56176 EP - e56176 JF - PloS one JO - PLoS ONE VL - 8 IS - 2 N2 - BACKGROUND: We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a real-time influenza surveillance system, Google Flu Trends, and meteorological and temporal information on forecast accuracy. METHODS: Forecast models designed to predict one week in advance were developed from weekly counts of confirmed influenza cases over seven seasons (2004-2011) divided into seven training and out-of-sample verification sets. Forecasting procedures using classical Box-Jenkins, generalized linear models (GLM), and generalized linear autoregressive moving average (GARMA) methods were employed to develop the final model and assess the relative contribution of external variables such as, Google Flu Trends, meteorological data, and temporal information. RESULTS: A GARMA(3,0) forecast model with Negative Binomial distribution integrating Google Flu Trends information provided the most accurate influenza case predictions. The model, on the average, predicts weekly influenza cases during 7 out-of-sample outbreaks within 7 cases for 83% of estimates. Google Flu Trend data was the only source of external information to provide statistically significant forecast improvements over the base model in four of the seven out-of-sample verification sets. Overall, the p-value of adding this external information to the model is 0.0005. The other exogenous variables did not yield a statistically significant improvement in any of the verification sets. CONCLUSIONS: Integer-valued autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance. This accessible and flexible forecast model can be used by individual medical centers to provide advanced warning of future influenza cases. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/23457520/Influenza_forecasting_with_Google_Flu_Trends_ L2 - http://dx.plos.org/10.1371/journal.pone.0056176 DB - PRIME DP - Unbound Medicine ER -