Advanced SearchSearch Tips
Comparison study of SARIMA and ARGO models for in influenza epidemics prediction
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Comparison study of SARIMA and ARGO models for in influenza epidemics prediction
Jung, Jihoon; Lee, Sangyeol;
  PDF(new window)
The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed `autoregressive model using Google (ARGO) model` (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as `Google Flu Trends (GFT)`. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.
ARGO model;big data;disease detection;Google flu trends;influenza epidemics;influenza-like illnesses activity estimation;SARIMA model;
 Cited by
Bollen, J., Mao, H. and Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2, 1-8. crossref(new window)

Chan, E. H., Sahai, V., Conrad, C. and Brownstein, J. S. (2011). Using web search query data to monitor dengue epidemics: A new model for neglected tropical disease surveillance. PLoS Neglected Tropical Diseases, 5, e1206. crossref(new window)

Cook, S., Conrad C., Fowlkes, A. L. and Mohebbi, M. H. (2011). Assessing Google flu trends performance in the United States during the 2009 influenza vrius A (H1N1) pandemic. PLoS One, 6, e23610. crossref(new window)

Ginsberg, J. Mohebbi1, M. H., Patel1, R. S., Brammer, L., Smolinski1, M. S. and Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457, 1012-1014. crossref(new window)

Hwang, S. and Oh, C. (2016). Estimation of the case fatality ratio of MERS epidemics using information on patients' severity condition. Journal of the Korean Data & Information Science Society, 27, 599-607. crossref(new window)

Labrinidis, A. and Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5, 2032-2033. crossref(new window)

Lazer, D., Kennedy, R., King, G. and Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343, 1203-1205. crossref(new window)

Lee, S. and Kim, B. (2013). Dependence structure analysis of KOSPI and NYSE based on time-varying copula models. Journal of the Korea & Information Science Society, 24, 1477-1488. crossref(new window)

Lee, S., Lee, J. and Noh, J. (2013). Maximum entropy test for infinite order autoregressive models. Journal of the Korean Data & Information Science Society, 24, 637-642. crossref(new window)

Overview of Influenza Surveillance in the United States. (2016). Retrived from

Polgreen, P. M., Chen, Y., Pennock, D. M. and Nelson, F. D. (2008). Using internet searches for influenza surveillance. Clinical Infectious Diseases, 47, 1443-1448. crossref(new window)

Preis, T., Moat, H. S. and Stanley H. E. (2013). Quantifying trading behavior in financial markets using Google trends. Scientific Reports, 3, 1684. crossref(new window)

Santillana, M., Nguyen, A. T., Dredze, M., Paul,M. J., Nsoesie, E. O. and Brownstein, J. S. (2015). Com-bining search, social media, and traditional data sources to improve influenza surveillance. PLoS Computational Biology, 11, e1004513. crossref(new window)

Santillana, M., Zhang, D. W., Althouse, B. M. and Ayers, J. W. (2014). What can digital disease detection learn from (an external revision to) Google Flu Trends?. American journal of preventive medicine, 47, 314-347. crossref(new window)

Wesolowski, A., Buckee, C. O., Bengtsson, L., Wetter, E., Lu, X. and Tatem, A. J. (2014). Commentary: Containing the Ebola outbreak{the potential and challenge of mobile network data. PLOS Currents Outbreaks, 10.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e.

Yang, S., Santillana, M. and Kou, S. C. (2015). ARGO: A model for accurate estimation of influenza epidemics using Google search data, arXiv preprint arXiv:1505.00864.