Forecasting Influenza Based on Autoregressive Moving Average and Holt-Winters Exponential Smoothing Models
Guohun Zhu*1,*2, Liping Li*3,*4, Yuebin Zheng*1, Xiaowei Zhang*3,*4, and Hui Zou*1
*1School of EEE, Guilin University of Electronic Technology
No.1 Jinji Road, Guilin, Guangxi 541004, China
*2School of ITEE, The University of Queensland
78-626 General Purpose South Building, St Lucia, Queensland 4067, Australia
*3School of Public Health, Shantou University
No.22 Xinling Road, Shantou, Guangdong 515041, China
*4Shantou University Medical College
No.22 Xinling Road, Shantou, Guangdong 515041, China
Influenza outbreaks can be effectively prevented if further outbreaks are predicted as early as possible. This article proposes an autoregressive integrated moving average (ARIMA) model and a Holt-Winters exponential smoothing (HWES) model to analyze tweet data for predicting influenza outbreaks and to visualize the number of flu-infection-related tweets with heat maps. First, textual influenza data for Australia from June 2015 to June 2017 are collected through the Twitter Application Programming Interface (API). Next, the ARIMA and HWES models are applied to predict the difference between the flu tweets and confirmations from the Centers for Disease Control and Prevention. Finally, a visualized heat map based on influenza topics validates the modeling analysis in two different time zones. The results show that the average relative error of the ARIMA (HWES) model is 7.25% (11.29%) for the one-week flu forecast.
-  I. Coalition, Influenza Activity Surveillance 2019, https://www.immunisationcoalition.org.au/news-media/2019-influenza-statistics/ [accessed June 24, 2019]
-  H. Brody, “Influenza,” Nature, Vol.573, No.7774, p. S49, 2019.
-  L. A. Grohskopf, L. Z. Sokolow, S. J. Olsen, J. S. Bresee, K. R. Broder, and R. A. Karron, “Prevention and control of influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices – United States, 2015–16 influenza season,” Morbidity and Mortality Weekly Report (MMWR), Vol.64, No.30, pp. 818, 2015.
-  S. Dhakal and S. L. Klein, “Host Factors Impact Vaccine Efficacy: Implications for Seasonal and Universal Influenza Vaccine Programs,” J. of Virology, Vol.93, No.21, pp. e00797-19, 2019.
-  S. V. Vemula, E. E. Sayedahmed, S. Sambhara, and S. K. Mittal, “Vaccine approaches conferring cross-protection against influenza viruses,” Expert Review of Vaccines, Vol.16, No.11, pp. 1141-1154, 2017.
-  V. N. Petrova and C. A. Russell, “The evolution of seasonal influenza viruses,” Nature reviews Microbiology Vol.16, No.1, pp. 47-60, 2018.
-  P. M. Polgreen, Y. Chen, D. M. Pennock, F. D. Nelson, and R. A. Weinstein, “Using internet searches for influenza surveillance,” Clinical Infectious Diseases, Vol.47, No.11, pp. 1443-1448, 2008.
-  D. Lazer, R. Kennedy, G. King, and A. Vespignani, “The parable of Google Flu: traps in big data analysis,” Science, Vol.343, No.6176, pp. 1203-1205, 2014.
-  D. Butler, “When Google got flu wrong,” Nature, Vol.494, No.7436, pp. 155-156, 2013.
-  B. M. Althouse et al., “Enhancing disease surveillance with novel data streams: challenges and opportunities,” EPJ Data Science, Vol.4, No.1, p. 17, 2015.
-  H. Woo, Y. Cho, E. Shim, J.-K. Lee, C.-G. Lee, and S. H. Kim, “Estimating influenza outbreaks using both search engine query data and social media data in South Korea,” J. of Medical Internet Research, Vol.18, No.7, p. e177, 2016.
-  G. Zuccon et al., “Automatic detection of tweets reporting cases of influenza like illnesses in Australia,” Health Information Science and Systems, Vol.3, Article No.S4, 2015.
-  Y. Wang, K. Xu, Y. Kang, H. Wang, F. Wang, and A. Avram, “Regional influenza prediction with sampling twitter data and PDE model,” Int. J. of Environmental Research and Public Health, Vol.17, No.3, pp. 678, 2020.
-  M. J. Paul, M. Dredze, and D. Broniatowski, “Twitter improves influenza forecasting,” PLoS Currents, Vol.1, No.6, 2014.
-  Y. Shu et al., “A ten-year China-US laboratory collaboration: improving response to influenza threats in China and the world, 2004–2014,” BMC Public Health, Vol.19, No.3, pp. 520, 2019.
-  Z. He and H. Tao, “Epidemiology and ARIMA model of positive-rate of influenza viruses among children in Wuhan, China: A nine-year retrospective study,” Int. J. of Infectious Diseases, Vol.74, pp. 61-70, 2018.
-  J. S. Beckmann and D. Lew, “Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities,” Genome Medicine, Vol.8, No.1, p. 134, 2016.
-  P. J. Brockwell and R. A. Davis, “Introduction to time series and forecasting,” Springer, 2002.
-  L. Liu, R. S. Luan, F. Yin, X. P. Zhu, and Q. Lü, “Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model,” Epidemiology and Infection, Vol.144, No.1, pp. 144-151, 2016.
-  J. D. Hamilton, “Time Series Analysis,” Princeton University Press, 1994.
-  D. C. Montgomery, L. A. Johnson, and J. S. Gardiner, “Forecasting and Time Series Analysis,” 2nd Edition, McGraw-Hill, 1990.
-  C. Chris and M. Yar, “Holt-Winters Forecasting: Some Practical Issues,” The Statistician, Vol.37, No.2, pp. 129-140, 1988.
-  Twitter, Twitter libraries, https://developer.twitter.com/en/docs/developer-utilities/twitter-libraries.html [accessed May 31, 2019]
-  H. Achrekar, A. Gandhe, R. Lazarus, S. H. Yu, and B. Liu. “Predicting Flu Trends Using Twitter Data,” Proc. of the IEEE Conf. on Computer Communications Workshops, pp. 702-707, 2011.
-  D. R. Olson et al., “Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales,” Plos Computational Biology, Vol.9, No.10, p. e1003256, 2013.
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