Forecasting influenza in Hong Kong with Google search queries and statistical model fusion

PLoS ONE

Volumn 12, Issue 5, 2017, Pages

  Xu, Qinneng ^a   Gel, Yulia R ^b   Ramirez, L Leticia Ramirez ^c   Nezafati, Kusha ^b   Zhang, Qingpeng ^a   Tsui, Kwok Leung ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF TEXAS AT DALLAS   (United States)

^c Centro de Investigación en Computación of the IPN CIC IPN   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL; AVERAGING; BAYESIAN MODEL AVERAGING; CLIMATE CHANGE; CLINICAL ARTICLE; CONTROLLED STUDY; DATA COLLECTION METHOD; DEEP LEARNING; DISEASE SURVEILLANCE; ENVIRONMENTAL TEMPERATURE; ERROR; FLU LIKE SYNDROME; FORECASTING; GENERALIZED LINEAR MODEL; HONG KONG; HUMAN; HUMIDITY; INFLUENZA; INFORMATION PROCESSING; LEAST ABSOLUTE SHRINKAGE AND SELECTION OPERATOR MODEL; MACHINE LEARNING; MEAN ABSOLUTE ERROR; MEAN ABSOLUTE PREDICTIVE ERROR; METEOROLOGY; OUTPATIENT DEPARTMENT; PREDICTION; ROOT MEAN SQUARED ERROR; SEASONAL INFLUENZA; SOCIAL MEDIA; STATISTICAL MODEL; WEB BROWSER; BAYES THEOREM; EVALUATION STUDY; HEALTH SURVEY; INFLUENZA, HUMAN; INTERNET; PROCEDURES; TIME FACTOR; WEATHER;

BAYES THEOREM; FORECASTING; HONG KONG; HUMANS; INFLUENZA, HUMAN; INTERNET; MACHINE LEARNING; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PUBLIC HEALTH SURVEILLANCE; TIME FACTORS; WEATHER;

EID: 85018976848     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0176690     Document Type: Article

References (80)

1. World Health Organization website. Influenza (Seasonal). Available: http://www.who.int/mediacentre/ factsheets/fs211/en/. Accessed November 28, 2016.
2. Centers for Disease Control and Prevention website. First Global Estimates of 2009 H1N1 Pandemic Mortality Released by CDC-Led Collaboration. Available: http://www.cdc.gov/flu/spotlights/pandemicglobal- estimates.htm. Accessed November 28, 2016.
3. Ginsberg J., Mohebbi M. H., Patel R. S., Brammer L., Smolinski M. S., & Brilliant L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014. https://doi.org/ 10.1038/nature07634 PMID: 19020500
4. McIver D. J., & Brownstein J. S. (2014). Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLoS Comput Biol, 10(4), e1003581. https://doi.org/10.1371/ journal.pcbi.1003581 PMID: 24743682
5. Santillana M., Zhang D. W., Althouse B. M., & Ayers J. W. (2014). What can digital disease detection learn from (an external revision to) Google Flu Trends?. American journal of preventive medicine, 47 (3), 341-347. https://doi.org/10.1016/j.amepre.2014.05.020 PMID: 24997572
6. Yang, S., Santillana, M., & Kou, S. C. (2015). ARGO: a model for accurate estimation of influenza epidemics using Google search data. arXiv preprint arXiv:1505.00864.
7. Lampos V., Miller A. C., Crossan S., & Stefansen C. (2015). Advances in nowcasting influenza-like illness rates using search query logs. Scientific reports, 5.
8. Kang M., Zhong H., He J., Rutherford S., & Yang F. (2013). Using google trends for influenza surveillance in South China. PloS one, 8(1), e55205. https://doi.org/10.1371/journal.pone.0055205 PMID: 23372837
9. Achrekar, H., Gandhe, A., Lazarus, R., Yu, S. H., & Liu, B. (2011, April). Predicting flu trends using twitter data. In Computer Communications Workshops (INFOCOM WKSHPS), 2011 IEEE Conference on (pp. 702-707). IEEE.
10. Broniatowski D. A., Paul M. J., & Dredze M. (2013). National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic. PloS one, 8(12), e83672. https://doi.org/10.1371/journal.pone.0083672 PMID: 24349542
11. Santillana M., Nguyen A. T., Dredze M., Paul M. J., Nsoesie E. O., & Brownstein J. S. (2015). Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput Biol, 11(10), e1004513. https://doi.org/10.1371/journal.pcbi.1004513 PMID: 26513245
12. Hickmann K. S., Fairchild G., Priedhorsky R., Generous N., Hyman J. M., Deshpande A., et al. (2015). Forecasting the 2013-2014 influenza season using Wikipedia. PLoS Comput Biol, 11(5), e1004239. https://doi.org/10.1371/journal.pcbi.1004239 PMID: 25974758
13. Santillana M., Nsoesie E. O., Mekaru S. R., Scales D., & Brownstein J. S. (2014). Using clinicians' search query data to monitor influenza epidemics. Clinical Infectious Diseases, 59(10), 1446-1450.
14. Polgreen P. M., Chen Y., Pennock D. M., Nelson F. D., & Weinstein R. A. (2008). Using internet searches for influenza surveillance. Clinical infectious diseases, 47(11), 1443-1448.
15. Yuan Q., Nsoesie E. O., Lv B., Peng G., Chunara R., & Brownstein J. S. (2013). Monitoring influenza epidemics in China with search query from Baidu.PloS one, 8(5), e64323. https://doi.org/10.1371/ journal.pone.0064323 PMID: 23750192
16. Shaman J., & Kohn M. (2009). Absolute humidity modulates influenza survival, transmission, and seasonality. Proceedings of the National Academy of Sciences, 106(9), 3243-3248.
17. Preis T., & Moat H. S. (2014). Adaptive nowcasting of influenza outbreaks using Google searches. Royal Society open science, 1(2), 140095. https://doi.org/10.1098/rsos.140095 PMID: 26064532
18. Shortridge K. F., Zhou N. N., Guan Y., Gao P., Ito T., Kawaoka Y., et al. (1998). Characterization of avian H5N1 influenza viruses from poultry in Hong Kong. Virology, 252(2), 331-342. https://doi.org/10.1006/viro.1998.9488 PMID: 9878612
19. Lo J. Y., Tsang T. H., Leung Y. H., Yeung E. Y., Wu T., & Lim W. W. (2005). Respiratory infections during SARS outbreak, Hong Kong, 2003. Emerg Infect Dis, 11(11), 1738-41. https://doi.org/10.3201/ eid1111.050729 PMID: 16318726
20. Cowling B. J., Ng D. M., Ip D. K., Liao Q., Lam W. W., Wu J. T., et al. (2010). Community psychological and behavioral responses through the first wave of the 2009 influenza A (H1N1) pandemic in Hong Kong.Journal of Infectious Diseases, 202(6), 867-876. https://doi.org/10.1086/655811 PMID: 20677945
21. Cowling B. J., Jin L., Lau E. H., Liao Q., Wu P., Jiang H., et al. (2013). Comparative epidemiology of human infections with avian influenza A H7N9 and H5N1 viruses in China: a population-based study of laboratory-confirmed cases. The Lancet, 382(9887), 129-137.
22. To K. K., Tsang A. K., Chan J. F., Cheng V. C., Chen H., & Yuen K. Y. (2014). Emergence in China of human disease due to avian influenza A (H10N8)-cause for concern?. Journal of Infection, 68(3), 205-215. https://doi.org/10.1016/j.jinf.2013.12.014 PMID: 24406432
23. Tamerius J. D., Shaman J., Alonso W. J., Bloom-Feshbach K., Uejio C. K., Comrie A., et al. (2013). Environmental predictors of seasonal influenza epidemics across temperate and tropical climates. PLoS Pathog, 9(3), e1003194. https://doi.org/10.1371/journal.ppat.1003194 PMID: 23505366
24. Wu J. T., Ho A., Ma E. S., Lee C. K., Chu D. K., Ho P. L., et al. (2011). Estimating infection attack rates and severity in real time during an influenza pandemic: analysis of serial cross-sectional serologic surveillance data. PLoS Med, 8(10), e1001103. https://doi.org/10.1371/journal.pmed.1001103 PMID: 21990967
25. Yang W., Cowling B. J., Lau E. H., & Shaman J. (2015). Forecasting influenza epidemics in Hong Kong. PLoS Comput Biol, 11(7), e1004383. https://doi.org/10.1371/journal.pcbi.1004383 PMID: 26226185
26. Cao P. H., Wang X., Fang S. S., Cheng X. W., Chan K. P., Wang X. L., et al. (2014). Forecasting influenza epidemics from multi-stream surveillance data in a subtropical city of China. PloS one, 9(3), e92945. https://doi.org/10.1371/journal.pone.0092945 PMID: 24676091
27. Lazer D., Kennedy R., King G., & Vespignani A. (2014). The parable of Google flu: traps in big data analysis. Science, 343(6176), 1203-1205. https://doi.org/10.1126/science.1248506 PMID: 24626916
28. Hinton G. E., & Salakhutdinov R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507. https://doi.org/10.1126/science.1127647 PMID: 16873662
29. LeCun Y., Bengio Y., & Hinton G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/ 10.1038/nature14539 PMID: 26017442
30. Schmidhuber J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003 PMID: 25462637
31. Ma X., Yu H., Wang Y., & Wang Y. (2015). Large-scale transportation network congestion evolution prediction using deep learning theory. PloS one, 10(3), e0119044. https://doi.org/10.1371/journal.pone. 0119044 PMID: 25780910
32. Zou, B., Lampos, V., Gorton, R., & Cox, I. J. (2016, April). On Infectious Intestinal Disease Surveillance using Social Media Content. In Proceedings of the 6th International Conference on Digital Health Conference (pp. 157-161). ACM.
33. Huang W., Song G., Hong H., & Xie K. (2014). Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Transactions on Intelligent Transportation Systems, 15(5), 2191-2201.
34. Hoeting J. A., Madigan D., Raftery A. E., & Volinsky C. T. (1999). Bayesian model averaging: a tutorial. Statistical Science, 382-401.
35. Raftery A. E., Gneiting T., Balabdaoui F., & Polakowski M. (2005). Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133(5), 1155-1174.
36. Centers for Disease Control and Prevention website. Overview of Influenza Surveillance in the United States. Available: http://www.cdc.gov/flu/weekly/overview.htm. Accessed November 28, 2016.
37. The Centre for Health Protection (CHP) of the Department of Health: GOPC. http://www.chp.gov.hk/en/ sentinel/26/44/292.html. Accessed 13 March 2017.
38. Wang, S., Paul, M. J., & Dredze, M. (2014, June). Exploring health topics in Chinese social media: An analysis of Sina Weibo. In AAAI Workshop on the World Wide Web and Public Health Intelligence.
39. du Prel J. B., Puppe W., Gröndahl B., Knuf M., Weigl F., Schaaff F., et al. (2009). Are meteorological parameters associated with acute respiratory tract infections?. Clinical infectious diseases, 49(6), 861-868. https://doi.org/10.1086/605435 PMID: 19663691
40. Urashima M, Shindo N, Okabe N (2003) A seasonal model to simulate influenza oscillation in Tokyo. Jpn J Infect Dis 56: 43-47. PMID: 12824683
41. Lowen AC, Mubareka S, Steel J, Palese P (2007) Influenza virus transmission is dependerelative humidity. PLoS Pathogens 3: e151.
42. Lofgren E, Fefferman NH, Naumova YN, Gorski J, Naumova EN (2007) Influenza seasonality: underlying causes and modeling theories. J Virol 81: 5429-5436. https://doi.org/10.1128/JVI.01680-06 PMID: 17182688
43. Viboud C., Pakdaman K., Boelle P. Y., Wilson M. L., Myers M. F., Valleron A. J., et al. (2004). Association of influenza epidemics with global climate variability. European journal of epidemiology, 19(11), 1055-1059. PMID: 15648600
44. Sagripanti JL, Lytle CD (2007) Inactivation of influenza virus by solar radiation. Photochem Photobiol 83: 1278-1282. https://doi.org/10.1111/j.1751-1097.2007.00177.x PMID: 17880524
45. Hong Kong Observatory. http://www.hko.gov.hk/. Accessed 13 March 2017.
46. Reich N. G., Lauer S. A., Sakrejda K., Iamsirithaworn S., Hinjoy S., Suangtho P., et al. (2016). Challenges in Real-Time Prediction of Infectious Disease: A Case Study of Dengue in Thailand. PLoS Negl Trop Dis, 10(6), e0004761. https://doi.org/10.1371/journal.pntd.0004761 PMID: 27304062
47. Brooks L. C., Farrow D. C., Hyun S., Tibshirani R. J., & Rosenfeld R. (2015). Flexible modeling of epidemics with an empirical Bayes framework. PLoS Comput Biol, 11(8), e1004382. https://doi.org/10.1371/journal.pcbi.1004382 PMID: 26317693
48. Funk S., Camacho A., Kucharski A. J., Eggo R. M., & Edmunds W. J. (2016). Real-time forecasting of infectious disease dynamics with a stochastic semi-mechanistic model. Epidemics.
49. Lenart B., Schlegelmilch J., Bergonzi-King L., Schnelle D., Difato T. L., & Wireman J. (2013). Operational epidemiological modeling: A proposed national process. Homeland Security Affairs, 9(1).
50. Burkom H. S., Murphy S. P., & Shmueli G. (2007). Automated time series forecasting for biosurveillance. Statistics in medicine, 26(22), 4202-4218.pr https://doi.org/10.1002/sim.2835 PMID: 17335120
51. Dugas A. F., Jalalpour M., Gel Y., Levin S., Torcaso F., Igusa T., et al. (2013). Influenza forecasting with Google flu trends. PloS one, 8(2), e56176. https://doi.org/10.1371/journal.pone.0056176 PMID: 23457520
52. Soebiyanto R. P., Adimi F., & Kiang R. K. (2010). Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PloS one, 5(3), e9450. https://doi.org/10.1371/ journal.pone.0009450 PMID: 20209164
53. Nelder J. A., & Baker R. J. (1972). Generalized linear models. Encyclopedia of statistical sciences.
54. McCullagh P., & Nelder J. A. (1989). Generalized linear models (Vol. 37). CRC press.
55. R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
56. Tibshirani R. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society Series B-Methodological 58 (1), 267-288.
57. Tibshirani R. (1997). The LASSO method for variable selection in the Cox model. Statistics in Medicine 16 (4), 385-395. PMID: 9044528
58. Tibshirani R., Saunders M., Rosset S., Zhu J., & Knight K. (2005). Sparsity and smoothness via the fused lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(1), 91-108.
59. Tibshirani R. and Wang P. (2007). Spatial smoothing and hot spot detection for cgh data using the fused lasso. Biostatistics Journal 9, 18-29.
60. Goeman, J. J. (2017). Penalized R package, version 0.9-50.
61. Box G. E., Jenkins G. M., Reinsel G. C., & Ljung G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
62. Hamilton J. D. (1994). Time series analysis (Vol. 2). Princeton: Princeton university press.
63. Chatfield C. (2000). Time-series forecasting. CRC Press.
64. Hyndman RJ (2017). forecast: Forecasting functions for time series and linear models. R package version 8.0
65. Hyndman R. J., & Khandakar Y. (2007). Automatic time series for forecasting: the forecast package for R (No. 6/07). Monash University, Department of Econometrics and Business Statistics.
66. Greenspan H., van Ginneken B., & Summers R. M. (2016). Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. IEEE Transactions on Medical Imaging, 35(5), 1153-1159.
67. Manning C. D. (2016). Computational linguistics and deep learning. Computational Linguistics.
68. Gawehn E., Hiss J. A., & Schneider G. (2016). Deep learning in drug discovery. Molecular Informatics, 35(1), 3-14. https://doi.org/10.1002/minf.201501008 PMID: 27491648
69. Zhang Y., & Wu L. (2008). Weights optimization of neural network via improved BCO approach. Progress In Electromagnetics Research, 83, 185-198.
70. Hinton, Geoffrey E., "A practical guide to training restricted Boltzmann machines." University of Toronto: Computer Science, https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf. Accessed 13 March 13 2017.
71. "Deep Learning Tutorial." http://deeplearning.net/tutorial/deeplearning.pdf. Accessed 13 March 2017
72. Goodfellow, Ian, Yoshua Bengio, Aaron Courville. "Deep Learning." http://www.deeplearningbook.org/. Accessed 13 March 2017.
73. Candel, Arno, Jessica Lanford, Erin LeDell, Viraj Parmar, Anisha Arora. "Deep Learning with H2O." https://h2o-release.s3.amazonaws.com/h2o/rel-slater/9/docs-website/h2o-docs/booklets/ DeepLearning-Vignette.pdf. Accessed 13 March 2017
74. Faust J., Gilchrist S., Wright J. H., & Zakrajssek E. (2013). Credit spreads as predictors of real-time economic activity: a Bayesian model-averaging approach. Review of Economics and Statistics, 95(5), 1501-1519.
75. McLean Sloughter J., Gneiting T., & Raftery A. E. (2013). Probabilistic wind vector forecasting using ensembles and Bayesian model averaging. Monthly Weather Review, 141(6), 2107-2119.
76. Wöhling T., Schöniger A., Gayler S., & Nowak W. (2015). Bayesian model averaging to explore the worth of data for soil-plant model selection and prediction. Water Resources Research, 51(4), 2825-2846.
77. Raftery, A., Hoeting, J., Volinsky, C., Painter, I. and Yeung, K.Y. (2015). BMA: Bayesian Model Averaging. R package version 3.18.6.
78. Ramirez-Ramirez L.L., Gel Y. R., Thompson M.E., De Villa E. and Mcpherson M. (2013). A new surveillance and spatio-temporal visualization tool SIMID: SIMulation of Infectious Diseases using random networks and GIS. Comput. Methods & Programs in Biomedicine, 110(3):455-470.
79. Nsoesie E.O., Marathe M, and Brownstein J.S. Forecasting peaks of seasonal influenza epidemics. PLoS ONE, 8(2), 06 2013.
80. Popa, J., Nezafati, K., Gel, Y.R., Zweck, J., Bobashev, G. (2016). Catching Social Butterflies: Identifying Influential Users of an Event-Based Social Networking Service. Proceedings of the 2016 IEEE Big Data Congress, 198-205.