Predicting infectious disease using deep learning and big data

International Journal of Environmental Research and Public Health

Volumn 15, Issue 8, 2018, Pages

  Chae, Sangwon ^a   Kwon, Sungjun ^a   Lee, Donghyun ^a  

^a Korea Polytechnic University   (South Korea)

Author keywords

Deep learning; Deep neural network; Infectious disease prediction; Long short term memory; Social media big data

Indexed keywords

ARTIFICIAL NEURAL NETWORK; DATA SET; DISEASE CONTROL; INFECTIOUS DISEASE; MODEL; PREDICTION; SOCIAL MEDIA;

ARTICLE; AUTOREGRESSIVE INTEGRATED MOVING AVERAGE; CHICKENPOX; COST MINIMIZATION ANALYSIS; DATA ANALYSIS; DEEP NEURAL NETWORK; DIAGNOSTIC ACCURACY; DISEASE SURVEILLANCE; HUMAN; INFECTION; INFORMATION PROCESSING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; LONG TERM MEMORY; MALARIA; MATHEMATICAL MODEL; NONHUMAN; PREDICTION; SCARLET FEVER; SHORT TERM MEMORY; SOCIAL MEDIA; SOUTH KOREA; ALGORITHM; COMMUNICABLE DISEASE; FORECASTING; THEORETICAL MODEL;

KOREA;

ANIMALIA;

ALGORITHMS; COMMUNICABLE DISEASES; DEEP LEARNING; FORECASTING; HUMANS; MODELS, THEORETICAL; REPUBLIC OF KOREA;

EID: 85050977874     PISSN: 16617827     EISSN: 16604601     Source Type: Journal    
DOI: 10.3390/ijerph15081596     Document Type: Article

References (48)

1. Jae, C.M. Infectious disease, safety, state: History of infectious disease prevention and MERS situation. Crit. Stud. Mod. Korean Hist. 2015, 34, 517-542, ISSN 1226-4199.
2. Cheng, C.K.; Lau, E.H.; Ip, D.K.; Yeung, A.S.; Ho, L.M.; Cowling, B.J. A profile of the online dissemination of national influenza surveillance data. BMC Public Health 2009, 9, 339, doi:10.1186/1471-2458-9-339.
3. Balcan, D.; Colizza, V.; Gonçalves, B.; Hu, H.; Ramasco, J.J.; Vespignani, A. Multiscale mobility networks and the spatial spreading of infectious diseases. Proc. Natl. Acad. Sci. USA 2009, 106, 21484, doi:10.1073/pnas.0906910106.
4. Colizza, V.; Barrat, A.; Barthelemy, M.; Valleron, A.; Vespignani, A. Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions. PLOS Med. 2007, 4, e13, doi:10.1371/journal.pmed.0040013.
5. Balcan, D.; Hu, H.; Goncalves, B.; Bajardi, P.; Poletto, C.; Ramasco, J.J.; Paolotti, D.; Perra, N.; Tizzoni, M.; van den Broeck, W. et al. Seasonal transmission potential and activity peaks of the new influenza A(H1N1): A Monte Carlo likelihood analysis based on human mobility. BMC Med. 2009, 1, 45, doi:10.1186/1741-7015-7-45.
6. Eubank, S.; Guclu, H.; Anil Kumar, V.S.; Marathe, M.V.; Srinivasan, A.; Toroczkai, Z.; Wang, N. Modelling disease outbreaks in realistic urban social networks. Nature 2004, 429, 180, doi:10.1038/nature02541.
7. Ferguson, N.M.; Cummings, D.A.T.; Fraser, C.; Cajka, J.C.; Cooley, P.C.; Burke, D.S. Strategies for mitigating an influenza pandemic. Nature 2006, 442, 448, doi:10.1038/nature04795.
8. Epstein, J.M.; Goedecke, D.M.; Yu, F.; Morris, R.J.; Wagener, D.K.; Bobashev, G.V. Controlling Pandemic Flu: The Value of International Air Travel Restrictions. PLoS ONE 2007, 2, e401, doi:10.1371/journal.pone.0000401.
9. Ciofi degli Atti, M.L.; Merler, S.; Rizzo, C.; Ajelli, M.; Massari, M.; Manfredi, P.; Furlanello, C.; Scalia Tomba, G.; Iannelli, M. Mitigation Measures for Pandemic Influenza in Italy: An Individual Based Model Considering Different Scenarios. PLoS ONE 2008, 3, e1790, doi:10.1371/journal.pone.0001790.
10. Zhang, Y.; Milinovich, G.; Xu, Z.; Bambrick, H.; Mengersen, K.; Tong, S.; Hu, W. Monitoring Pertussis Infections Using Internet Search Queries. Sci. Rep. 2017, 7, 10437, doi:10.1038/s41598-017-11195-z.
11. Rohart, F.; Milinovich, G.J.; Avril, S.M.R.; Lê Cao, K.; Tong, S.; Hu, W. Disease surveillance based on Internet-based linear models: An Australian case study of previously unmodeled infection diseases. Sci. Rep. 2016, 6, 38522, doi:10.1038/srep38522.
12. Lampos, V.; Miller, A.C.; Crossan, S.; Stefansen, C. Advances in nowcasting influenza-like illness rates using search query logs. Sci. Rep. 2015, 5, 12760, doi:10.1038/srep12760.
13. Cho, S.; Sohn, C.H.; Jo, M.W.; Shin, S.Y.; Lee, J.H.; Ryoo, S.M.; Kim, W.Y.; Seo, D.W. Correlation between national influenza surveillance data and google trends in South Korea. PLoS ONE 2013, 8, e81422, doi:10.1371/journal.pone.0081422.
14. Teng, Y.; Bi, D.; Xie, G.; Jin, Y.; Huang, Y.; Lin, B.; An, X.; Feng, D.; Tong, Y. Dynamic Forecasting of Zika Epidemics Using Google Trends. PLoS ONE 2017, 12, e0165085, doi:10.1371/journal.pone.0165085.
15. Dugas, A.F.; Jalalpour, M.; Gel, Y.; Levin, S.; Torcaso, F.; Igusa, T.; Rothman, R.E. Influenza forecasting with Google Flu Trends. PLoS ONE 2013, 8, e56176, doi:10.1371/journal.pone.0056176.
16. Towers, S.; Afzal, S.; Bernal, G.; Bliss, N.; Brown, S.; Espinoza, B.; Jackson, J.; Judson-Garcia, J.; Khan, M.; Lin, M. et al. Mass Media and the Contagion of Fear: The Case of Ebola in America. PLoS ONE 2015, 10, e0129179, doi:10.1371/journal.pone.0129179.
17. Huang, D.C.; Wang, J.F. Monitoring hand, foot and mouth disease by combining search engine query data and meteorological factors. Sci. Total Environ. 2018, 612, 1293-1299, doi:10.1016/j.scitotenv.2017.09.017.
18. Tenkanen, H.; di Minin, E.; Heikinheimo, V.; Hausmann, A.; Herbst, M.; Kajala, L.; Toivonen, T. Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas. Sci. Rep. 2017, 7, 17615, doi:10.1038/s41598-017-18007-4.
19. Reece, A.G.; Reagan, A.J.; Lix, K.L.M.; Dodds, P.S.; Danforth, C.M.; Langer, E.J. Forecasting the onset and course of mental illness with Twitter data. Sci. Rep. 2017, 7, 13006, doi:10.1038/s41598-017-12961-9.
20. Shin, S.; Seo, D.; An, J.; Kwak, H.; Kim, S.; Gwack, J.; Jo, M. High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea. Sci. Rep. 2016, 6, 32920, doi:10.1038/srep32920.
21. Thapen, N.; Simmie, D.; Hankin, C.; Gillard, J. DEFENDER: Detecting and Forecasting Epidemics Using Novel Data-Analytics for Enhanced Response. PLoS ONE 2016, 11, e0155417, doi:10.1371/journal.pone.0155417.
22. Allen, C.; Tsou, M.; Aslam, A.; Nagel, A.; Gawron, J. Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza. PLoS ONE 2016, 11, e0157734, doi:10.1371/journal.pone.0157734.
23. Volkova, S.; Ayton, E.; Porterfield, K.; Corley, C.D. Forecasting influenza-like illness dynamics for military populations using neural networks and social media. PLoS ONE 2017, 12, e0188941, doi:10.1371/journal.pone.0188941.
24. Simon, T.; Goldberg, A.; Aharonson-Daniel, L.; Leykin, D.; Adini, B. Twitter in the Cross Fire—The Use of Social Media in the Westgate Mall Terror Attack in Kenya. PLoS ONE 2014, 9, e104136, doi:10.1371/journal.pone.0104136.
25. Tafti, A.; Zotti, R.; Jank, W. Real-Time Diffusion of Information on Twitter and the Financial Markets. PLoS ONE 2016, 11, e0159226, doi:10.1371/journal.pone.0159226.
26. Xia, F.; Su, X.; Wang, W.; Zhang, C.; Ning, Z.; Lee, I. Bibliographic Analysis of Nature Based on Twitter and Facebook Altmetrics Data. PLoS ONE 2016, 11, e0165997. doi:10.1371/journal.pone.0165997.
27. Patel, R.; Belousov, M.; Jani, M.; Dasgupta, N.; Winakor, C.; Nenadic, G.; Dixon, W.G. Frequent discussion of insomnia and weight gain with glucocorticoid therapy: An analysis of Twitter posts. Npj Digit. Med. 2018, 1, 7, doi:10.1038/s41746-017-0007-z.
28. Xu, Q.; Gel, YR.; Ramirez Ramirez, LL.; Nezafati, K.; Zhang, Q.; Tsui, KL. Forecasting influenza in Hong Kong with Google search Queries and statistical model fusion. PLOS ONE 2017, 12, e0176690, doi:10.1371/journal.pone.0176690.
29. He, F.; Hu, Z.; Zhang, W.; Cai, L.; Cai, G.; Aoyagi, K. Construction and evaluation of two computational models for predicting the incidence of influenza in Nagasaki Prefecture, Japan. Sci. Rep. 2017, 7, 7192, doi:10.1038/s41598-017-07475-3.
30. Najafabadi, M.M.; Villanustre, F.; Khoshgoftaar, T.M.; Seliya, N.; Wald, R.; Muharemagic, E. Deep learning applications and challenges in big data analytics. J. Big Data 2017, 2, doi:10.1186/s40537-014-0007-7.
31. Janowczyk, A.; Madabhushi, A. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases. J. Pathol. Inform. 2016, 7, 29-29, doi:10.4103/2153-3539.186902.
32. Esteva, A.; Kuprel, B.; Novoa, R.A.; Ko, J.; Swetter, S.M.; Blau, H.M.; Thrun, S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017, 542, 115, doi:10.1038/nature21056.
33. Bychkov, D.; Linder, N.; Turkki, R.; Nordling, S.; Kovanen, P.E.; Verrill, C.; Walliander, M.; Lundin, M.; Haglund, C.; Lundin, J. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep. 2018, 8, 3395, doi:10.1038/s41598-018-21758-3.
34. Song, Y.; Wang, F.; Wang, B.; Tao, S.; Zhang, H.; Liu, S.; Ramirez, O.; Zeng, Q. Time Series Analyses of Hand, Foot and Mouth Disease Integrating Weather Variables. PLoS ONE 2015, 10, e0117296, doi:10.1371/journal.pone.0117296.
35. Hii, Y.L.; Rocklöv, J.; Ng, N. Short Term Effects of Weather on Hand, Foot and Mouth Disease. PLoS ONE 2011, 6, e16796, doi:10.1371/journal.pone.0016796.
36. Lopman, Ben.; Armstrong, Ben.; Atchison, Christina.; Gray, Jim J. Host, Weather and Virological Factors Drive Norovirus Epidemiology: Time-Series Analysis of Laboratory Surveillance Data in England and Wales. PLoS ONE 2009, 4, e6671, doi:10.1371/journal.pone.0006671.
37. Huang, X.; Williams, G.; Clements, A.C.A.; Hu, W. Imported Dengue Cases, Weather Variation and Autochthonous Dengue Incidence in Cairns, Australia. PLoS ONE 2013, 8, e81887, doi:10.1371/journal.pone.0081887.
38. Liu, T.; Zhang, Y.; Lin, H.; Lv, X.; Xiao, J.; Zeng, W.; Gu, Y.; Rutherford, S.; Tong, S.; Ma, W. A large temperature fluctuation may trigger an epidemic erythromelalgia outbreak in China. Sci. Rep. 2015, 5, 9525, doi:10.1038/srep09525.
39. Blanford, J.I.; Blanford, S.; Crane, R.G.; Mann, M.E.; Paaijmans, K.P.; Schreiber, K.V.; Thomas, M.B. Implications of temperature variation for malaria parasite development across Africa. Sci. Rep. 2013, 3, 1300, doi:10.1038/srep01300.
40. Noden, B.H.; Kent, M.D.; Beier, J.C. The impact of variations in temperature on early Plasmodium falciparum development in Anopheles stephensi. Parasitology 1995, 111, 539-545, doi:10.1017/S0031182000077003.
41. Liang, W.; Gu, X.; Li, X.; Zhang, K.; Wu, K.; Pang, M.; Dong, J.; Merrill, H.R.; Hu, T.; Liu, K. et al. Mapping the epidemic changes and risks of hemorrhagic fever with renal syndrome in Shaanxi Province, China, 2005-2016. Sci. Rep. 2018, 8, 749, doi:10.1038/s41598-017-18819-4.
42. Huang, X.; Clements, A.C.A.; Williams, G.; Milinovich, G.; Hu, W. A threshold analysis of dengue transmission in terms of weather variables and imported dengue cases in Australia. Emerg. Microbes Amp Infect. 2013, 2, e87, doi:10.1038/emi.2013.85.
43. Kwon, C.; Hwang, S.; Jung, J. Monitoring Seasonal Influenza Epidemics in Korea through Query Search. J. Korea Soc. Simul. 2014, 23, 31-39, doi:10.9709/jkss.2014.23.4.031.
44. Zheng, Y.L.; Zhang, L.P.; Zhang, X.L.; Wank, K.; Zheng, Y.J. Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China. PLoS ONE 2015, 10, e0116832, doi:10.1371/journal.pone.0116832.
45. Myriam, G.; Philippe, Q.; Joel, G.; Sylvie, C.; Guy, L.; Laurent, G.; Laurence, M. Time series analysis of dengue incidence in Guadeloupe, French West Indies: Forecasting models using climate variables as predictors. BMC Infect. Dis. 2011, 11, 166, doi:10.1186/1471-2334-11-166.
46. Li, Q.; Guo, N.N.; Han, Z.Y.; Zhang, Y.B.; Qi, S.X.; Xu, Y.G.; Wei, Y.M.; Han, X.; Liu, Y.Y. Application of an autoregressive integrated moving average model for predicting the incidence of hemorrhagic fever with renal syndrome. Am. J. Trop. Med. Hyg. 2012, 87, 364-370, doi:10.4269/ajtmh.2012.11-0472.
47. Bao, W.; Yue, J.; Rao, Y. A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE 2017, 12, e0180944, doi:10.1371/journal.pone.0180944.
48. Alex, G. Supervised Sequence Labelling with Recurrent Neural Networks; Studies in Computational Intelligence; Springer: Berlin, Germany, 2012.