Forecasting type-specific seasonal influenza after 26 weeks in the United States using influenza activities in other countries
Autoři:
Soo Beom Choi aff001; Juhyeon Kim aff001; Insung Ahn aff001
Působiště autorů:
Department of Data-centric Problem Solving Research, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea
aff001; Center for Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon, Republic of Korea
aff002
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0220423
Souhrn
To identify countries that have seasonal patterns similar to the time series of influenza surveillance data in the United States and other countries, and to forecast the 2018–2019 seasonal influenza outbreak in the U.S., we collected the surveillance data of 164 countries using the FluNet database, search queries from Google Trends, and temperature from 2010 to 2018. Data for influenza-like illness (ILI) in the U.S. were collected from the Fluview database. We identified the time lag between two time-series which were weekly surveillances for ILI, total influenza (Total INF), influenza A (INF A), and influenza B (INF B) viruses between two countries using cross-correlation analysis. In order to forecast ILI, Total INF, INF A, and INF B of next season (after 26 weeks) in the U.S., we developed prediction models using linear regression, auto regressive integrated moving average, and an artificial neural network (ANN). As a result of cross-correlation analysis between the countries located in northern and southern hemisphere, the seasonal influenza patterns in Australia and Chile showed a high correlation with those of the U.S. 22 weeks and 28 weeks earlier, respectively. The R2 score of ANN models for ILI for validation set in 2015–2019 was 0.758 despite how hard it is to forecast 26 weeks ahead. Our prediction models forecast that the ILI for the U.S. in 2018–2019 may be later and less severe than those in 2017–2018, judging from the influenza activity for Australia and Chile in 2018. It allows to estimate peak timing, peak intensity, and type-specific influenza activities for next season at 40th week. The correlation between seasonal influenza patterns in the U.S., Australia, and Chile could be used to forecast the next seasonal influenza pattern, which can help to determine influenza vaccine strategy approximately six months ahead in the U.S.
Klíčová slova:
Artificial neural networks – Australia – Infectious disease surveillance – Influenza – Influenza A virus – Influenza B virus – Influenza viruses
Zdroje
1. Petrova VN, Russell CA. The evolution of seasonal influenza viruses. Nat Rev Microbiol 2018;16:47–60. doi: 10.1038/nrmicro.2017.118 29081496
2. Xu C, Chan KH, Tsang TK, Fang VJ, Fung RO, Ip DK, et al. Comparative Epidemiology of Influenza B Yamagata–and Victoria–Lineage Viruses in Households. Am J Epidemiol 2015;182:705–13. doi: 10.1093/aje/kwv110 26400854
3. Newman LP, Bhat N, Fleming JA, Neuzil KM. Global influenza seasonality to inform country–level vaccine programs: An analysis of WHO FluNet influenza surveillance data between 2011 and 2016. PLoS One 2018;13:e0193263. doi: 10.1371/journal.pone.0193263 29466459
4. Chretien JP, George D, Shaman J, Chitale RA, McKenzie FE. Influenza forecasting in human populations: a scoping review. PLoS One 2014;9:e94130. doi: 10.1371/journal.pone.0094130 24714027
5. Cho S, Sohn CH, Jo MW, Shin SY, Lee JH, Ryoo SM, et al. Correlation between national influenza surveillance data and google trends in South Korea. PLoS One 2013;8:e81422. doi: 10.1371/journal.pone.0081422 24339927
6. Won M, Marques-Pita M, Louro C, Gonçalves-Sá J. Early and Real–Time Detection of Seasonal Influenza Onset. PLoS Comput Biol 2017;13:e1005330. doi: 10.1371/journal.pcbi.1005330 28158192
7. Tamerius JD, Shaman J, Alonso WJ, Bloom-Feshbach K, Uejio CK, Comrie A, et al. Environmental predictors of seasonal influenza epidemics across temperate and tropical climates. PLoS Pathog 2013;9:e1003194. doi: 10.1371/journal.ppat.1003194 23505366
8. Xi G, Yin L, Li Y, Mei S. A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery. ACM; 2018;19–28.
9. Yang W, Olson DR, Shaman J. Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City. PLoS Comput Biol 2016;12:e1005201. doi: 10.1371/journal.pcbi.1005201 27855155
10. Ortiz JR, Sotomayor V, Uez OC, Oliva O, Bettels D, McCarron M, et al. Strategy to enhance influenza surveillance worldwide. Emerg Infect Dis 2009;15:1271–8. doi: 10.3201/eid1508.081422 19751590
11. Viboud C, Vespignani A. The future of influenza forecasts. Proc Natl Acad Sci U S A. 2019;116:2802–4. doi: 10.1073/pnas.1822167116 30737293
12. Agor JK, Özaltın OY. Models for predicting the evolution of influenza to inform vaccine strain selection. Hum Vaccin Immunother 2018;14:678–83. doi: 10.1080/21645515.2017.1423152 29337643
13. Alonso WJ, Yu C, Viboud C, Richard SA, Schuck-Paim C, Simonsen L, et al. A global map of hemispheric influenza vaccine recommendations based on local patterns of viral circulation. Sci Rep 2015;5:17214. doi: 10.1038/srep17214 26621769
14. Shaman J, Karspeck A. Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci U S A 2012;109:20425–30. doi: 10.1073/pnas.1208772109 23184969
15. Paul S, Mgbere O, Arafat R, Yang B, Santos E. Modeling and Forecasting Influenza-like Illness (ILI) in Houston, Texas Using Three Surveillance Data Capture Mechanisms. Online J Public Health Inform 2017;9:e187. doi: 10.5210/ojphi.v9i2.8004 29026453
16. Polgreen PM, Nelson FD, Neumann GR. Use of prediction markets to forecast infectious disease activity. Clin Infect Dis 2007;44:272–9. doi: 10.1086/510427 17173231
17. Du X, Pascual M. Incidence Prediction for the 2017–2018 Influenza Season in the United States with an Evolution–informed Model. PLoS Curr 2018;10.
18. Saha S, Chadha M, Shu Y. Group of Asian Researchers on Influenza (GARI). Divergent seasonal patterns of influenza types A and B across latitude gradient in Tropical Asia. Influenza Other Respir Viruses 2016;10:176–84. doi: 10.1111/irv.12372 26781162
19. Caini S, Alonso WJ, Séblain CE, Schellevis F, Paget J. The spatiotemporal characteristics of influenza A and B in the WHO European Region: can one define influenza transmission zones in Europe? Euro Surveill 2017;22:1–11.
20. Arora VS, McKee M, Stuckler D. Google Trends: Opportunities and limitations in health and health policy research. Health Policy 2019;123:338–41. doi: 10.1016/j.healthpol.2019.01.001 30660346
21. Páscoa R, Rodrigues AP, Silva S, Nunes B, Martins C. Comparison between influenza coded primary care consultations and national influenza incidence obtained by the General Practitioners Sentinel Network in Portugal from 2012 to 2017. PLoS One 2018;13:e0192681. doi: 10.1371/journal.pone.0192681 29438406
22. Zhang J, Nawata K. Multi–step prediction for influenza outbreak by an adjusted long short–term memory. Epidemiol Infect 2018;146:809–16. doi: 10.1017/S0950268818000705 29606177
23. Zou J, Han Y, So SS. Overview of artificial neural networks. Methods Mol Biol 2008;458:15–23. doi: 10.1007/978-1-60327-101-1_2 19065803
24. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44. doi: 10.1038/nature14539 26017442
25. Poonia P, Jain VK, Kumar A. Deep Learning: Review. Int J Comput Sci Math Sci 2016;5:43–7.
26. Soebiyanto RP, Adimi F, Kiang RK. Modeling and predicting seasonal influenza transmission in warm regions using climatological parameters. PLoS One 2010;5:e9450. doi: 10.1371/journal.pone.0009450 20209164
27. Cryer JD, Chan K. Time Series Analysis: With Applications in R. Springer-Verlag New York; 2008.
28. Centers for disease control and prevention. Overview of Influenza Surveillance in the United States. https://www.cdc.gov/flu/weekly/overview.htm (accessed 10 Jul 2019).
29. Chattopadhyay I, Kiciman E, Elliott JW, Shaman JL, Rzhetsky A. Conjunction of factors triggering waves of seasonal influenza. Elife 2018;7:e30756. doi: 10.7554/eLife.30756 29485041
30. Bedford T, Riley S, Barr IG, Broor S, Chadha M, Cox NJ, et al. Global circulation patterns of seasonal influenza viruses vary with antigenic drift. Nature 2015;523:217–20. doi: 10.1038/nature14460 26053121
31. Viboud C, Boëlle PY, Pakdaman K, Carrat F, Valleron AJ, Flahault A. Influenza epidemics in the United States, France, and Australia, 1972–1997. Emerg Infect Dis 2004;10:32–9. doi: 10.3201/eid1001.020705 15078594
32. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature 2009;457:1012–4. doi: 10.1038/nature07634 19020500
33. Hickmann KS, Fairchild G, Priedhorsky R, Generous N, Hyman JM, Deshpande A, et al. Forecasting the 2013–2014 influenza season using Wikipedia. PLoS Comput Biol 2015;11:e1004239. doi: 10.1371/journal.pcbi.1004239 25974758
34. Hu H, Wang H, Wang F, Langley D, Avram A, Liu M. Prediction of influenza–like illness based on the improved artificial tree algorithm and artificial neural network. Sci Rep 2018;8:4895. doi: 10.1038/s41598-018-23075-1 29559649
35. Kandula S, Yang W, Shaman J. Type- and Subtype-Specific Influenza Forecast. Am J Epidemiol 2017;185:395–402. doi: 10.1093/aje/kww211 28174833
36. Moa AM, Muscatello DJ, Turner RM, MacIntyre CR. Epidemiology of influenza B in Australia: 2001–2014 influenza seasons. Influenza Other Respir Viruses 2017;11:102–9. doi: 10.1111/irv.12432 27650482
Článek vyšel v časopise
PLOS One
2019 Číslo 11
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