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Prediction of influenza peaks in Russian cities: Comparing the accuracy of two SEIR models

. ITMO University, 49 Kronverksky Pr, 197101, St. Petersburg, Russia

This paper is dedicated to the application of two types of SEIR models to the influenza outbreak peak prediction in Russian cities. The first one is a continuous SEIR model described by a system of ordinary differential equations. The second one is a discrete model formulated as a set of difference equations, which was used in the Baroyan-Rvachev modeling framework for the influenza outbreak prediction in the Soviet Union. The outbreak peak day and height predictions were performed by calibrating both models to varied-size samples of long-term data on ARI incidence in Moscow, Saint Petersburg, and Novosibirsk. The accuracy of the modeling predictions on incomplete data was compared with a number of other peak forecasting methods tested on the same dataset. The drawbacks of the described prediction approach and possible ways to overcome them are discussed.

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Keywords Mathematical modeling; epidemiology; influenza; prediction

Citation: Vasiliy N. Leonenko, Sergey V. Ivanov. Prediction of influenza peaks in Russian cities: Comparing the accuracy of two SEIR models. Mathematical Biosciences and Engineering, 2018, 15(1): 209-232. doi: 10.3934/mbe.2018009

References

  • [1] O. Baroyan, U. Basilevsky, V. Ermakov, K. Frank, L. Rvachev and V. Shashkov, Computer modelling of influenza epidemics for large-scale systems of cities and territories, in Proc. WHO Symposium on Quantitative Epidemiology, Moscow, 1970.
  • [2] R. Burger,G. Chowell,P. Mulet,L. Villada, Modelling the spatial-temporal progression of the 2009 A/H1N1 influenza pandemic in Chile, Mathematical Biosciences and Engineering, 13 (2016): 43-65.
  • [3] CDC, Influenza signs and symptoms and the role of laboratory diagnostics, [online], http://www.cdc.gov/flu/professionals/diagnosis/labrolesprocedures.htm.
  • [4] CDC, People with heart disease and those who have had a stroke are at high risk of developing complications from influenza (the flu), [online], http://www.cdc.gov/flu/heartdisease/.
  • [5] J. -P. Chretien, D. George, J. Shaman, R. A. Chitale and F. E. McKenzie, Influenza forecasting in human populations: A scoping review, PloS one, 9 (2014), e94130.
  • [6] A. D. Cliff, P. Haggett and J. K. Ord, Spatial Aspects of Influenza Epidemics, Routledge, 1986.
  • [7] V. Colizza, A. Barrat, M. Barthelemy, A. -J. Valleron and A. Vespignani, Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions, PLoS Med, 4 (2007), e13.
  • [8] S. Cook, C. Conrad, A. L. Fowlkes and M. H. Mohebbi, Assessing google flu trends performance in the united states during the 2009 influenza virus a (h1n1) pandemic PloS one, 6 (2011), e23610.
  • [9] N. Goeyvaerts, L. Willem, K. Van~Kerckhove, Y. Vandendijck, G. Hanquet, P. Beutels and N. Hens, Estimating dynamic transmission model parameters for seasonal influenza by fitting to age and season-specific influenza-like illness incidence Epidemics, 13 (2015), p1.
  • [10] I. Hall,R. Gani,H. Hughes,S. Leach, Real-time epidemic forecasting for pandemic influenza, Epidemiology and Infection, 135 (2007): 372-385.
  • [11] D. He, J. Dushoff, R. Eftimie and D. J. Earn, Patterns of spread of influenza A in Canada, Proceedings of the Royal Society of London B: Biological Sciences, 280 (2013), 20131174.
  • [12] K. S. Hickmann,G. Fairchild,R. Priedhorsky,N. Generous,J. M. Hyman,A. Deshpande,S. Y. Del Valle, Forecasting the 2013-2014 influenza season using wikipedia, PLoS Comput Biol, 11 (2015): e1004239.
  • [13] A. Hyder, D. L. Buckeridge and B. Leung, Predictive validation of an influenza spread model PloS one, 8 (2013), e65459.
  • [14] F. Institute, Research Institute of Influenza website, [online], http://influenza.spb.ru/en/.
  • [15] Y. G. Ivannikov and A. T. Ismagulov, Epidemiologiya Grippa (The Epidemiology of Influenza), Almaty, Kazakhstan, 1983, In Russian.
  • [16] Y. Ivannikov,P. Ogarkov, An experience of mathematical computing forecasting of the influenza epidemics for big territory, Journal of Infectology, 4 (2012): 101-106.
  • [17] V. N. Leonenko,S. V. Ivanov, Fitting the SEIR model of seasonal influenza outbreak to the incidence data for Russian cities, Russian Journal of Numerical Analysis and Mathematical Modelling, 31 (2016): 267-279.
  • [18] V. N. Leonenko,S. V. Ivanov,Y. K. Novoselova, A computational approach to investigate patterns of acute respiratory illness dynamics in the regions with distinct seasonal climate transitions, Procedia Computer Science, 80 (2016): 2402-2412.
  • [19] V. N. Leonenko,Y. K. Novoselova,K. M. Ong, Influenza outbreaks forecasting in Russian cities: Is Baroyan-Rvachev approach still applicable?, Procedia Computer Science, 101 (2016): 282-291.
  • [20] V. N. Leonenko,N. V. Pertsev,M. Artzrouni, Using high performance algorithms for the hybrid simulation of disease dynamics on CPU and GPU, Procedia Computer Science, 51 (2015): 150-159.
  • [21] D. C. Liu,J. Nocedal, On the limited memory bfgs method for large scale optimization, Mathematical programming, 45 (1989): 503-528.
  • [22] A. Romanyukha,T. Sannikova,I. Drynov, The origin of acute respiratory epidemics, Herald of the Russian Academy of Sciences, 81 (2011): 31-34.
  • [23] L. A. Rvachev,I. M. Longini, A mathematical model for the global spread of influenza, Mathematical Biosciences, 75 (1985): 1-22.
  • [24] J. Shaman, V. E. Pitzer, C. Viboud, B. T. Grenfell and M. Lipsitch, Absolute humidity and the seasonal onset of influenza in the continental United States, PLoS Biol, 8 (2010), e1000316.
  • [25] J. Tamerius,M. I. Nelson,S. Z. Zhou,C. Viboud,M. A. Miller,W. J. Alonso, Global influenza seasonality: Reconciling patterns across temperate and tropical regions, Environmental Health Perspectives, 119 (2011): 439-445.
  • [26] J. Truscott,C. Fraser,S. Cauchemez,A. Meeyai,W. Hinsley,C. A. Donnelly,A. Ghani,N. Ferguson, Essential epidemiological mechanisms underpinning the transmission dynamics of seasonal influenza, Journal of The Royal Society Interface, 9 (2011): 304-312.
  • [27] S. P. van Noort,R. Águas,S. Ballesteros,M. G. M. Gomes, The role of weather on the relation between influenza and influenza-like illness, Journal of Theoretical Biology, 298 (2012): 131-137.
  • [28] C. Viboud,O. N. Bjornstad,D. L. Smith,L. Simonsen,M. A. Miller,B. T. Grenfell, Synchrony, waves, and spatial hierarchies in the spread of influenza, Science, 312 (2006): 447-451.
  • [29] WHO, Influenza (seasonal). Fact sheet No. 211, March 2014. , [online], http://www.who.int/mediacentre/factsheets/fs211/en/.
  • [30] WHO, Surveillance case definitions for ILI and SARI, [online], http://www.who.int/influenza/surveillance_monitoring/ili_sari_surveillance_case_definition/en/.
  • [31] R. Yaari, G. Katriel, A. Huppert, J. Axelsen and L. Stone, Modelling seasonal influenza: The role of weather and punctuated antigenic drift, Journal of The Royal Society Interface, 10 (2013), 20130298.
  • [32] W. Yang, B. J. Cowling, E. H. Lau and J. Shaman, Forecasting influenza epidemics in hong kong, PLoS Comput Biol, 11 (2015), e1004383.

 

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