doi: 10.17586/2226-1494-2016-16-6-1145-1148


V. N. Leonenko, Y. K. Novoselova, K. M. Ong

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For citation: Leonenko V.N., Novoselova Yu.K., Ong K.M. Prediction of flu epidemic peaks in St. Petersburg through population-based mathematical models. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2016, vol. 16, no. 6, pp. 1145–1148. doi: 10.17586/2226-1494-2016-16-6-1145-1148


The paper presents two methods of predicting the peak of influenza epidemics using population-based mathematical models: Baroyan-Rvachev and modified Kermack-McKendrick model, proposed by the authors. We perform the comparison of the prediction accuracy of time and the value of epidemics peaks on long-term data of ARI incidence in the city of St. Petersburg. The methodology of comparison is based on three criteria of accuracy conventionally named as "square", "vertical stripe" and "horizontal stripe", and two variants of the model parameters estimation. In the first variant we calibrate the model on the data of the first city impacted by the epidemic, and use these parameters in the future for the other cities, that allows taking into account the spatial characteristics of the epidemic in the country. In the second case, we only use historical data available at the time of the prediction for a given city. The advantage of this approach is the lack of need for additional, not always available, external data to predict the epidemic. The results of test calculations have demonstrated that the first method shows good results in the case of significant delays between the peaks of epidemics in different cities. If the outbreak in St. Petersburg started soon after the registration of the first outbreaks in the other cities of the Russian Federation, the second method shows comparable results to an accuracy of 90% to predict the peak of the epidemic. In most cases, it is sufficient for the use of the results of calculations for planning antiviral activities. The lead time of the peak prediction is still at a relatively low level, that seems to be associated with a variety of patterns of virus spread and permanent changes in transport communications within the country.

Keywords: population-based models, prediction, mathematical epidemiology, influenza, calibration

Acknowledgements. This paper is financially supported by the Russian Scientific Foundation, Agreement No.14–21–00137 (15.08.2014)


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