Application of the backstepping method to the prediction of increase or decrease of infected population. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Kuniya, Toshikazu; Sano, Hideki
• Is Part Of: Theoretical biology and medical modelling. Volume 13:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: Backstepping method -- Age structure -- Prediction -- Influenza -- ARIMA; Biology -- Mathematical models -- Periodicals; Biology -- Periodicals; Medical sciences -- Periodicals; 570.15118
• DOI: 10.1186/s12976-016-0041-6
• Languages: English
• ISSNs: 1742-4682

Abstract

Abstract Background In mathematical epidemiology, age-structured epidemic models have usually been formulated as the boundary-value problems of the partial differential equations. On the other hand, in engineering, the backstepping method has recently been developed and widely studied by many authors. Methods Using the backstepping method, we obtained a boundary feedback control which plays the role of the threshold criteria for the prediction of increase or decrease of newly infected population. Under an assumption that the period of infectiousness is same for all infected individuals (that is, the recovery rate is given by the Dirac delta function multiplied by a sufficiently large positive constant), the prediction method is simplified to the comparison of the numbers of reported cases at the current and previous time steps. Results Our prediction method was applied to the reported cases per sentinel of influenza in Japan from 2006 to 2015 and its accuracy was 0.81 (404 correct predictions to the total 500 predictions). It was higher than that of the ARIMA models with different orders of the autoregressive part, differencing and moving-average process. In addition, a proposed method for the estimation of the number of reported cases, which is consistent with our prediction method, was better than that of the best-fitted ARIMA modelARIMA (1, 1, 0) in the sense of mean square error. Conclusions Our prediction method based on the backstepping method can be simplified to the comparison of the numbers of reported cases of the current and previous time steps. In spite of its simplicity, it can provide a good prediction for the spread of influenza in Japan.