tetano
Editor, Senior Moderator
Math Biosci Eng. 2018 Feb 1;15(1):209-232. doi: 10.3934/mbe.2018009.
[h=1]Prediction of influenza peaks in Russian cities: Comparing the accuracy of two SEIR models.[/h] Leonenko VN[SUP]1[/SUP], Ivanov SV[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
PMID: 29161833 DOI: 10.3934/mbe.2018009
[h=1]Prediction of influenza peaks in Russian cities: Comparing the accuracy of two SEIR models.[/h] Leonenko VN[SUP]1[/SUP], Ivanov SV[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] 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.
PMID: 29161833 DOI: 10.3934/mbe.2018009