Improved nonparametric estimation for diffusion epidemiological SIR model from incomplete data

被引：0
|
作者：
Perelevskiy, Svyatoslav S. [1 ]
Pchelintsev, Evgeny A. [1 ]
机构：
[1] Tomsk State Univ, Tomsk, Russia
来源：
VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE | 2023年 / 65期
关键词：
ergodic diffusion process; SIR model; heteroscedastic regression; incomplete data; improved estimation; mean square risk; weighted estimates;
D O I：
10.17223/19988605/65/8
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper considers the problem of statistical estimation of the epidemic spread function from discrete data in a stochastic SIR model of the Kermack-Mackendrick type, in which the infection dynamics is determined by an ergodic diffusion process with an unknown diffusion coefficient. To estimate the drift function of the diffusion process, an improved procedure is proposed that has a higher convergence rate than least squares estimates. The results of Monte Carlo numerical simulation are given.
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页码：79 / 88
页数：10
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