Bayesian tracking of emerging epidemics using ensemble optimal statistical interpolation

被引：10

作者：

Cobb, Loren ^{[1
]}

Krishnamurthy, Ashok ^{[2
]}

Mandel, Jan ^{[1
]}

Beezley, Jonathan D. ^{[3
]}

机构：

[1] Univ Colorado, Dept Math & Stat Sci, Denver, CO 80217 USA

[2] Mt Royal Univ, Dept Math Phys & Engn, Calgary, AB T3E 6K6, Canada

[3] CERFACS, F-31057 Toulouse 1, France

来源：

SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY | 2014年 / 10卷

基金：

美国国家科学基金会;

关键词：

Bayesian statistical tracking; Emerging epidemics; Spatial S-I-R epidemic model; Data assimilation; Ensemble Kalman filter; Optimal statistical interpolation;

D O I：

10.1016/j.sste.2014.06.004

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

We present a preliminary test of the Ensemble Optimal Statistical Interpolation (EnOSI) method for the statistical tracking of an emerging epidemic, with a comparison to its popular relative for Bayesian data assimilation, the Ensemble Kalman Filter (EnKF). The spatial data for this test was generated by a spatial susceptible-infectious-removed (S-I-R) epidemic model of an airborne infectious disease. Both tracking methods in this test employed Poisson rather than Gaussian noise, so as to handle epidemic data more accurately. The EnOSI and EnKF tracking methods worked well on the main body of the simulated spatial epidemic, but the EnOSI was able to detect and track a distant secondary focus of infection that the EnKF missed entirely. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：39 / 48

页数：10

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