Statistical analysis of the dynamics of antibody loss to a disease-causing agent: plague in natural populations of great gerbils as an example

被引:10
作者
Park, Siyun
Chan, Kung-Sik [1 ]
Viljugrein, Hildegunn
Nekrassova, Larissa
Suleimenov, Bakhtiyar
Ageyev, Vladimir S.
Klassovskiy, Nikolay L.
Pole, Sergey B.
Stenseth, Nils Chr.
机构
[1] Univ Iowa, Dept Stat & Actuarial Sci, Iowa City, IA 52242 USA
[2] Seoul Natl Univ, Dept Stat, Seoul 151742, South Korea
[3] Univ Oslo, Dept Biol, CEES, N-0316 Oslo, Norway
[4] M Aikimbaeva Kazakh Sci Ctr Quarantine & Zoonot D, Alma Ata 480074, Kazakhstan
关键词
continuous-time Markov chain; generalized nonlinear mixed-effect model; specific immunity; serology; Yersinia pestis;
D O I
10.1098/rsif.2006.0160
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
We propose a new stochastic framework for analysing the dynamics of the immunity response of wildlife hosts against a disease-causing agent. Our study is motivated by the need to analyse the monitoring time-series data covering the period from 1975 to 1995 on bacteriological and serological tests samples from great gerbils being the main host of Yersinia pestis in Kazakhstan. Based on a four-state continuous-time Markov chain, we derive a generalized nonlinear mixed-effect model for analysing the serological test data. The immune response of a host involves the production of antibodies in response to an antigen. Our analysis shows that great gerbils recovered from a plague infection are more likely to keep their antibodies to plague and survive throughout the summer-to-winter season than throughout the winter-to-summer season. Provided the seasonal mortality rates are similar (which seems to be the case based on a mortality analysis with abundance data), our finding indicates that the immune function of the sampled great gerbils is seasonal.
引用
收藏
页码:57 / 64
页数:8
相关论文
共 28 条
[1]   Microevolution and history of the plague bacillus, Yersinia pestis [J].
Achtman, M ;
Morelli, G ;
Zhu, PX ;
Wirth, T ;
Diehl, I ;
Kusecek, B ;
Vogler, AJ ;
Wagner, DM ;
Allender, CJ ;
Easterday, WR ;
Chenal-Francisque, V ;
Worsham, P ;
Thomson, NR ;
Parkhill, J ;
Lindler, LE ;
Carniel, E ;
Keim, P .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2004, 101 (51) :17837-17842
[2]  
[Anonymous], RODENTS DESERT ENV
[3]  
[Anonymous], 1995, MIXED EFFECTS MODELS
[4]  
Begon M, 2006, EMERG INFECT DIS, V12, P268
[5]  
Bhattacharya RN., 1990, STOCHASTIC PROCESSES
[6]  
Chan KS, 2003, STAT SINICA, V13, P207
[7]  
Cox D.A., 1968, THEORY STOCHASTIC PR
[8]   Nonlinear models for repeated measurement data: An overview and update [J].
Davidian, M ;
Giltinan, DM .
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2003, 8 (04) :387-419
[9]   Predictive thresholds for plague in Kazakhstan [J].
Davis, S ;
Begon, M ;
De Bruyn, L ;
Ageyev, VS ;
Klassovskiy, NL ;
Pole, SB ;
Viljugrein, H ;
Stenseth, NC ;
Leirs, H .
SCIENCE, 2004, 304 (5671) :736-738
[10]  
Dickmann O., 2000, MATH EPIDEMIOLOGY IN