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  • 标题:Predicting the local dynamics of epizootic rabies among raccoons in the United States
  • 本地全文:下载
  • 作者:James E. Childs ; Aaron T. Curns ; Meghan E. Dey
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2000
  • 卷号:97
  • 期号:25
  • 页码:13666-13671
  • DOI:10.1073/pnas.240326697
  • 语种:English
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:Mathematical models have been developed to explore the population dynamics of viral diseases among wildlife. However, assessing the predictions stemming from these models with wildlife databases adequate in size and temporal duration is uncommon. An epizootic of raccoon rabies that began in the mid-Atlantic region of the United States in the late 1970s has developed into one of the largest and most extensive in the history of wildlife rabies. We analyzed the dynamics of local epizootics at the county level by examining a database spanning more than 20 years and including 35,387 rabid raccoons. The size, number, and periodicity of rabies epizootics among raccoons were compared with predictions derived from a susceptible, exposed, infectious, and recovered model of raccoon rabies [Coyne, J., Smith, G. & McAllister, F. E. (1989) Am. J. Vet. Res. 50, 2148-2154]. After our methods for defining epizootics were applied to solutions of the model, the time series revealed recurrent epizootics in some counties, with a median first epizootic period of 48 months. Successive epizootics declined in size and the epizootic period progressively decreased. Our reanalysis of the model predicted the initial-epizootic period of 4-5 years, with a progressive dampening of epizootic size and progressive decrease in epizootic period. The best quantitative agreement between data and model assumed low levels of immunity (1-5%) within raccoon populations, suggesting that raccoons develop little or no rabies immune class. These results encourage the use of data obtained through wildlife surveillance in assessing and refining epidemic models for wildlife diseases.
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