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THE RELATIONSHIP BETWEEN MONTHDISEASE INCIDENCE RATE AND CLIMATIC FACTOR OF CLASSICAL SWINE FEVER

  • Hongbin Wang
  • Danning Xu
  • Jianhua Xiao
  • Ru Zhang
  • Jing Dong
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 294)

Abstract

The Swine Fever is a kind of acute, highly infective epidemic disease of animals; it is name as Classical Swine Fever (CSF) by World animal Health organization. Meteorological factors such as temperature, air pressure and rainfall affect the epidemic of CSF significantly through intermediary agent and CSF viral directly. However there is significant difference among different region for mode of effects. Accordingly, the analyze must adopt different methods. The dependability between incidence rate each month of CSF and meteorological factors from 1999 to 2004 was analyzed in this paper. The function of meteorological factors on CSF was explored and internal law was expected to be discovered. The correlation between the incidence rate of Swine Fever and meteorological factors, thus the foundation analysis of the early warning and the decision-making was made, the result indicated that the incidence rate of CSF has negative correlation with temperature, rainfall, cloudage; relative humidity has positive correlation with disease; for air pressure, except average air pressure of one month, other air pressure factors have positive correlation with disease; for wind speed, except Difference among moths of wind speed and average temperature of one month. have positive correlation with disease, other wind speed factors has negative correlation with disease.

Keywords

Wind Speed Incidence Rate Wild Boar Meteorological Factor Classical Swine Fever 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. M Artois, KR Depner, V Guberti, Classical swine fever (hog cholera) in wild boar in Europe. Rev Sci Tech. 2002, 21(2):287∼303Google Scholar
  2. TA Abeku, SJ Vlas, G Borsboom, et al. Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas od Ethiopia: a simple seasonal adjustment method performs best. Trop Med Int Heath. 2002. 7(10):851∼857CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Hongbin Wang
    • 1
  • Danning Xu
    • 1
  • Jianhua Xiao
    • 1
  • Ru Zhang
    • 1
  • Jing Dong
    • 1
  1. 1.NorthEast Agricultural UniversityHarbinChina

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