Impact of meteorological factors on the prevalence of porcine pasteurellosis in the southcentral of Mainland China

Abstract

Using data collected from 2006 to 2014, we applied geographic information system (GIS) mapping and spatial clustering analysis to evaluate prevalence of porcine pasteurellosis in all 31 provinces of Mainland China. All provinces have been affected, but our results show that there is a very high incidence in provinces of the southcentral of Mainland China. Six provinces comprise the area and account for 14082 outbreaks or 74.66% of the total 18862 number: Guangxi (4574), Sichuan (3493), Chongqing (2443), Guangdong (1584), Guizou (1041) and Yunnan (947). This study aims to evaluate the relation between meteorological factors and number of cases of porcine pasteurellosis in the southcentral of Mainland China. Local meteorological variables and case data of porcine pasteurellosis were provided by authorities. Spearman rank correlation analysis and cross-correlation analysis were used to control for collinearity and lag effects. A zero-inflated Poisson model was used to estimate the probability of an impact of meteorological factors in the epidemiology of porcine pasteurellosis. The results of this model indicated that ENSO have a positive effect on the occurrence of the disease. And there is a positive correlation between mean monthly temperature, relative humidity of the current and previous month and the number of cases of the disease. In contrast, average wind speed of the current month negatively correlated to the number of newly reported cases. Our findings indicate that there may exist meteorological conditions in the southcentral of Mainland China that increase the risk for the appearance of porcine pasteurellosis. Moreover, these meteorological variables may be used to estimate the number of disease' cases in this region.