Abstract:Diseases represent a significant threat to the survival of tigers (Panthera tigris). Many previous studies have linked the emergence of the diseases to socio-economic, environmental and ecological factors, but none of these studies specifically analyzed the relationship between these factors and tiger case reports. This study used Excel software to make statistics and present the classification statistics of tiger case reports in the world (Table 1), as well as time-dependent change (Fig. 4, 5). The Poisson regression of SPSS software was used to analyze correlation between case reports’ number and time quantitatively. ArcGIS software was used to present the geographical distribution of tiger case reports worldwide (Fig. 1) and in China (Fig. 2). Furthermore, this study used ArcGIS software to open the spatial interpolation database derived from the ‘Resource and Environment Data Cloud Platform’ which is created by Chinese Academy of Sciences. The properties of grids where tiger distribute in China (Taiwan Province, Hong Kong and Macao special administrative regions were not counted) were export to an excel sheet, including whether the grid was tiger case reports positive, the population interpolation of the grid, the precipitation interpolation of the grid, and the air temperature interpolation of this grid. The logistic regression model in SPSS was used to quantify the association’s strength among grids where tiger case reports occurred and factors hypothesized. The response variable (yes, no) was whether the grids occurred tiger case reports. Candidate variables to fit the prediction were population, precipitation and air temperature. The result of analysis confirms that there is a significant positive correlation between tiger case reports and demographic factor (Table 2), suggesting that we should pay more attention to the prevention of tiger diseases in areas with high population density (Fig. 3). This study provides a basis for building a model to predict regions where new tiger diseases are most likely to occur.