[Epidemiological and spatial-temporal distribution of several natural focus diseases in Gansu province, 2014-2018]

Objective: To analyze the epidemiological and spatial-temporal distribution of Brucellosis, epidemic encephalitis B and hemorrhagic fever with renal syndrome (HFRS) in Gansu province during 2014-2018 so as to provide evidence for the prevention and control of those diseases. Methods: A database was established in Gansu province from 2014 to 2018, using the geographical information system. A spatial distribution map was drawn, with trend analysis and space-time clustering used to study the 3-dimention of the diseases, by using both ArcGIS 10.5 and SaTScan 9.6 softwares. Results: Results from the trend surface analysis showed that the incidence of Brucellosis decreased gradually from north to south parts while the U type curve could reflect the distribution from the east to the west areas. Incidence of epidemic encephalitis B decreased significantly from south to north areas in the province, with incidence higher in the eastern than in the mid-west region. Difference on the incidence of HFRS was not significantly visible in the eastern and western regions, while the incidence was slightly higher in the southern than the northern parts of the province. Spatial and space-time clustering did exist among the 3 diseases in Gansu from 2014 to 2018. The areas with clusters of Brucellosis appeared in the eastern parts during 2014-2015, including 19 counties. The areas with secondary clusters of Brucellosis were seen in the Hexi district, including 4 counties, during 2017-2018. The areas with high incidence of epidemic encephalitis B were clustered in the middle and southeast areas, including 32 counties, during 2017-2018. Areas with most clusters of HFRS appeared in Min county of Dingxi city in 2018, with the areas of secondary clusters in 8 counties of the eastern areas in 2018. Conclusions: The overall incidence rates of the 3 natural focus diseases were in a upward trend and showing obvious characteristics on spatial clustering. According to the distributive characteristics, effective measures should be developed accordingly.