Epidemiological characteristics of dengue fever outbreaks in Nanning, Guangxi Zhuang Autonomous Region in 2014 and 2019

  Objective   To analyze the epidemiological characteristics of dengue fever outbreaks in 2014 and 2019 in Nanning, Guangxi Zhuang Autonomous Region and provide reference for local dengue fever prevention and control.

  Methods   The incidence data of dengue fever outbreaks in Nanning in 2014 and 2019 were collected, and descriptive epidemiological analysis was conducted to compare the time, population and spatial distributions of dengue fever cases in the two outbreaks.

  Results   In 2014 outbreak, the first case occurred on June 13, and the number of cases reached its 25.00%, 50.00%, 75.00% and 100.00% on September 27, October 4, October 15 and December 8, respectively, and the daily incidence peak was on September 28 (38 cases). In 2019 outbreak, the first case occurred on April 9, and the number of cases reached its 25.00%, 50.00%, 75.00% and 100.00% on September 29, October 9, October 19 and December 13 respectively. The daily incidence peak was on October 1 (36 cases). The majority of cases in 2014 outbreak were women, but there was no gender specific difference in 2019. The cases in 2014 are more concentrated in the 20–39 age group. There were slightly more elderly cases in 2019. In 2014, the most cases were business or service categories but the most cases were housework or unemployed in 2019. Business or service cases caused the earliest dengue outbreak in 2014. However, none of the occupational types shows earlier in dengue outbreak in 2019. Nearly half of the cases in 2014 were concentrated in Xingning district with a total of 347 cases (48.06%), followed by Xixiangtang district, with a total of 235 cases (32.55%). The cases in 2019 were relatively concentrated in Jiangnan district, with a total of 451 cases (42.11%), followed by Xixiangtang district with a total of 298 cases (27.82%).

  Conclusion   Although the time distribution of the dengue outbreaks in 2014 and 2019 were similar, the characteristics of population distribution and spatial distribution are significantly different. Indicating the popularity of epidemic risks and the need to focus on implementing prevention and control measures.