ABSTRACT
Objective To understand the spatial and temporal distribution characteristics of dengue fever in China from 2011 to 2018, and predict the incidence of dengue fever in China in 2019. Methods Based on the case data of dengue fever in China from 2011 to 2018 in the Chinese Disease Prevention and Control Information System, the trend of dengue fever was described and predicted by using the autoregressive integrated moving average model (ARIMA) with R 3.6.0 software. Based on the data of the incidence of dengue fever in the country, provinces and cities from 2011 to 2016 provided by the national scientific data sharing platform for population and health, global and local spatial autocorrelation analysis was performed using GeoDa 1.12 software to determine the dengue fever hotspots. Results The incidence of dengue fever was 14 302 in 2019, showing no disease outbreaks. The incidence of dengue fever in 2012(Moran’s I=-0.088, P=0.037), 2013(Moran’s I=-0.121, P=0.040) and 2014(Moran’s I=-0.076, P=0.045) showed a global spatial negatively correlaton. In 2016(Moran’s I=0.078, P=0.048), the incidence of dengue fever was positively correlated with global space. The results of local autocorrelation analysis showed that the high incidence of dengue fever was mainly in the southeast coastal areas of China. Conclusions In 2019, the epidemic of dengue fever in China showed no obvious fluctuation trend, and the epidemic situation showed spatial clustering distribution.
ABSTRACT
Objective To explore the spatial distribution of measles from 2013 through 2015 in Liaoning province,China and to provide references for measles control and prevention. Methods The GeoDa 1.4. 6 program was used to conduct exploratory spatial data analysis to identify the spatial distribution characteristics and pattern of measles in Liaoning province. Results The frequency analysis showed that the measles epidemic situation appeared to have significant positive skewing within 105 counties of Liaoning province in each year from 2013 through 2015. The global trend analysis indicated a balanced trend in 2013 and 2015,and that the high incidence measles areas were located in the eastern and northern provincial regions in 2014. The global Moran'sⅠwas 0.294 5,0.391 9,and 0.147 7,and general G values were 0.015 9,0.012 0,and 0.013 5,revealing a positive spatial autocorrelation and a high-high aggregation model for each year between 2013 and 2015. The local spatial autocorrelation analysis recognized 5 core areas and 25 hot-spot counties with a high incidence of the measles epidemic,mainly distributed in Shenyang,Fuxin,Tieling,Fushun,Benxi,Liaoyang,Panjin,and Huludao. Conclusion Measles cases were clustered geographically in Liaoning province from 2013 through 2015. Spatial epidemiology methods may offer insights for the epidemiologic study of measles.