1. Lu County Center for Disease Control and Prevention, Luzhou, Sichuan 646100, China; 2. Sichuan Provincial Center for Disease Control and Prevention, Chengdu, Sichuan 610041, China; 3. Ya’an Municipal Center for Disease Control and Prevention, Ya’an, Sichuan 625000, China
Abstract:Objective To describe the temporal and spatial epidemiological characteristics of mumps in Luzhou City from 2010 to 2020, to construct the ARIMA time series model for short-term prediction, and to provide a reference basis for comprehensive prevention and control of mumps. Methods Microsoft Excel 2020 was used to sort the epidemic data and draw the statistical map. ArcGIS 10.6 was employed to construct a spatial distribution map, and the spatial autocorrelation was analyzed. Eviews 10 was applied to constructing the ARIMA time series model of monthly incidence. The temporal and spatial epidemiological characteristics of mumps in Luzhou City from 2010 to 2020 were analyzed, and the short-term incidence trend was predicted. Results Mumps cases were reported in 7 districts and counties in Luzhou City from 2010 to 2020. Districts and counties with a high incidence of mumps were different in each year, and there was no spatial autocorrelation in each year. The annual average reported incidence rate was 22.17/100,000. A high incidence of mumps was found in 2012 (37.51/100,000), 2013 (35.72/100,000) and 2019 (31.80/100,000), but a low incidence was found in 2015 (10.90/100,000). On the whole, there were two seasonal peaks from April to July and from November to January of the following year, and the main onset months were from April to July. The ARIMA (1,1,1)(1,1,2)12 model constructed was the best model, which predicted that the number ofreported cases from April to December in 2021 was slightly lower than that of the corresponding period in 2020. Conclusion The incidence rates of mumps in Luzhou City from 2010 to 2020 generally showed a downward trend, and there was no spatial aggregation. The ARIMA(1,1,1)(1,1,2)12 model can be used for time series fitting and short-term prediction. The presented spatiotemporal distribution characteristics and incidence trend can provide references for prevention and control of the disease.