Epidemiological characteristics and incidence trend prediction of hemorrhagic fever with renal syndrome in Jixi region, Heilongjiang Province from 2013 to 2024
10.3760/cma.j.cn311365-20250426-00126
- VernacularTitle:2013年至2024年黑龙江省鸡西地区肾综合征出血热的流行病学特征与发病趋势预测
- Author:
Zhaoqi WANG
1
;
Jinhua WANG
;
Hongbin WANG
;
Zhimin CAO
Author Information
1. 鸡西市疾病预防控制中心传染病预防控制科，鸡西 158100
- Publication Type:Journal Article
- Keywords:
Hemorrhagic fever with renal syndrome;
Autoregressive integrated moving average model prediction;
Space-time clustering;
Spatial autocorrelation analysis
- From:
Chinese Journal of Infectious Diseases
2025;43(6):326-331
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To characterize the epidemiological features of hemorrhagic fever with renal syndrome (HFRS) in Jixi region, Heilongjiang Province, and to develop an autoregressive integrated moving average (ARIMA) model to predict the incidence trend of HFRS, thus to provide a scientific basis for targeted prevention and control.Methods:HFRS case data and host animals surveillance data from January 2013 to December 2024 in Jixi City were collected. Descriptive epidemiological methods were used to analyze the temporal, spatial, and population distribution characteristics of HFRS, with comparisons made using the chi-square test. ArcGIS10.8.2 software was employed for spatial clustering analysis. Moran I was used to assess the overall spatial trend of HFRS incidence. Getis-Ord Gi * analyses was used to identify the hotspots and coldspots. SaTScan v10.2 software was applied to detect spatiotemporal clusters. The Spearman correlation analysis was used to examine the relationship between rodent viral load index and HFRS incidence. ARIMA model was constructed and optimized using Bayesian information criterion (BIC) for short-term prediction of HFRS. Results:From January 2013 to December 2024, a total of 1 045 HFRS cases were reported in Jixi City, with an average annual incidence rate of 5.09/100 000. Three epidemic peaks were observed: 2014 (8.12/100 000), 2019 (7.22/100 000), and 2023 (3.86/100 000). The temporal distribution showed a bimodal pattern, with the autumn-winter peak (October to December) having more cases than the spring-summer peak (May to June). Geographically, cases were mainly concentrated in the central-eastern urban areas of Jixi City. The average annual incidence rate in males (7.89/100 000) was significantly higher than that in females (2.24/100 000), the difference was statistically significant ( χ2=322.15, P<0.001). The highest incidence rate was observed in the 50 to 59 age group (6.79/100 000), and farmers accounted for the largest proportion of cases (59.04% (617/1 045)). Spatial analysis revealed clustered distribution (Moran I=0.50, P<0.05), with hotspots in Hulin City and Mishan City. Spatiotemporal analysis identified one significant cluster (radius=89.2 km). No correlation was found between the rodent viral load index and HFRS incidence ( r=0.455, P=0.138). The optimal short-term prediction model was ARIMA (1, 0, 0) (0, 1, 1) (BIC=-3.24), which forecasted 38 HFRS cases in Jixi City in 2025. Conclusions:HFRS incidence in Jixi City exhibits periodicity, seasonality, and spatial clustering. The developed ARIMA model provides a valuable tool for predicting incidence trends, thus to optimize vaccination, rodent control, and surveillance measures in high-risk areas and populations.
