Epidemiological Characteristics and Incidence Trends of Hemorrhagic Fever With Renal Syndrome in Shandong Province, China: Retrospective Observational Study With Forecasting.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is a severe natural focal infectious disease that remains a major public health concern in Shandong Province, China. Understanding the epidemiological characteristics of HFRS and forecasting its incidence are crucial to provide guidance for policymaking and implement timely interventions against this disease. OBJECTIVE: This study examined the long-term epidemiological characteristics of HFRS based on 15 years of surveillance data; the analysis used 4 time series models to predict its incidence trends. The aim was to provide a scientific basis for local disease control departments, thereby supporting more accurate prediction and effective prevention and control of HFRS in this region. METHODS: HFRS case surveillance data in Shandong Province from 2010 to 2024 were collected through the China Information System for Disease Control and Prevention. Descriptive epidemiological methods were used to analyze the basic characteristics of HFRS. Four forecasting models, namely, seasonal autoregressive integrated moving average, Prophet, the Holt-Winters exponential smoothing method, and Extreme Gradient Boosting, were implemented in R to fit the time series and generate predictions. Their predictive performance was evaluated and compared using the root mean squared error, mean absolute error, and mean absolute percentage error. RESULTS: From 2010 to 2024, a total of 15,184 HFRS cases were reported in Shandong Province, with 188 deaths. The annual incidence rate ranged from 0.45 to 1.84 per 100,000 population, showing an overall fluctuating downward trend. The peak incidence occurred during the autumn and winter months from October to December each year. Spatially, the eastern and southeastern regions of Shandong Province were the primary high-incidence areas. Demographically, the high-risk populations were mainly middle-aged and older adults, male individuals, and farmers. In the 2024 test set, the Holt-Winters model yielded the lowest prediction error metrics among the 4 models, with root mean squared error, mean absolute error, and mean absolute percentage error values of 7.56, 6.43, and 34.56%, respectively. CONCLUSIONS: The Holt-Winters model showed the lowest prediction error metrics among the 4 models, suggesting its potential utility for forecasting HFRS incidence. It is recommended to apply this model to enhance precise monitoring and implement targeted prevention and control measures focusing on key areas, peak periods, and high-risk populations.