Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease, with rodents serving as the primary source of infection [1,2]. Humans are commonly infected through contact with rodent excreta or the resulting aerosols; currently, there is no conclusive evidence of human-to-human transmission of HFRS [3,4]. The disease is primarily characterized by acute fever, hemorrhage, and renal dysfunction, which can lead to a substantial disease burden [5]. HFRS first emerged during World War I and is now distributed across more than 70 countries and regions worldwide, although it is predominantly endemic to Eurasia [6]. China experiences the most severe HFRS epidemics worldwide, with cases having been reported in all 31 provinces (including autonomous regions and municipalities) [7]. The transmission of HFRS is jointly influenced by ecological, meteorological, and socioeconomic factors [8]. Meteorological factors such as temperature, precipitation, and humidity can directly affect rodent population dynamics as well as the survival and transmission of the virus in the environment [9]. Meanwhile, socioeconomic factors, including land use, urbanization, and human behaviors, also impact the risk of human exposure [10]. HFRS is an acute infectious disease caused by various genotypes of hantaviruses [11]. HFRS endemic areas in China are primarily classified into 3 categories, based on virus types and host animal species: the wild rodent–type endemic area, where HFRS is caused by the Hantaan virus (HTNV) and primarily involves dominant wild rodent species such as the striped field mouse; the domestic rodent–type endemic area, where HFRS is caused by the Seoul virus (SEOV) and primarily involves dominant domestic rodent species such as the brown rat; and the mixed-type endemic area, where both types coexist [12].

Shandong Province is one of the primary endemic areas for HFRS in China. Since the identification of the first case in 1968, cases have been reported annually [13-15]. Despite long-term efforts in surveillance, prevention, and control, HFRS in Shandong Province continues to exhibit complex temporal fluctuations, including seasonal peaks and multiyear cyclical variations [16]. Recent studies also suggest that the prevalence of HFRS in Shandong Province is characterized by distinct spatial heterogeneity and environment-driven features, highlighting the need to further enhance surveillance and early warning capabilities [17]. In 2004, China established a unified national web-based direct reporting system for infectious diseases, enabling real-time epidemic monitoring through a hierarchical reporting structure, which has laid a solid foundation for the forecasting and early warning of infectious diseases. Reliable forecasting results help optimize the allocation of health care resources, facilitate the timely issuance of health advisories, and enable the strengthening of vector control prior to the onset of anticipated epidemic peaks [18,19]. Time series modeling has emerged as a crucial method for predicting infectious disease epidemics [20]. Traditional models such as the seasonal autoregressive integrated moving average (SARIMA) model have been widely used to capture linear patterns and seasonal dependencies within HFRS incidence data [21]. Furthermore, exponential smoothing methods such as the Holt-Winters method offer considerable simplicity and practicality in handling trends and seasonality [22]. In recent years, the application of highly complex algorithmic models, including machine learning, has further enriched this research domain [23]. For instance, the Prophet model is well suited for time series with pronounced seasonal characteristics, whereas the Extreme Gradient Boosting (XGBoost) model demonstrates robust potential in forecasting structured epidemiological data [21,24]. However, in highly endemic areas for HFRS such as Shandong, research that systematically characterizes long-term epidemic patterns while simultaneously comparing the performance of classic and modern forecasting models remains relatively limited. Such studies are of profound significance for identifying forecasting tools that better meet the practical demands of public health.

Given the persistent prevalence of HFRS in Shandong Province and the lack of a systematic evaluation of time series forecasting models, this study conducted an in-depth analysis of the long-term epidemiological characteristics of HFRS based on 15 years of surveillance data and 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.

As a major endemic region for HFRS in China, Shandong Province has experienced an overall decline in incidence in recent years. However, HFRS’s seasonality, spatial clustering, and high-risk population characteristics remain significant, indicating that the underlying endemicity has not fundamentally changed [17]. To further translate epidemiological research into practical prevention, control, and early warning capabilities, this study systematically analyzed 15 years of HFRS surveillance data in Shandong Province and comparatively evaluated 4 prediction models. The results indicate significant spatiotemporal heterogeneity in the transmission of HFRS in Shandong Province, with the Holt-Winters model demonstrating superior short-term predictive performance. These findings contribute to a deeper understanding of the epidemiological characteristics of HFRS and provide a methodological reference for implementing routine surveillance and early warning. Unlike our previous studies [16,17], which mainly focused on spatiotemporal patterns and driving factors of HFRS, the present study examined the long-term epidemiological trends of HFRS in Shandong Province based on a 15-year time series dataset (2010-2024). This extended observation window enabled a more systematic profiling of temporal dynamics, spatial heterogeneity, and demographic changes, together with the evaluation of multiple time series forecasting models. The present study addressed a distinct research question and provided complementary, nonredundant rather than repetitive evidence.

In terms of long-term epidemic trends, this study found that the incidence of HFRS in Shandong Province exhibited a fluctuating downward trend, consistent with the overall epidemic trend in China in recent years [33,34]. Previous studies have indicated that although China still bears the heaviest global burden of HFRS, the national incidence has gradually decreased over the past decades. This decline may be attributed to multiple factors, including the promotion of vaccination, implementation of rodent control measures, improvement of living conditions, and strengthening of disease surveillance [35]. However, the decline in HFRS incidence does not imply the disappearance of its transmission risk as Shandong still possesses the ecological foundation to sustain endemicity in terms of geographical environment, host animal distribution, and population exposure patterns [14]. Furthermore, this study revealed that reported HFRS cases were primarily clustered between October and December, which differs from the previously bimodal seasonal distribution reported in Shandong Province [17]. This finding indicates a potential shift in the recent seasonal pattern of HFRS toward autumn-winter predominance. Previous studies have shown that HFRS seasonality may be influenced by changes in rodent host population composition, circulating hantavirus types, and opportunities for human exposure [36]. Typically, HTNV-related HFRS cases peak in autumn and winter, whereas SEOV-associated cases tend to show a spring peak [1,37]. Accordingly, the diminished spring incidence peak observed in our study may be attributable to altered predominance of hantavirus types, the spatial distribution of rodent hosts, and seasonal patterns of human-rodent contact. However, the precise mechanisms remain to be explored through further virological and ecological investigations for validation.

HFRS has long been influenced by ecological environments and socioeconomic conditions, which leads to disparities in its regional distribution [8,38]. This study revealed that high-incidence counties for HFRS were primarily located in eastern and southeastern Shandong, whereas both the geographic extent of the epidemic and the number of high-incidence counties gradually decreased in recent years. Previous studies have shown that HFRS occurrence is associated with altitude, land use, and population density, suggesting that ecological suitability for rodent hosts and opportunities for human exposure may jointly influence infection risk [14]. Additional ecological studies in Shandong further showed that land cover and elevation were closely associated with the distribution of hantavirus-infected rodent hosts, whereas southeastern Shandong has been identified as an important natural focus characterized by the presence of striped field mouse and HTNV [39]. Agricultural field activities may further increase opportunities for contact with infected rodents and their contaminated environments [40]. Such ecological and occupational factors may partly contribute to the relatively high incidence observed in eastern and southeastern Shandong. Previous studies have also demonstrated substantial spatial heterogeneity in HFRS across Shandong Province, indicating that provincial-level unified prevention and control measures are insufficient; geographically tailored and risk-stratified intervention strategies should therefore be prioritized [16]. Additionally, consistent with previous findings, HFRS cases were predominantly among middle-aged and older adult male individuals, with farmers representing the highest proportion in occupational distribution [24,41]. The differential distribution of HFRS among the population is likely driven by variations in exposure opportunities. Male farmers often engage in outdoor cultivation, leading to more frequent contact with rodents or their excreta, which suggests that agricultural production settings remain the core focus for HFRS prevention and control [42]. Furthermore, the outflow of young rural labor, the aging of the agricultural workforce, and the fact that HFRS vaccines are not administered to individuals over 60 years of age may also contribute to the high proportion of middle-aged and older adult individuals among HFRS cases [43]. This suggests that middle-aged and older adult male individuals in rural areas should be the primary targets for health education, advocating for active HFRS vaccination and the use of personal protection during labor.

This study shows that all 4 models can effectively capture the seasonal fluctuations of HFRS; however, the Holt-Winters model yielded the lowest RMSE, MAE, and MAPE, outperforming the SARIMA, Prophet, and XGBoost models. This result suggests that for monthly case sequences with a clear annual cycle, a relatively stable long-term trend, and a limited training sample size, structurally simple exponential smoothing methods still possess significant practical value. By separately smoothing and updating the level, trend, and seasonal components, the Holt-Winters model can stably track monthly fluctuations without introducing excessive parameters, making it particularly suitable for data dominated by significant seasonality rather than abrupt changes [30,44]. The overall performance of the SARIMA model was slightly inferior to that of the Holt-Winters model, yet it remains one of the most widely used models in previous HFRS time series analyses. For instance, research in Jiaonan County, Shandong Province, has shown that SARIMA could accurately predict local monthly HFRS incidence, and national-scale studies have also demonstrated its ability to effectively simulate long-term seasonal fluctuations of HFRS [45,46]. The disadvantage of SARIMA lies in its reliance on stationarity and linearity assumptions, and its parameter identification is highly dependent on the sequence structure; thus, its extrapolation capability may be limited when the epidemic is influenced by policy changes or abnormal exposure events [23]. The Prophet model is suitable for time series with prominent trend shifts or holiday disturbances [47]. However, as the changes in monthly HFRS cases primarily reflect fixed seasonality and local volatility, the advantages of the Prophet model were not fully realized in this study. The XGBoost model performed the worst in this study, which contradicts the “naturally superior” expectation some scholars hold for machine learning models. The advantages of machine learning methods are mainly reflected in handling complex nonlinear relationships and high-dimensional features, but their performance typically depends on large training sample sizes, sufficient feature engineering, and the inclusion of external variables. This study modeled HFRS using only monthly case data without incorporating features such as meteorology, host density, or land use that could improve nonlinear fitting capabilities; therefore, the XGBoost model could not fully leverage its strengths. In addition, the residuals of the Prophet and XGBoost models exhibited significant autocorrelation, indicating that neither model fully captured the temporal dependencies in HFRS incidence. This may be attributable to the complex temporal dynamics of HFRS, including evolving seasonal patterns, interannual fluctuations, and occasional abrupt changes in incidence. Therefore, although these models were able to fit the overall temporal trends reasonably well, they remained somewhat limited in characterizing fine-scale temporal variations.

This study also has certain limitations. First, the HFRS case data were obtained from a passive surveillance information system, which may involve reporting biases such as underreporting and diagnostic delays. Second, although the 2024 test set covered a complete annual seasonal cycle, its relatively limited sample size may have affected the robustness of the model comparisons to some extent. Future studies should consider using data spanning longer periods for validation to further confirm the reliability of the models. Finally, to ensure consistency in the input variables across the 4 forecasting models and assess their applicability under routine surveillance conditions, the XGBoost model in this study incorporated only historical monthly HFRS case counts. However, this univariate design may constrain the advantage of XGBoost in capturing multivariable nonlinear relationships. Future studies should further explore incorporating meteorological conditions, rodent dynamics, ecological and environmental factors, and socioeconomic indicators into multivariable models to further improve prediction performance.

In conclusion, the HFRS epidemic in Shandong Province features low overall incidence at the provincial level, whereas specific local foci continue to exhibit high incidence. The incidence of HFRS shows significant spatiotemporal clustering, including distinct autumn and winter epidemic peaks, obvious geographical concentrations in eastern and southeastern Shandong, and high-risk groups dominated by middle-aged and older adult male farmers. This suggests that future prevention and control efforts should focus more on spatially precise strategies and targeted interventions for high-risk populations. For short-term forecasting based on monthly surveillance data, the Holt-Winters model yielded the lowest prediction error metrics among the evaluated models and may be a useful practical tool for routine provincial HFRS early warning. The results of this study have important implications for the precise monitoring and scientific prevention and control of regional HFRS.