Dual seasonal pattern for hemorrhagic fever with renal syndrome and its potential determinants in China

Scientometric analysis of research trends in hemorrhagic fever with renal syndrome: A historical review and network visualization

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) research has undergone significant global transformation over the past decades. A comprehensive scientometric overview of research trends and scholarly cooperation in HFRS is absent. This study employs scientometric analysis to map the evolution of research themes, identify widely and scarcely explored areas, and anticipate future research directions.

METHODS

We searched Web of Science Core Collection from inception until July 31, 2023, identifying 3908 HFRS-related studies published for analysis. Utilizing CiteSpace, VOSviewer, and Bibliometrix, we performed co-authorship, co-occurrence, and co-citation analyses, and visualized research networks.

RESULTS

Our analysis revealed a consistent upward trend in HFRS publications since 1980, with an average growth rate of 11.34 %. The United States led in publication and citation counts, followed by China, Finland, Germany, and Sweden. Through co-occurrence analysis, we categorized keywords into eight clusters and 24 sub-clusters, revealing six predominant research themes: Clinical Features, Epidemiology, Mechanisms, Virus, Evolution, and Host. Notably, while themes such as Virus and Pathogenesis have been extensively studied, others, including certain aspects of Host research and Environmental Factors, remain less explored.

CONCLUSION

This scientometric synthesis provides a global perspective on the breadth and depth of HFRS research, highlighting well-trodden and understudied areas. It offers a roadmap for researchers to navigate the evolving landscape of HFRS studies and prioritize areas ripe for future investigation.

Epidemiological Characteristics of Hemorrhagic Fever with Renal Syndrome in Longyou County, China

(1) Background: We aimed to analyze the epidemiological characteristics of hemorrhagic fever with renal syndrome (HFRS) in Longyou County and to provide a basis for the future response to this disease. (2) Methods: Data on hemorrhagic fever and host animals were collected from 2011 to 2023. Descriptive methods were used to analyze the epidemic. The R4.4.1 software was used to show how the host density relates to the virus levels, temperature, and rainfall and to predict the host density. (3) Results: We observed 58 cases of hemorrhagic fever, the majority of which occurred in farmers. There were two incidence peaks each year during the spring and winter seasons, accounting for 22.41% and 43.10% of the total cases, respectively. The outdoor rodent population density was significantly and positively correlated with the outdoor rodent virus prevalence (R2 = 0.9411), serving as a robust predictor of the outdoor rodent virus prevalence. Additionally, the density of outdoor rodents exhibited a strong nonlinear relationship with the temperature and precipitation. (4) Conclusions: After hemorrhagic fever vaccination, rodent population density control, and rodent carrier rodent control from 1995 to 2000, the hemorrhagic fever epidemic was generally stable, and the epidemiological characteristics remained stable. In the future, we should continue to take active and effective comprehensive measures to intervene, further realize the effective control of HFRS, and prevent the recurrence of hemorrhagic fever epidemics.

Relationship between meteorological factors and the incidence of severe fever with thrombocytopenia syndrome: a systematic review and meta-analysis

OBJECTIVE

Although meteorological factors are connected with severe fever with thrombocytopenia syndrome (SFTS) incidence, available findings have been inconsistent. This study was performed to systematically evaluate the correlation between meteorological factors and SFTS incidence.

METHODS

We performed a thorough literature search in PubMed, Web of Science, Embase, Cochrane Library, and Chinese databases from databases initiatives to November 30, 2024. Literature was searched for correlation between meteorological factors and SFTS incidence. Two researchers screened the retrieved literature based on exclusion and inclusion criteria. Finally, data extraction and quality evaluation were carried out for the included literature, and meta-analysis was executed applying the R package (4.4.1).

RESULTS

A total of 404 relevant literature were retrieved, and 12 studies were enrolled in the meta-analysis. Both average temperature (rs=0.73, 95%CI 0.63-0.81, P<0.001), average relative humidity (rs=0.46, 95%CI 0.32-0.57, P < 0.001), cumulative precipitation (rs=0.49, 95%CI 0.33-0.62, P < 0.001), average precipitation (rs=0.48, 95%CI 0.21-0.68, P < 0.001), and sunlight (rs=0.34, 95%CI 0.11-0.53, P < 0.01) were positively correlated with SFTS incidence. The average atmospheric pressure was negatively correlated with SFTS incidence (rs= -0.69, 95%CI -0.78- -0.59, P < 0.001), and the average wind speed was not significantly correlated with SFTS incidence (P > 0.05).

CONCLUSIONS

Factors such as temperature, humidity, precipitation, sunshine duration, and atmospheric pressure are related to the incidence of SFTS with a certain lag effect. Future studies on the relationship between meteorological factors and the incidence of SFTS should fully consider human activities and environmental factors, and explore the pathogenesis and transmission mechanisms in greater depth, so as to provide targeted preventive measures.

CLINICAL TRIAL NUMBER

Not applicable.

Risk of hemorrhagic fever with renal syndrome associated with meteorological factors in diverse epidemic regions: a nationwide longitudinal study in China

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) is a climate-sensitive zoonotic disease that poses a significant public health burden worldwide. While previous studies have established associations between meteorological factors and HFRS incidence, there remains a critical knowledge gap regarding the heterogeneity of these effects across diverse epidemic regions. Addressing this gap is essential for developing region-specific prevention and control strategies. This study conducted a national investigation to examine the associations between meteorological factors and HFRS in three distinct epidemic regions.

METHODS

We collected daily meteorological data (temperature and relative humidity) and HFRS incidence cases of 285 cities in China from the Resource and Environment Science and Data Center and the Chinese National Notifiable Infectious Disease Reporting Information System from 2005-2022. Study locations were stratified into three distinct epidemic categories (Rattus-dominant, Apodemus-dominant, and mixed) based on the seasonality of peak incidence. The associations between meteorological variables and HFRS incidence were investigated using a time-stratified case-crossover design combined with distributed lag nonlinear modeling for each epidemic category.

RESULTS

The exposure-response relationships between meteorological factors and HFRS incidence revealed significant heterogeneity across epidemic regions, as evidenced by Cochran's Q test for temperature (Q = 324.40, P < 0.01) and relative humidity (Q = 30.57, P < 0.01). The optimal daily average temperature for HFRS transmission in Rattus-dominant epidemic regions (- 6.6 °C), characterized by spring epidemics, was lower than that observed in Apodemus-dominant epidemic regions (13.7 °C), where primary cases occurred during autumn and winter months. Furthermore, the association between relative humidity and HFRS incidence exhibited as a monotonic negative correlation in Rattus-dominant regions, while Apodemus-dominant regions showed a nonlinear, inverted U-shaped association.

CONCLUSIONS

This study highlights the heterogeneous effects of meteorological factors on HFRS incidence across different epidemic regions. Targeted preventive measures should be taken during cold and dry spring days in Rattus-dominant regions, and during warm and moderately humid winter days in Apodemus-dominant regions. In mixed epidemic regions, both scenarios require attention. These findings provide novel scientific evidence for the formulation and implementation of region-specific HFRS prevention policies.

Distribution dynamics and urbanization-related factors of Hantaan and Seoul virus infections in China between 2001 and 2020: A machine learning modelling analysis

Objectives

The epidemical and clinical features of distinct hantavirus infections exhibit heterogeneity. However, the evolving epidemics and distinct determines of the two hantavirus infections remain uncertain.

Methods

Data on hemorrhagic fever with renal syndrome (HFRS) cases and genotyping were collected from multiple sources to explore the distribution dynamics of different endemic categories. Four modelling algorithms were used to examine the relationship between infected hantavirus genotypes in HFRS patients, as well as assess the impacts of urbanization-related factors on HFRS incidence.

Results

The number of cities dominated by Hantaan (HTNV) and Seoul (SEOV) viruses was projected to decrease between two phases, while the mixed endemic cities increased. Patients with SEOV infection predominantly presented gastrointestinal symptoms. The modeling analysis revealed that built-up land and real GDP demonstrated the highest contribution to HTNV and SEOV infections, respectively. The impact of nightlight index and park green land was more pronounced in HTNV-dominant cities, while cropland, impervious surface, and floor space of commercialized buildings sold contributed more to HFRS incidence in SEOV-dominant cities.

Conclusions

Our findings fill a gap for the three endemic categories of HFRS, which may guide the development of targeted prevention and control measures under the conditions of urbanization development.

Investigating the impact of climatic and environmental factors on HFRS prevalence in Anhui Province, China, using satellite and reanalysis data

Introduction

Hemorrhagic Fever with Renal Syndrome (HFRS) is the most commonly diagnosed zoonosis in Asia. Despite taking various preventive measures, HFRS remains prevalent across multiple regions in China. This study aims to investigate the impact of climatic and environmental factors on the prevalence of HFRS in Anhui Province, China, utilizing satellite and reanalysis data.

Methods

We collect monthly HFRS data from Anhui Province spanning 2005 to 2019 and integrated MODIS satellite datasets and ERA5 reanalysis data, including variables such as precipitation, temperature, humidity, solar radiation, aerosol optical depth (AOD), and Normalized Difference Vegetation Index (NDVI). Continuous wavelet transform, Spearman correlation analysis, and Poisson regression analysis are employed to assess the association between climatic and environmental factors and HFRS cases.

Results

Our findings reveal that HFRS cases predominantly occur during the spring and winter seasons, with the highest peak intensity observed in a 9-year cycle. Notably, the monthly average relative humidity exhibits a Spearman correlation coefficient of 0.404 at a 4-month lag, taking precedence over other contributing factors. Poisson regression analysis elucidates that NDVI at a 2-month lag, mean temperature (T) and solar radiation (SR) at a 4-month lag, precipitation (P), relative humidity (RH), and AOD at a 5-month lag exhibit the most robust explanatory power for HFRS occurrence. Moreover, the developed predictive model exhibiting commendable accuracy.

Discussion

This study provides key evidence for understanding how climatic and environmental factors influence the transmission of HFRS at the provincial scale. Insights from this research are critical for formulating effective preventive strategies and serving as a resource for HFRS prevention and control efforts.

DNA Vaccines Encoding HTNV GP-Derived Th Epitopes Benefited from a LAMP-Targeting Strategy and Established Cellular Immunoprotection

Vaccines has long been the focus of antiviral immunotherapy research. Viral epitopes are thought to be useful biomarkers for immunotherapy (both antibody-based and cellular). In this study, we designed a novel vaccine molecule, the Hantaan virus (HTNV) glycoprotein (GP) tandem Th epitope molecule (named the Gnc molecule), in silico. Subsequently, computer analysis was used to conduct a comprehensive and in-depth study of the various properties of the molecule and its effects as a vaccine molecule in the body. The Gnc molecule was designed for DNA vaccines and optimized with a lysosomal-targeting membrane protein (LAMP) strategy. The effects of GP-derived Th epitopes and multiepitope vaccines were initially verified in animals. Our research has resulted in the design of two vaccines based on effective antiviral immune targets. The effectiveness of molecular therapies has also been preliminarily demonstrated in silico and in laboratory animals, which lays a foundation for the application of a vaccines strategy in the field of antivirals.

The 2016 Severe Floods and Incidence of Hemorrhagic Fever With Renal Syndrome in the Yangtze River Basin

Importance

Hemorrhagic fever with renal syndrome (HFRS), a neglected zoonotic disease, has received only short-term attention in postflood prevention and control initiatives, possibly because of a lack of evidence regarding the long-term association of flooding with HFRS.

Objectives

To quantify the association between severe floods and long-term incidence of HFRS in the Yangtze River basin and to examine the modifying role of geographical factors in this association.

Design, Setting, and Participants

This cross-sectional study collected data on HFRS cases between July 1, 2013, and June 30, 2019, from 58 cities in 4 provinces (Anhui, Hubei, Hunan, and Jiangxi) in the Yangtze River basin of China, with a breakpoint of flooding in July 2016, generating monthly data. The 3 years after July 2016 were defined as the postflood period, while the 3 years before the breakpoint were defined as the control period. Statistical analysis was performed from October to December 2023.

Exposures

City-level monthly flooding, elevation, ruggedness index, and closest distance from each city to the Yangtze River and its tributaries.

Main Outcomes and Measures

The primary outcomes were the number of city-level monthly HFRS cases and the number of type 1 (spring or summer) and type 2 (autumn or winter) HFRS cases.

Results

A total of 11 745 patients with HFRS were reported during the study period: 5216 patients (mean [SD] age, 47.1 [16.2] years; 3737 men [71.6%]) in the control period and 6529 patients (mean [SD] age, 49.8 [15.8] years; 4672 men [71.6%]) in the postflood period. The pooled effects of interrupted time series analysis indicated a long-term association between flooding and HFRS incidence (odds ratio, 1.38; 95% CI, 1.13-1.68), with type 1 cases being at highest risk (odds ratio, 1.71; 95% CI, 1.40-2.09). The metaregression results indicated that elevation and ruggedness index were negatively associated with the risk of HFRS, while the distance to rivers interacted with these associations.

Conclusions and Relevance

This cross-sectional study of the long-term association between flooding and HFRS incidence, as well as the modification effects of geographical factors, suggests that severe floods were associated with an increased risk of HFRS within 3 years. This study provides evidence for the development of HFRS prevention and control strategies after floods.

Spatial and temporal patterns of haemorrhagic fever with renal syndrome (HFRS) and the impact of environmental drivers in a border area of the Russian Far East

AIMS

Haemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease transmitted by rodents. The distribution of HFRS in the European part of Russia has been studied quite well; however, much less is known about the endemic area in the Russian Far East. The mutual influence of the epidemic situation in the border regions and the possibility of cross-border transmission of infection remain poorly understood. This study aims to identify the spatiotemporal hot spots of the incidence and the impact of environmental drivers on the HFRS distribution in the Russian Far East.

METHODS AND RESULTS

A two-scale study design was performed. Kulldorf's spatial scan statistic was used to conduct spatiotemporal analysis at a regional scale from 2000 to 2020. In addition, an ecological niche model based on maximum entropy was applied to analyse the contribution of various factors and identify spatial favourability at the local scale. One spatiotemporal cluster that existed from 2002 to 2011 and located in the border area and one pure temporal cluster from 2004 to 2007 were revealed. The best suitability for orthohantavirus persistence was found along rivers, including those at the Chinese-Russian border, and was mainly explained by land cover, NDVI (as an indicator of vegetation density and greenness) and elevation.

CONCLUSIONS

Despite the stable incidence in recent years in, targeted prevention strategies are still needed due to the high potential for HRFS distribution in the southeast of the Russian Far East.

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model

Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = - 9.568%, 95% CI - 16.165 to - 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = - 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = - 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = - 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = - 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(-), ∆AP(+), ∆MWV(+), and ∆ASH(-) at 0-2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact.

Development and validation of a novel death risk stratification scale in patients with hemorrhagic fever with renal syndrome: a 14-year ambispective cohort study

OBJECTIVES

To develop and validate a simple and effective death risk stratification scale for hemorrhagic fever with renal syndrome (HFRS).

METHODS

In this ambispective cohort study, we investigated the epidemiological and clinical data of 2245 patients with HFRS (1873 enrolled retrospectively and constituting the training cohort, 372 prospectively recruited as the validation cohort) from September 2008 to December 2021, and identified independent risk factors for 30-day death of HFRS. Using logistic regression analysis, a nomogram prediction model was established and was further simplified into a novel scoring scale. Calibration plot, receiver operating characteristic curve, net reclassification index, integrated discrimination index, and decision curve analysis were used to assess the calibration, discrimination, precision, and clinical utility in both training and validation cohorts.

RESULTS

Of 2245 patients with HFRS, 132 (5.9%) died during hospitalization. The nomogram prediction model and scoring scale were developed using six predictors: comorbid hypertension, hypotensive shock, hypoxemia, neutrophils, aspartate aminotransferase, and activated partial thromboplastin time. Both the scale and nomogram were well calibrated (near-diagonal calibration curves) and demonstrated significant predictive values (areas under receiver operating characteristic curves >0.9, sensitivity and specificity >90% in the training cohort and >84% in the validation cohort). The simplified scoring scale demonstrated equivalent discriminative ability to the nomogram, with net reclassification index and integrated discrimination index of 0.022 and 0.007 in the training cohort, 0.126 and 0.022 in the validation cohort. Decision curve analysis graphically represented significant clinical utility and comparable net benefits of the nomogram and scoring scale across a range of threshold probabilities.

DISCUSSION

This evidence-based, factor-weighted, accurate score could help clinicians swiftly stratify HFRS mortality risk and facilitate the implementation of patient triage and tiered medical services during epidemic peaks.

Comparison of simulation and predictive efficacy for hemorrhagic fever with renal syndrome incidence in mainland China based on five time series models

Background

Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic infectious disease commonly found in Asia and Europe, characterized by fever, hemorrhage, shock, and renal failure. China is the most severely affected region, necessitating an analysis of the temporal incidence patterns in the country.

Methods

We employed Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Nonlinear AutoRegressive with eXogenous inputs (NARX), and a hybrid CNN-LSTM model to model and forecast time series data spanning from January 2009 to November 2023 in the mainland China. By comparing the simulated performance of these models on training and testing sets, we determined the most suitable model.

Results

Overall, the CNN-LSTM model demonstrated optimal fitting performance (with Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE) of 93.77/270.66, 7.59%/38.96%, and 64.37/189.73 for the training and testing sets, respectively, lower than those of individual CNN or LSTM models).

Conclusion

The hybrid CNN-LSTM model seamlessly integrates CNN's data feature extraction and LSTM's recurrent prediction capabilities, rendering it theoretically applicable for simulating diverse distributed time series data. We recommend that the CNN-LSTM model be considered as a valuable time series analysis tool for disease prediction by policy-makers.

Spatiotemporal trends of hemorrhagic fever with renal syndrome (HFRS) in China under climate variation

Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by the rodent-transmitted orthohantaviruses (HVs), with China possessing the most cases globally. The virus hosts in China are Apodemus agrarius and Rattus norvegicus, and the disease spread is strongly influenced by global climate dynamics. To assess and predict the spatiotemporal trends of HFRS from 2005 to 2098, we collected historical HFRS data in mainland China (2005-2020), historical and projected climate and population data (2005-2098), and spatial variables including biotic, environmental, topographical, and socioeconomic. Spatiotemporal predictions and mapping were conducted under 27 scenarios incorporating multiple integrated representative concentration pathway models and population scenarios. We identify the type of magistral HVs host species as the best spatial division, including four region categories. Seven extreme climate indices associated with temperature and precipitation have been pinpointed as key factors affecting the trends of HFRS. Our predictions indicate that annual HFRS cases will increase significantly in 62 of 356 cities in mainland China. Rattus regions are predicted to be the most active, surpassing Apodemus and Mixed regions. Eighty cities are identified as at severe risk level for HFRS, each with over 50 reported cases annually, including 22 new cities primarily located in East China and Rattus regions after 2020, while 6 others develop new risk. Our results suggest that the risk of HFRS will remain high through the end of this century, with Rattus norvegicus being the most active host, and that extreme climate indices are significant risk factors. Our findings can inform evidence-based policymaking regarding future risk of HFRS.