Bayesian spatiotemporal modelling analysis of hemorrhagic fever with renal syndrome outbreaks in China using R-INLA

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.

Long- and Short-Run Asymmetric Effects of Meteorological Parameters on Hemorrhagic Fever with Renal Syndrome in Heilongjiang: A Population-Based Retrospective Study

Examining both long-term and short-term effects can enhance the precision and reliability of time series analysis. This study aimed to delve into the asymmetric effects of weather conditions on hemorrhagic fever with renal syndrome (HFRS) in the long and short terms and build a forecasting system. Data comprising monthly HFRS incidents and weather factors in Heilongjiang from January 2004 to December 2019 were extracted. Subsequently, the long- and short-term asymmetric impacts were examined using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Next, the samples were partitioned into training and testing subsets to evaluate the predictive potential of both models. From 2004 to 2019, HFRS exhibited a declining trend (average annual percentage change = -6.744%, 95% CI: -13.52%-0.563%) and a dual seasonal pattern, with a prominent peak in June and a secondary one in October-December. This study identified long-term asymmetric effects of rainfall (Wald long-run asymmetry (WLR) = 3.292, p=0.001), wind velocity (WLR = -3.271, p=0.001), and air pressure (WLR = -6.453, p < 0.001) on HFRS. Additionally, this study observed short-term asymmetric impacts of relative humidity (Wald short-run symmetry (WSR) = -1.547, p=0.001), rainfall (WSR = -1.984, p=0.049), and air pressure (WSR = -2.33, p=0.021) on HFRS. A unit increase in relative humidity, sunshine hours, and air pressure resulted in about 10.9%, 1.9%, and 13.6% decreases in HFRS, respectively; a unit decrease in relative humidity, rainfall, and sunshine hours led to about 6.7%, 1.8%, and 2% decreases in HFRS, respectively. When temperature increased and decreased by one unit, the HFRS incidence increased by 11.6% and 22.5%, respectively. HFRS also varied significantly with the positive and negative changes in differenced (D) temperature, D (relative humidity), D (wind velocity), D (rainfall), D (air pressure), and D (sunshine hours) at 0-3-month delays over the short term. The NARDL model exhibited notably lower error rates in forecasting compared to the ARDL model. Meteorological parameters affect HFRS in both the long and short term, often showing asymmetric effects. The NARDL model, capable of incorporating various weather parameters, proves to be valuable in predicting HFRS epidemic and guiding strategies for prevention and control.

Land-cover, land-use and human hantavirus infection risk: a systematic review

Previous studies suggest that the risk of human infection by hantavirus, a family of rodent-borne viruses, might be affected by different environmental determinants such as land cover, land use and land use change. This study examined the association between land-cover, land-use, land use change, and human hantavirus infection risk. PubMed and Scopus databases were interrogated using terms relative to land use (change) and human hantavirus disease. Screening and selection of the articles were completed by three independent reviewers. Classes of land use assessed by the different studies were categorized into three macro-categories of exposure ('Agriculture', 'Forest Cover', 'Urban Areas') to qualitatively synthesize the direction of the association between exposure variables and hantavirus infection risk in humans. A total of 25 articles were included, with 14 studies (56%) conducted in China, 4 studies (16%) conducted in South America and 7 studies (28%) conducted in Europe. Most of the studies (88%) evaluated land cover or land use, while 3 studies (12%) evaluated land use change, all in relation to hantavirus infection risk. We observed that land cover and land-use categories could affect hantavirus infection incidence. Overall, agricultural land use was positively associated with increased human hantavirus infection risk, particularly in China and Brazil. In Europe, a positive association between forest cover and hantavirus infection incidence was observed. Studies that assessed the relationship between built-up areas and hantavirus infection risk were more variable, with studies reporting positive, negative or no associations.

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.

Spatial-temporal drivers and incidence heterogeneity of hemorrhagic fever with renal syndrome transmission in Shandong Province, China, 2016-2022

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) signals a recurring risk in Eurasia in recent years owing to its continued rise in case notifications and the extension of geographical distribution. This study was undertaken to investigate the spatiotemporal drivers and incidence heterogeneity of HFRS transmission in Shandong Province.

METHODS

The epidemiological data for HFRS, meteorological data and socioeconomic data were obtained from China Information System for Disease Control and Prevention, China Meteorological Data Sharing Service System, and Shandong Statistical Yearbook, respectively. The spatial-temporal multicomponent model was employed to analyze the values of spatial-temporal components and the heterogeneity of HFRS transmission across distinct regions.

RESULTS

The total effect values of the autoregressive, epidemic, and endemic components were 0.451, 0.187, and 0.033, respectively, exhibiting significant heterogeneity across various cities. This suggested a pivotal role of the autoregressive component in propelling HFRS transmission in Shandong Province. The epidemic component of Qingdao, Weifang, Yantai, Weihai, and Jining declined sharply at the onset of 2020. The random effect identified distinct incidence levels associated with Qingdao and Weifang, signifying regional variations in HFRS occurrence.

CONCLUSIONS

The autoregressive component emerged as a significant driver in the transmission of HFRS in Shandong Province. Targeted preventive measures should be strategically implemented across various regions, taking into account the predominant component influencing the epidemic.

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.

Assessing the Relationship between Climate Variables and Hemorrhagic Fever with Renal Syndrome Transmission in Eastern China: A Multi-Cities Time Series Study

Hemorrhagic fever with renal syndrome (HFRS) is a climate-sensitive infectious disease. The effect of climatic drivers might predict and prevent HFRS, and understanding their relationship is urgently needed in the face of climate change. This study aimed to investigate the effect of meteorological factors on HFRS incidence. The random forest regression model, generalized additive model, and distributed lag nonlinear model (DLNM) were constructed to predict the importance, nonlinear trend and interaction effect, and exposure-lag effect of meteorological factors on HFRS incidence based on the data obtained in Shandong Province, China, 2013-2022. The most crucial meteorological factor was the weekly mean temperature. Interaction results showed that relative humidity affected HFRS incidence only under high or low-temperature conditions, and the effect of relative humidity with high and low pressure was the opposite. Using the median value as the reference, DLNM indicated that extremely low temperature had significant associations with HFRS at a lag of 3-5 weeks. Under extremely high temperatures, relative risks (RRs) became significantly high from a lag of 11 weeks, with the lowest value of 1.07 (95% CI: 1.00-1.13). RRs increased and then decreased with increasing mean temperature at lag 4 and 8 weeks, whereas at lag 12 and 16 weeks, the RRs gradually increased as the mean temperature climbed. This study demonstrates the complex relationship between meteorological factors and HFRS incidence. Our findings provide implications for the development of weather-based HFRS early warning systems.

Identification and characterization of codon usage pattern and influencing factors in HFRS-causing hantaviruses

Hemorrhagic fever with renal syndrome (HFRS) is an acute viral zoonosis carried and transmitted by infected rodents through urine, droppings, or saliva. The etiology of HFRS is complex due to the involvement of viral factors and host immune and genetic factors which hinder the development of potential therapeutic solutions for HFRS. Hantaan virus (HTNV), Dobrava-Belgrade virus (DOBV), Seoul virus (SEOV), and Puumala virus (PUUV) are predominantly found in hantaviral species that cause HFRS in patients. Despite ongoing prevention and control efforts, HFRS remains a serious economic burden worldwide. Furthermore, recent studies reported that the hantavirus nucleocapsid protein is a multi-functional protein and plays a major role in the replication cycle of the hantavirus. However, the precise mechanism of the nucleoproteins in viral pathogenesis is not completely understood. In the framework of the current study, various in silico approaches were employed to identify the factors influencing the codon usage pattern of hantaviral nucleoproteins. Based on the relative synonymous codon usage (RSCU) values, a comparative analysis was performed between HFRS-causing hantavirus and their hosts, suggesting that HTNV, DOBV, SEOV, and PUUV, were inclined to evolve their codon usage patterns that were comparable to those of their hosts. The results indicated that most of the overrepresented codons had AU-endings, which revealed that mutational pressure is the major force shaping codon usage patterns. However, the influence of natural selection and geographical factors cannot be ignored on viral codon usage bias. Further analysis also demonstrated that HFRS causing hantaviruses adapted host-specific codon usage patterns to sustain successful replication and transmission chains within hosts. To our knowledge, no study to date reported the factors influencing the codon usage pattern within hantaviral nucleoproteins. Thus, the proposed computational scheme can help in understanding the underlying mechanism of codon usage patterns in HFRS-causing hantaviruses which lend a helping hand in designing effective anti-HFRS treatments in future. This study, although comprehensive, relies on in silico methods and thus necessitates experimental validation for more solid outcomes. Beyond the identified factors influencing viral behavior, there could be other yet undiscovered influences. These potential factors should be targets for further research to improve HFRS therapeutic strategies.

Spatio-temporal distribution characteristics of the risk of viral hepatitis B incidence based on INLA in 14 prefectures of Xinjiang from 2004 to 2019

This study aimed to explore the spatio-temporal distribution characteristics and risk factors of hepatitis B (HB) in 14 prefectures of Xinjiang, China, and to provide a relevant reference basis for the prevention and treatment of HB. Based on HB incidence data and risk factor indicators in 14 prefectures in Xinjiang from 2004 to 2019, we explored the distribution characteristics of the risk of HB incidence using global trend analysis and spatial autocorrelation analysis and established a Bayesian spatiotemporal model to identify the risk factors of HB and their spatio-temporal distribution to fit and extrapolate the Bayesian spatiotemporal model using the Integrated Nested Laplace Approximation (INLA) method. There was spatial autocorrelation in the risk of HB and an overall increasing trend from west to east and north to south. The natural growth rate, per capita GDP, number of students, and number of hospital beds per 10, 000 people were all significantly associated with the risk of HB incidence. From 2004 to 2019, the risk of HB increased annually in 14 prefectures in Xinjiang, with Changji Hui Autonomous Prefecture, Urumqi City, Karamay City, and Bayangol Mongol Autonomous Prefecture having the highest rates.