Hemorrhagic Fever With Renal Syndrome Transmission Research Articles

Using empirical dynamic modeling to identify the impact of meteorological factors on hemorrhagic fever with renal syndrome in Weifang, Northeastern China, from 2011 to 2020.

Hemorrhagic Fever with Renal Syndrome (HFRS) continues to pose a significant public health threat to the well-being of the population. Given that the spread of HFRS is susceptible to meteorological factors, we aim to probe into the meteorological drivers of HFRS. Thus, novel techniques that can discern time-delayed non-linear relationships from nonlinear dynamical systems are compulsory. We analyze the epidemiological features of HFRS in Weifang City, 2011-2020, via the employment of the Empirical Dynamic Modeling (EDM) method. Our analysis delves into the intricate web of time-delayed non-linear associations between meteorological factors and HFRS. Additionally, we investigate the repercussions of minor perturbations in meteorological variables on future HFRS incidence. A total of 2515 HFRS cases were reported in Weifang from 2011 to 2020. The number of cases per week was 4.81, and the average weekly incidence was 0.52 per 1,000,000. The propagation of HFRS is significantly impacted by the mean weekly temperature, relative humidity, cumulative rainfall, and wind speed, and the ρCCM converges to 0.55,0.48,0.38 and 0.39, respectively. The graphical representation of the relationship between temperature (lagged by 2 weeks) and the incidence of HFRS exhibits an inverted U-shaped curve, whereby the incidence of HFRS culminates as the temperature reaches 10 °C. Moreover, temperature, relative humidity, cumulative rainfall, and wind speed exhibit a positive correlation with HFRS incidence, with a time lag of 4-6 months. Our discoveries suggest that meteorological factors can drive the transmission of HFRS both at a macroscopic and microscopic scale. Prospective alterations in meteorological conditions, for instance, elevations in temperature, relative humidity, and precipitation will instigate an upsurge in the incidence of HFRS after 4-6 months, and thus, timely public health measures should be taken to mitigate these changes.

Spatial-temporal patterns and influencing factors for hemorrhagic fever with renal syndrome: A 16-year national surveillance analysis in China

BackgroundChina is confronted with the significant menace posed by hemorrhagic fever with renal syndrome (HFRS). Nevertheless, the long-term spatial-temporal variations, regional prevalence patterns, and fundamental determinants' mechanisms for HFRS remain inadequately elucidated. MethodsNewly diagnosed cases of HFRS from January 2004 to December 2019 were acquired from the China Public Health Science Data repository. We used Age-period-cohort and Bayesian Spacetime Hierarchy models to identify high-risk populations and regions in mainland China. Additionally, the Geographical Detector model was employed to quantify the determinant powers of significant driver factors to the disease. ResultsA total of 199,799 cases of HFRS were reported in mainland China during 2004–2019. The incidence of HFRS declined from 1.93 per 100,000 in 2004 to 0.69 per 100,000 in 2019. The incidence demonstrated an inverted U-shaped trend with advancing age, peaking in the 50–54 age group, with higher incidences observed among individuals aged 20–74 years. Hyperendemic areas were mainly concentrated in the northeastern regions of China, while some western provinces exhibited a potential upward trend. Geographical detector model identified that the spatial variations of HFRS were significantly associated with the relative humidity (Q = 0.36), forest cover (Q = 0.26), rainfall (Q = 0.18), temperature (Q = 0.16), and the surface water resources (Q = 0.14). ConclusionsThis study offered comprehensive examinations of epidemic patterns, identified high-risk areas quantitatively, and analyzed factors influencing HFRS transmission in China. The findings may contribute to the necessary implementations for the effective prevention and control of HFRS.

The Spatiotemporal Pattern and Its Determinants of Hemorrhagic Fever With Renal Syndrome in Northeastern China: Spatiotemporal Analysis.

Hemorrhagic fever with renal syndrome (HFRS) is a significant zoonotic disease mainly transmitted by rodents. However, the determinants of its spatiotemporal patterns in Northeast China remain unclear. This study aimed to investigate the spatiotemporal dynamics and epidemiological characteristics of HFRS and detect the meteorological effect of the HFRS epidemic in Northeastern China. The HFRS cases of Northeastern China were collected from the Chinese Center for Disease Control and Prevention, and meteorological data were collected from the National Basic Geographic Information Center. Times series analyses, wavelet analysis, Geodetector model, and SARIMA model were performed to identify the epidemiological characteristics, periodical fluctuation, and meteorological effect of HFRS in Northeastern China. A total of 52,655 HFRS cases were reported in Northeastern China from 2006 to 2020, and most patients with HFRS (n=36,558, 69.43%) were aged between 30-59 years. HFRS occurred most frequently in June and November and had a significant 4- to 6-month periodicity. The explanatory power of the meteorological factors to HFRS varies from 0.15 ≤ q ≤ 0.01. In Heilongjiang province, mean temperature with a 4-month lag, mean ground temperature with a 4-month lag, and mean pressure with a 5-month lag had the most explanatory power on HFRS. In Liaoning province, mean temperature with a 1-month lag, mean ground temperature with a 1-month lag, and mean wind speed with a 4-month lag were found to have an effect on HFRS, but in Jilin province, the most important meteorological factors for HFRS were precipitation with a 6-month lag and maximum evaporation with a 5-month lag. The interaction analysis of meteorological factors mostly showed nonlinear enhancement. The SARIMA model predicted that 8,343 cases of HFRS are expected to occur in Northeastern China. HFRS showed significant inequality in epidemic and meteorological effects in Northeastern China, and eastern prefecture-level cities presented a high risk of epidemic. This study quantifies the hysteresis effects of different meteorological factors and prompts us to focus on the influence of ground temperature and precipitation on HFRS transmission in future studies, which could assist local health authorities in developing HFRS-climate surveillance, prevention, and control strategies targeting high-risk populations in China.

Epidemiological characteristics and the development of spatiotemporal analysis models on hemorrhagic fever with renal syndrome in China

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease of natural infectious focus caused by Hantavirus (HV) with clinical characteristics as fever, hemorrhage, hyperemia, hypotensive shock and renal damage. Through contacting the excreta or secretion of infected rats, human may get infected. The epidemiological characteristics of HFRS are significantly different in terms of population differences, geographical heterogeneity and seasonal variation, which are all closely related to the habitat of host animals and human productive activities. The reported number of HFRS is about 150 000 to 200 000 each year worldwide, and China accounted for 70%-90% of the total reported cases standing the most seriously infected country. In this study, we reviewed the epidemiological characteristics and the influencing factors of HFRS as well as the models and methods used in relevant ecological studies, in order to understand the distribution of time, regional and population and potential influencing factors on the transmission of HFRS better, so as to improve the strategies on investigation, monitoring, prevention and control of the diseases.

Exploring the Dynamics of Hemorrhagic Fever with Renal Syndrome Incidence in East China Through Seasonal Autoregressive Integrated Moving Average Models.

ObjectiveThe purpose of this study was to explore the dynamics of incidence of hemorrhagic fever with renal syndrome (HFRS) from 2000 to 2017 in Anqiu City, a city located in East China, and find the potential factors leading to the incidence of HFRS.MethodsMonthly reported cases of HFRS and climatic data from 2000 to 2017 in the city were obtained. Seasonal autoregressive integrated moving average (SARIMA) models were used to fit the HFRS incidence and predict the epidemic trend in Anqiu City. Univariate and multivariate generalized additive models were fit to identify and characterize the association between the HFRS incidence and meteorological factors during the study period.ResultsStatistical analysis results indicate that the annualized average incidence at the town level ranged from 1.68 to 6.31 per 100,000 population among 14 towns in the city, and the western towns exhibit high endemic levels during the study periods. With high validity, the optimal SARIMA(0,1,1,)(0,1,1)12 model may be used to predict the HFRS incidence. Multivariate generalized additive model (GAM) results show that the HFRS incidence increases as sunshine time and humidity increases and decreases as precipitation increases. In addition, the HFRS incidence is associated with temperature, precipitation, atmospheric pressure, and wind speed. Those are identified as the key climatic factors contributing to the transmission of HFRS.ConclusionThis study provides evidence that the SARIMA models can be used to characterize the fluctuations in HFRS incidence. Our findings add to the knowledge of the role played by climate factors in HFRS transmission and can assist local health authorities in the development and refinement of a better strategy to prevent HFRS transmission.

Effects of climate factors on hemorrhagic fever with renal syndrome in Changchun, 2013 to 2017.

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by hantaviruses (HVs). Climate factors have a significant impact on the transmission of HFRS. Here, we characterized the dynamic temporal trend of HFRS and identified the roles of climate factors in its transmission in Changchun, China.Surveillance data of HFRS cases and data on related environmental variables from 2013 to 2017 were collected. A principal components regression (PCR) model was used to quantify the relationship between climate factors and transmission of HFRS.During 2013 to 2017, a distinctly declining temporal trend of annual HFRS incidence was identified. Four principal components were extracted, with a cumulative contribution rate of 89.282%. The association between HFRS epidemics and climate factors was better explained by the PCR model (F = 10.050, P <.001, adjusted R2 = 0.456) than by the general multiple regression model (F = 2.748, P <.005, adjusted R2 = 0.397).The monthly trends of HFRS were positively correlated with the mean wind velocity but negatively correlated with the mean temperature, relative humidity, sunshine duration, and accumulative precipitation of the different previous months. The study results may be useful for the development of HFRS preventive initiatives that are customized for Changchun regarding specific climate environments.

Environmental Determinants of Hemorrhagic Fever with Renal Syndrome in High-Risk Counties in China: A Time Series Analysis (2002-2012).

The transmission pattern of hemorrhagic fever with renal syndrome (HFRS) is associated with environmental conditions, including meteorological factors and land-cover. In the present study, the association between HFRS and environmental factors (including maximum temperature, relative humidity, rainfall, and normalized difference vegetation index) were explored in two typical counties in Northeast and two counties in Northwest China with severe HFRS outbreaks by using seasonal autoregressive integrated moving average model with exogenous variables (SARIMAX). The results showed that rainfall with 3- to 4-month lag was closely associated with HFRS in the two counties in Northeast China, whereas relative humidity with 1- or 5-month lag significantly impacts HFRS transmission in the two counties in Northwest China. Moreover, the SARIMAX models exhibit accurate forecasting ability of HFRS cases. Our findings provide scientific support for local HFRS monitoring and control, and the development of a HFRS early warning system.

Impact of meteorological factors on hemorrhagic fever with renal syndrome in 19 cities in China, 2005–2014

This study aims to investigate the associations between meteorological factors and hemorrhagic fever with renal syndrome (HFRS) in 19 cities selected from HFRS high risk areas across different climate zones in three Provinces of China. De-identified daily reports of HFRS in Anhui, Heilongjiang, and Liaoning Provinces for 2005–2014 were obtained from the Chinese Center for Disease Control and Prevention. Daily weather data from each study location were obtained from the China meteorological Data Sharing Service System. Generalised estimating equation models (GEE) were used to quantify the city-specific HFRS-weather associations. Multivariate random-effects meta-regression models were used to pool the city-specific HFRS-weather effect estimates. HFRS showed an overall downward trend during the study period with a slight rebound after 2010. Meteorological factors were significantly associated with HFRS incidence. HFRS was relatively more sensitive to weather variability in subtropical regions (Anhui Province) than in temperate regions (Heilongjiang and Liaoning Provinces). The size of effect estimates and the duration of lagged effects varied by locations. Pooled results of the 19 cities showed that a 1 °C increase in maximum temperature (Tmax) resulted in a 1.6% (95% CI: 1.0%–2.2%) increase in HFRS; a 1 mm increase in weekly precipitation was associated with 0.2% (95%CI: 0.1%–0.3%) increase in HFRS; a 1% increase in average relative humidity was associated with a 0.9% (95%CI: 0.5%–1.2%) increase in HFRS. The lags with the largest effects for Tmax, precipitation, and relative humidity occurred in weeks 29, 22, and 16, respectively. Lagged effects of meteorological factors did not end after an epidemic season but waned gradually in the following 3–4 epidemic seasons. Weather variability plays a significant role in HFRS transmission in China. The long duration of lagged effects indicates the necessity of continuous interventions following the epidemics.

Effects of Climate and Rodent Factors on Hemorrhagic Fever with Renal Syndrome in Chongqing, China, 1997-2008.

China has the highest global incidence of hemorrhagic fever with renal syndrome (HFRS), constituting 90% of the cases in the world. Chongqing, located in the Three Gorges Reservoir Region, has been experiencing differences in the occurrence of HFRS from 1997 to 2008. The current study was designed to explore the effects of climate and rodent factors on the transmission of HFRS in Chongqing. Data on monthly HFRS cases, rodent strains, and climatic factors were collected from 1997 to 2008. Spatio-temporal analysis indicated that most HFRS cases were clustered in central Chongqing and that the incidence of HFRS decreased from 1997 to 2008. Poisson regression models showed that temperature (with lagged months of 0 and 5) and rainfall (with 2 lagged months) were key climatic factors contributing to the transmission of HFRS. A zero-inflated negative binomial model revealed that rodent density was also significantly associated with the occurrence of HFRS in the Changshou district. The monthly trend in HFRS incidence was positively associated with rodent density and rainfall and negatively associated with temperature. Possible mechanisms are proposed through which construction of the dam influenced the incidence of HFRS in Chongqing. The findings of this study may contribute to the development of early warning systems for the control and prevention of HFRS in the Three Gorges Reservoir Region.

Time-specific ecologic niche models forecast the risk of hemorrhagic fever with renal syndrome in Dongting Lake district, China, 2005-2010.

BackgroundHemorrhagic fever with renal syndrome (HFRS), a rodent-borne infectious disease, is one of the most serious public health threats in China. Increasing our understanding of the spatial and temporal patterns of HFRS infections could guide local prevention and control strategies.Methodology/Principal FindingsWe employed statistical models to analyze HFRS case data together with environmental data from the Dongting Lake district during 2005–2010. Specifically, time-specific ecologic niche models (ENMs) were used to quantify and identify risk factors associated with HFRS transmission as well as forecast seasonal variation in risk across geographic areas. Results showed that the Maximum Entropy model provided the best predictive ability (AUC = 0.755). Time-specific Maximum Entropy models showed that the potential risk areas of HFRS significantly varied across seasons. High-risk areas were mainly found in the southeastern and southwestern areas of the Dongting Lake district. Our findings based on models focused on the spring and winter seasons showed particularly good performance. The potential risk areas were smaller in March, May and August compared with those identified for June, July and October to December. Both normalized difference vegetation index (NDVI) and land use types were found to be the dominant risk factors.Conclusions/SignificanceOur findings indicate that time-specific ENMs provide a useful tool to forecast the spatial and temporal risk of HFRS.

Animal Reservoir, Natural and Socioeconomic Variations and the Transmission of Hemorrhagic Fever with Renal Syndrome in Chenzhou, China, 2006–2010

BackgroundChina has the highest incidence of hemorrhagic fever with renal syndrome (HFRS) worldwide. Reported cases account for 90% of the total number of global cases. By 2010, approximately 1.4 million HFRS cases had been reported in China. This study aimed to explore the effect of the rodent reservoir, and natural and socioeconomic variables, on the transmission pattern of HFRS.Methodology/Principal FindingsData on monthly HFRS cases were collected from 2006 to 2010. Dynamic rodent monitoring data, normalized difference vegetation index (NDVI) data, climate data, and socioeconomic data were also obtained. Principal component analysis was performed, and the time-lag relationships between the extracted principal components and HFRS cases were analyzed. Polynomial distributed lag (PDL) models were used to fit and forecast HFRS transmission. Four principal components were extracted. Component 1 (F1) represented rodent density, the NDVI, and monthly average temperature. Component 2 (F2) represented monthly average rainfall and monthly average relative humidity. Component 3 (F3) represented rodent density and monthly average relative humidity. The last component (F4) represented gross domestic product and the urbanization rate. F2, F3, and F4 were significantly correlated, with the monthly HFRS incidence with lags of 4 months (r = −0.289, P<0.05), 5 months (r = −0.523, P<0.001), and 0 months (r = −0.376, P<0.01), respectively. F1 was correlated with the monthly HFRS incidence, with a lag of 4 months (r = 0.179, P = 0.192). Multivariate PDL modeling revealed that the four principal components were significantly associated with the transmission of HFRS.ConclusionsThe monthly trend in HFRS cases was significantly associated with the local rodent reservoir, climatic factors, the NDVI, and socioeconomic conditions present during the previous months. The findings of this study may facilitate the development of early warning systems for the control and prevention of HFRS and similar diseases.

Association between Hemorrhagic Fever with Renal Syndrome Epidemic and Climate Factors in Heilongjiang Province, China

The purpose of this study was to quantify the relationship between climate variation and transmission of hemorrhagic fever with renal syndrome (HFRS) in Heilongjiang Province, a highly endemic area for HFRS in China. Monthly notified HFRS cases and climatic data for 2001-2009 in Heilongjiang Province were collected. Using a seasonal autoregressive integrated moving average model, we found that relative humidity with a one-month lag (β = -0.010, P = 0.003) and a three-month lag (β = 0.008, P = 0.003), maximum temperature with a two-month lag (β = 0.082, P = 0.028), and southern oscillation index with a two-month lag (β = -0.048, P = 0.019) were significantly associated with HFRS transmission. Our study also showed that predicted values expected under the seasonal autoregressive integrated moving average model were highly consistent with observed values (Adjusted R(2) = 83%, root mean squared error = 108). Thus, findings may help add to the knowledge gap of the role of climate factors in HFRS transmission in China and also assist national local health authorities in the development/refinement of a better strategy to prevent HFRS transmission.

Temporal trend and climate factors of hemorrhagic fever with renal syndrome epidemic in Shenyang City, China

BackgroundHemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution, external environmental factors including climate factors may play a significant role in its transmission. The city of Shenyang is one of the most seriously endemic areas for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified climate-related risk factors and their roles in HFRS transmission in Shenyang, China.MethodsThe annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal component analysis was constructed by using climate data from 2004 to 2009 to extract principal components of climate factors to reduce co-linearity. The extracted principal components and autocorrelation terms of monthly HFRS cases were added into a multiple regression model called principal components regression model (PCR) to quantify the relationship between climate factors, autocorrelation terms and transmission of HFRS. The PCR model was compared to a general multiple regression model conducted only with climate factors as independent variables.ResultsA distinctly declining temporal trend of annual HFRS incidence was identified. HFRS cases were reported every month, and the two peak periods occurred in spring (March to May) and winter (November to January), during which, nearly 75% of the HFRS cases were reported. Three principal components were extracted with a cumulative contribution rate of 86.06%. Component 1 represented MinRH0, MT1, RH1, and MWV1; component 2 represented RH2, MaxT3, and MAP3; and component 3 represented MaxT2, MAP2, and MWV2. The PCR model was composed of three principal components and two autocorrelation terms. The association between HFRS epidemics and climate factors was better explained in the PCR model (F = 446.452, P < 0.001, adjusted R2 = 0.75) than in the general multiple regression model (F = 223.670, P < 0.000, adjusted R2 = 0.51).ConclusionThe temporal distribution of HFRS in Shenyang varied in different years with a distinctly declining trend. The monthly trends of HFRS were significantly associated with local temperature, relative humidity, precipitation, air pressure, and wind velocity of the different previous months. The model conducted in this study will make HFRS surveillance simpler and the control of HFRS more targeted in Shenyang.

The warning model and influence of climatic changes on hemorrhagic fever with renal syndrome in Changsha city

To realize the influence of climatic changes on the transmission of hemorrhagic fever with renal syndrome (HFRS), and to explore the adoption of climatic factors in warning HFRS. A total of 2171 cases of HFRS and the synchronous climatic data in Changsha from 2000 to 2009 were collected to a climate-based forecasting model for HFRS transmission. The Cochran-Armitage trend test was employed to explore the variation trend of the annual incidence of HFRS. Cross-correlations analysis was then adopted to assess the time-lag period between the climatic factors, including monthly average temperature, relative humidity, rainfall and Multivariate Elño-Southern Oscillation Index (MEI) and the monthly HFRS cases. Finally the time-series Poisson regression model was constructed to analyze the influence of different climatic factors on the HFRS transmission. The annual incidence of HFRS in Changsha between 2000 - 2009 was 13.09/100 000 (755 cases), 9.92/100 000 (578 cases), 5.02/100 000 (294 cases), 2.55/100 000 (150 cases), 1.13/100 000 (67 cases), 1.16/100 000 (70 cases), 0.95/100 000 (58 cases), 1.40/100 000 (87 cases), 0.75/100 000 (47 cases) and 1.02/100 000 (65 cases), respectively. The incidence showed a decline during these years (Z = -5.78, P < 0.01). The results of Poisson regression model indicated that the monthly average temperature (18.00°C, r = 0.26, P < 0.01, 1-month lag period; IRR = 1.02, 95%CI: 1.00 - 1.03, P < 0.01), relative humidity (75.50%, r = 0.62, P < 0.01, 3-month lag period; IRR = 1.03, 95%CI: 1.02 - 1.04, P < 0.01), rainfall (112.40 mm, r = 0.25, P < 0.01, 6-month lag period; IRR = 1.01, 95CI: 1.01 - 1.02, P = 0.02), and MEI (r = 0.31, P < 0.01, 3-month lag period; IRR = 0.77, 95CI: 0.67 - 0.88, P < 0.01) were closely associated with monthly HFRS cases (18.10 cases). Climate factors significantly influence the incidence of HFRS. If the influence of variable-autocorrelation, seasonality, and long-term trend were controlled, the accuracy of forecasting by the time-series Poisson regression model in Changsha would be comparatively high, and we could forecast the incidence of HFRS in advance.

Partial genetic characterization of Seoul hantavirus in rats from Buenos Aires City, Argentina, and generation of a Seoul recombinant nucleoprotein antigen

Hemorrhagic fever with renal syndrome (HFRS) is a severe infectious disease characterized by fever, hemorrhage, renal impairment, and thrombocytopenia. At least seven hantaviruses cause HFRS: Hantaan, Seoul (SEOV) (distributed worldwide), Dobrava/Belgrade, Saaremaa, Amur, Thailand and Puumala. To investigate the epidemiology of HFRS and virus transmission in Argentina, we constructed a prokaryotic plasmid encoding the SEOV rNP, of 430 amino acids. After expression, the rNP was tested as an antigen for use in an enzyme-linked immunosorbent assay for infection diagnosis. To determine the current level of virus transmission in wild brown rats or Norway rats (Rattus norvegicus) captured in Buenos Aires City, Argentina, we tested tissues from rats that were determined to be serologically positive for the SEOV, and the viral genome were detected by RT-PCR using specific primers for two fragments of M segment-encoding Gn and Gc proteins. The viral genome was detected in 11 of 21 seropositive rats (52.4%) captured in two parklands. Sequence analysis of a 333-nt region of the Gc-encoding M segment revealed 97% and 96% identity with strains of SEOV from Baltimore and Brazil, respectively. Our genetic data confirm a very low diversity among SEOV virus strains.

Climate Variability and Hemorrhagic Fever with Renal Syndrome Transmission in Northeastern China

BackgroundThe transmission of hemorrhagic fever with renal syndrome (HFRS) is influenced by climatic variables. However, few studies have examined the quantitative relationship between climate variation and HFRS transmission.ObjectiveWe examined the potential impact of climate variability on HFRS transmission and developed climate-based forecasting models for HFRS in northeastern China.MethodsWe obtained data on monthly counts of reported HFRS cases in Elunchun and Molidawahaner counties for 1997–2007 from the Inner Mongolia Center for Disease Control and Prevention and climate data from the Chinese Bureau of Meteorology. Cross-correlations assessed crude associations between climate variables, including rainfall, land surface temperature (LST), relative humidity (RH), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI) and monthly HFRS cases over a range of lags. We used time-series Poisson regression models to examine the independent contribution of climatic variables to HFRS transmission.ResultsCross-correlation analyses showed that rainfall, LST, RH, and MEI were significantly associated with monthly HFRS cases with lags of 3–5 months in both study areas. The results of Poisson regression indicated that after controlling for the autocorrelation, seasonality, and long-term trend, rainfall, LST, RH, and MEI with lags of 3–5 months were associated with HFRS in both study areas. The final model had good accuracy in forecasting the occurrence of HFRS.ConclusionsClimate variability plays a significant role in HFRS transmission in northeastern China. The model developed in this study has implications for HFRS control and prevention.

Characterization of truncated hantavirus nucleocapsid proteins and their application for serotyping

Hemorrhagic fever with renal syndrome (HFRS) is a fulminant infectious disease characterized by fever, hemorrhage, renal impairment, and thrombocytopenia. Hantaviruses associated with this belong to different serotypes: Hantaan (HTN), Seoul (SEO), Dobrava/Belgrade (DOB), and Puumala (PUU). The first two, HTN and SEO, are endemic in China. To investigate the epidemiology of HFRS and virus transmission in China, we constructed prokaryotic plasmids encoding truncated recombinant HTN and SEO nucleocapsid proteins (NPs), which lacked 154 amino acid (aa), 99 aa, or 49 aa in the N-terminal region, respectively. After expression, the truncated rNPs were tested as serotyping antigens, particularly for use in the enzyme-linked immunosorbent assay (ELISA). In addition, 68 acute and 52 convalescent sera were collected from HFRS patients from Harbin, Lantian, and Kaifeng regions in China in 2004, which had hantavirus specific antibodies by IFA. A neutralization test was used to differentiate these, which showed that 73 were due to HTN infection, 33 to SEO infection, and 14 undetermined. By ELISA, the truncated rNPs, that lacked 99 (rNP100) or 49 (rNP50) N-terminal amino acids of the NPs of HTN and SEO, were able to differentiate HTNV and SEOV-specific immune sera, but the rNP155 could not. Particularly, the ELISAs based on the rNP50s had a result comparable to PRNT. Thus, the rNP50 is recommended as efficient serotyping antigen for hantavirus infection diagnosis by ELISA.