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Maps of Zhejiang Province, China with area names. This map was created by ArcGIS software (version 10.1, ESRI Inc.; Redlands, CA, USA). The homepage for the ArcGIS software was https://www.esri.com/.
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Zhejiang Province is one of the six provinces in China that has the highest incidence of haemorrhagic fever with renal syndrome (HFRS). Data on HFRS cases in Zhejiang Province from January 2007 to July 2017 were obtained from the China Information Network System of Disease Prevention and Control. Joinpoint regression analysis was used to observe th...
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Background:
Dengue is becoming a major public health concern in Guangdong (GD) Province of China. The problem was highlighted in 2014 by an unprecedented explosive outbreak, where the number of cases was larger than the total cases in previous 30 years. The present study aimed to clarify the spatial and temporal patterns of this dengue outbreak....
Citations
... This shows that regions with more mountains are more prone to the prevalence of HFRS. Among the five counties with the highest prevalence rates, Kaihua County was located in the westernmost part of Zhejiang Province, and Shengzhou County, Xinchang County, and Tiantai County formed the east-central clustering area [24]. Longquan County, which is relatively far from the clustering area, is adjacent to Nanping City, a high-incidence area in Fujian Province. ...
Hemorrhagic fever with renal syndrome (HFRS) is caused by hantaviruses (HVs) and is endemic in Zhejiang Province, China. In this study, we aimed to explore the changing epidemiology of HFRS cases and the dynamics of hantavirus hosts in Zhejiang Province. Joinpoint regression was used to analyze long-term trends in the incidence of HFRS. The comparison of animal density at different stages was conducted using the Mann–Whitney Test. A comparison of HV carriage rates between stages and species was performed using the chi-square test. The incidence of HFRS shows a continuous downward trend. Cases are widely distributed in all counties of Zhejiang Province except Shengsi County. There was a high incidence belt from west to east, with low incidence in the south and north. The HFRS epidemic showed two seasonal peaks in Zhejiang Province, which were winter and summer. It showed a marked increase in the age of the incidence population. A total of 23,073 minibeasts from 21 species were captured. Positive results were detected in the lung tissues of 14 rodent species and 1 shrew species. A total of 80% of the positive results were from striped field mice and brown rats. No difference in HV carriage rates between striped field mice and brown rats was observed (χ2 = 0.258, p = 0.611).
... Frontiers in Public Health 07 frontiersin.org to its long history of agricultural production and lifestyle, its greater exposure to HFRS, and its greater susceptibility to the disease (43). Therefore, targeted health education on HFRS among the middle-aged and older adult population to enhance their self-protection awareness is an important measure for prevention and control. ...
Background
Hemorrhagic fever with renal syndrome (HFRS) is one of the 10 major infectious diseases that jeopardize human health and is distributed in more than 30 countries around the world. China is the country with the highest number of reported HFRS cases worldwide, accounting for 90% of global cases. The incidence level of HFRS in Quzhou is at the forefront of Zhejiang Province, and there is no specific treatment for it yet. Therefore, it is crucial to grasp the epidemiological characteristics of HFRS in Quzhou and establish a prediction model for HFRS to lay the foundation for early warning of HFRS.
Methods
Descriptive epidemiological methods were used to analyze the epidemic characteristics of HFRS, the incidence map was drawn by ArcGIS software, the Seasonal AutoRegressive Integrated Moving Average (SARIMA) and Prophet model were established by R software. Then, root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the fitting and prediction performances of the model.
Results
A total of 843 HFRS cases were reported in Quzhou City from 2005 to 2022, with the highest annual incidence rate in 2007 (3.93/100,000) and the lowest in 2022 (1.05/100,000) (P trend<0.001). The incidence is distributed in a seasonal double-peak distribution, with the first peak from October to January and the second peak from May to July. The incidence rate in males (2.87/100,000) was significantly higher than in females (1.32/100,000). Farmers had the highest number of cases, accounting for 79.95% of the total number of cases. The incidence is high in the northwest of Quzhou City, with cases concentrated on cultivated land and artificial land. The RMSE and MAE values of the Prophet model are smaller than those of the SARIMA (1,0,1) (2,1,0)12 model.
Conclusion
From 2005 to 2022, the incidence of HFRS in Quzhou City showed an overall downward trend, but the epidemic in high-incidence areas was still serious. In the future, the dynamics of HFRS outbreaks and host animal surveillance should be continuously strengthened in combination with the Prophet model. During the peak season, HFRS vaccination and health education are promoted with farmers as the key groups.
... The Autoregressive Integrated Moving Average model (ARIMA) is a widely recognized and commonly used statistical technique for time series forecasting (Wu et al., 2018). It falls under the category of models that can effectively capture various typical temporal patterns within time series data and excel in modeling linear relationships between time series data points. ...
... Therefore, this study employ the scaling law to explain the scaling relationship between syphilis incidence and population size in Zhejiang Province, China. Additionally, to identify the main driving components affecting the occurrence of syphilis in different regions of Zhejiang, a multivariate time series model was applied (22). ...
... The variance components are estimated by maximizing the approximated marginal likelihood obtained via Laplace's approximation. The optimal model is selected through the Akaike information criterion (AIC), and the smaller the value is, the better the fitting effect of the model (22,34,35). The multivariate time series model was run by R Studio (version 1.2.5001). ...
Background
Syphilis has caused epidemics for hundreds of years, and the global syphilis situation remains serious. The reported incidence rate of syphilis in Zhejiang Province has ranked first in the province in terms of notifiable infectious diseases for many years and is the highest in China. This study attempts to use the scaling law theory to study the relationship between population size and different types of syphilis epidemics, while also exploring the main driving factors affecting the incidence of syphilis in different regions.
Methods
Data on syphilis cases and affected populations at the county level were obtained from the China Disease Control and Prevention Information System. The scaling relationship between different stages of syphilis and population size was explained by scaling law. The trend of the incidence from 2016 to 2022 was tested by the joinpoint regression. The index of distance between indices of simulation and observation (DISO) was applied to evaluate the overall performance of joinpoint regression model. Furthermore, a multivariate time series model was employed to identify the main driving components that affected the occurrence of syphilis at the county level. The p value less than 0.05 or confidence interval (CI) does not include 0 represented statistical significance for all the tests.
Results
From 2016 to 2022, a total of 204,719 cases of syphilis were reported in Zhejiang Province, including 2 deaths, all of which were congenital syphilis. Latent syphilis accounted for 79.47% of total syphilis cases. The annual percent change (APCs) of all types of syphilis, including primary syphilis, secondary syphilis, tertiary syphilis, congenital syphilis and latent syphilis, were − 21.70% (p < 0.001, 95% CI: −26.70 to −16.30), −16.80% (p < 0.001, 95% CI: −20.30 to −13.30), −8.70% (p < 0.001, 95% CI: −11.30 to −6.00), −39.00% (p = 0.001, 95% CI: −49.30 to −26.60) and − 7.10% (p = 0.008, 95% CI: −11.20 to −2.80), respectively. The combined scaling exponents of primary syphilis, secondary syphilis, tertiary syphilis, congenital syphilis and latent syphilis based on the random effects model were 0.95 (95% CI: 0.88 to 1.01), 1.14 (95% CI: 1.12 to 1.16), 0.43 (95% CI: 0.37 to 0.49), 0.0264 (95% CI: −0.0047 to 0.0575) and 0.88 (95% CI: 0.82 to 0.93), respectively. The overall average effect values of the endemic component, spatiotemporal component and autoregressive component for all counties were 0.24, 0.035 and 0.72, respectively. The values of the autoregressive component for most counties were greater than 0.7. The endemic component of the top 10 counties with the highest values was greater than 0.34. Two counties with value of the spatiotemporal component higher than 0.1 were Xihu landscape county and Shengsi county. From 2016 to 2022, the endemic and autoregressive components of each county showed obvious seasonal changes.
Conclusion
The scaling exponent had both temporal trend characteristics and significant heterogeneity in the association between each type of syphilis and population size. Primary syphilis and latent syphilis exhibited a linear pattern, secondary syphilis presented a superlinear pattern, and tertiary syphilis exhibited a sublinear pattern. This suggested that further prevention of infection and transmission among high-risk populations and improvement of diagnostic accuracy in underdeveloped areas is needed. The autoregressive components and the endemic components were the main driving factors that affected the occurrence of syphilis. Targeted prevention and control strategies must be developed based on the main driving modes of the epidemic in each county.
... More common in China are Seoul orthohantavirus and Hantaan orthohantavirus; Caobang orthohantavirus has not been found in mainland China but only in shrews from Taiwan, China [28]. Hantavirus has been found in 31 provinces in China, and the number of human cases in Zhejiang, Jiangsu, and other provinces has been on the rise since 2011 [29][30][31][32][33]. Our study found that the Caobang orthohantavirus were first identified in A. squamipes of Yunnan province. ...
Rodents and shrews are major reservoirs of various pathogens that are related to zoonotic infectious diseases. The purpose of this study was to investigate co-infections of zoonotic pathogens in rodents and shrews trapped in four provinces of China. We sampled different rodent and shrew communities within and around human settlements in four provinces of China and characterised several important zoonotic viral, bacterial, and parasitic pathogens by PCR methods and phylogenetic analysis. A total of 864 rodents and shrews belonging to 24 and 13 species from RODENTIA and EULIPOTYPHLA orders were captured, respectively. For viral pathogens, two species of hantavirus (Hantaan orthohantavirus and Caobang orthohantavirus) were identified in 3.47% of rodents and shrews. The overall prevalence of Bartonella spp., Anaplasmataceae, Babesia spp., Leptospira spp., Spotted fever group Rickettsiae, Borrelia spp., and Coxiella burnetii were 31.25%, 8.91%, 4.17%, 3.94%, 3.59%, 3.47%, and 0.58%, respectively. Furthermore, the highest co-infection status of three pathogens was observed among Bartonella spp., Leptospira spp., and Anaplasmataceae with a co-infection rate of 0.46%. Our results suggested that species distribution and co-infections of zoonotic pathogens were prevalent in rodents and shrews, highlighting the necessity of active surveillance for zoonotic pathogens in wild mammals in wider regions.
... hantavirus infection causes rodent-borne zoonotic illness hemorrhagic fever with renal syndrome (hFRs), which is mostly spread to people by aerosolized viral particles found in rodent urine, feces, and saliva [1]. china has the greatest incidence of hFRs worldwide, with asia and europe having the highest prevalence rates [2]. in mainland china, 209,209 hFRs cases and 1855 related fatalities were documented from 2004 to 2019. ...
Background
Hantavirus infection is the main cause of hemorrhagic fever with renal syndrome (HFRS), which is common in Asia and Europe. There is a considerable risk of morbidity and mortality from the uncommon Hantavirus complication known as acute pancreatitis (AP).
Methods
Retrospective analysis of the medical records of individuals with HFRS was performed. Relevant variables were assessed by univariate analyses and the variables with a p value <.05 were entered into the multivariable regression analysis.
Results
In this study, 114 individuals with HFRS in total were included, and 30 of them (26.32%) had AP. The univariate analyses showed that living in Xuancheng city (Anhui Province); an alcohol consumption history; white blood cell (WBC) count; lymphocyte (lym%) and eosinophil percentages (EO%); neutrophil (neut), eosinophil (EO), and red blood cell (RBC) counts; hemoglobin (Hb); hematocrit (HCT); proteinuria; hematuria; albumin (ALB), blood urea nitrogen (BUN), creatinine (Cr), uric acid (UA), cystatin-C (Cys-C) levels; carbon dioxide-combining power (CO2CP); fibrinogen degradation products (FDPs); and D-dimer level were significantly associated with HFRS complicated with AP (p < .05). In the multivariable regression analysis, an alcohol consumption history, lym%, proteinuria, FDPs and D-dimer level were found to be risk factors for HFRS complicated with AP (p < .05).
Conclusion
Our findings indicate that HFRS patients with a history of consuming alcohol, a high lym%, intense proteinuria, high levels of FDPs, and a low level of D-dimer might be more prone to the development of AP.
KEY MESSAGES
This is the first report employing Logistic regression analysis methods for exploring the risk factors for HFRS complicated with AP in China.
Many factors (most are laboratory parameters) were significantly associated with HFRS complicated with AP.
We found that HFRS patients with a history of consuming alcohol, a high lym%, intense proteinuria, high levels of FDPs, and a low level of D-dimer might be more prone to the development of AP.
... A high male to female ratio in the disease has been demonstrated in multiple studies [16,29,34]. Krautkramer et al. ...
Nephropathia epidemica (NE) is a zoonotic disease caused by hantaviruses transmitted from rodents, endemic in the Republic of Tatarstan, Russia. The disease presents clinically with mild, moderate, and severe forms, and time-dependent febrile, oliguric, and polyuric stages of the disease are also recognized. The patient’s cytokine responses have been suggested to play a central role in disease pathogenesis; however, little is known about the different patterns of cytokine expression in NE in cohorts of different ages and sexes. Serum samples and clinical records were collected from 139 patients and 57 controls (healthy donors) and were used to analyze 48 analytes with the Bio-Plex multiplex magnetic bead-based antibody detection kits. Principal component analysis of 137 patient and 55 controls (for which there was full data) identified two components that individually accounted for >15% of the total variance in results and together for 38% of the total variance. PC1 represented a proinflammatory TH17/TH2 cell antiviral cytokine profile and PC2 a more antiviral cytokine profile with patients tending to display one or the other of these. Severity of disease and stage of illness did not show any correlation with PC1 profiles; however, significant differences were seen in patients with high PC1 profiles vs. lower for a number of individual clinical parameters: High PC1 patients showed a reduced number of febrile days, but higher maximum urine output, higher creatinine levels, and lower platelet levels. Overall, the results of this study point towards a stronger proinflammatory profile occurring in younger NE patients, this being associated with markers of acute kidney injury and low levels of high-density cholesterol. This is consistent with previous work indicating that the pathology of NE is immune driven, with an inflammatory immune response being associated with disease and that this immune response is more extreme in younger patients.
... 3 In Asia, the HFRS-causing pathogenic agents predominantly include Hantaan virus (HTNV), Seoul virus (SEOV), and Amur virus (AMRV); whereas in Europe, mainly comprising Puumala virus (PUUV), Sochi virus (SOCV), and Dobrava-Belgrade virus (DOBV). [3][4][5] HFRS is currently found in more than 70 countries, but it is mainly endemic in Asian and European continents, especially in China, Russia, and Korea. 1,3,4 Among them, China is always hit the hardest country with HFRS in the past, accounting for around 70-90% documented case notifications throughout the world each year. ...
Objective
We aim to examine the adequacy of an innovation state-space modeling framework (called TBATS) in forecasting the long-term epidemic seasonality and trends of hemorrhagic fever with renal syndrome (HFRS).
Methods
The HFRS morbidity data from January 1995 to December 2020 were taken, and subsequently, the data were split into six different training and testing segments (including 12, 24, 36, 60, 84, and 108 holdout monthly data) to investigate its predictive ability of the TBATS method, and its forecasting performance was compared with the seasonal autoregressive integrated moving average (SARIMA).
Results
The TBATS (0.27, {0,0}, -, {<12,4>}) and SARIMA (0,1,(1,3))(0,1,1)12 were selected as the best TBATS and SARIMA methods, respectively, for the 12-step ahead prediction. The mean absolute deviation, root mean square error, mean absolute percentage error, mean error rate, and root mean square percentage error were 91.799, 14.772, 123.653, 0.129, and 0.193, respectively, for the preferred TBATS method and were 144.734, 25.049, 161.671, 0.203, and 0.296, respectively, for the preferred SARIMA method. Likewise, for the 24-, 36-, 60-, 84-, and 108-step ahead predictions, the preferred TBATS methods produced smaller forecasting errors over the best SARIMA methods. Further validations also suggested that the TBATS model outperformed the Error-Trend-Seasonal framework, with little exception. HFRS had dual seasonal behaviors, peaking in May–June and November–December. Overall a notable decrease in the HFRS morbidity was seen during the study period (average annual percentage change=−6.767, 95% confidence intervals: −10.592 to −2.778), and yet different stages had different variation trends. Besides, the TBATS model predicted a plateau in the HFRS morbidity in the next ten years.
Conclusion
The TBATS approach outperforms the SARIMA approach in estimating the long-term epidemic seasonality and trends of HFRS, which is capable of being deemed as a promising alternative to help stakeholders to inform future preventive policy or practical solutions to tackle the evolving scenarios.
... Our research showed that of the patients assessed, middle-aged patients were predominantly affected. Furthermore, most patients were men, and the main occupation was farming, consistent with the findings of previous reports 1, 13,14,25 . The risk prediction model showed that cultivated land was the highest risk factor for HFRS 16 . ...
Hemorrhagic fever with renal syndrome (HFRS), a serious threat to human health, is mainly transmitted by rodents in Eurasia. The risk of disease differs according to sex, age, and occupation. Further, temperature and rainfall have some lagging effects on the occurrence of the disease. The quantitative data for these factors in the Tai’an region of China are still unknown. We used a forest map to calculate the risk of HFRS in different populations and used four different mathematical models to explain the relationship between time factors, meteorological factors, and the disease. The results showed that compared with the whole population, the relative risk in rural medical staff and farmers was 5.05 and 2.00, respectively (p < 0.05). Joinpoint models showed that the number of cases decreased by 33.32% per year from 2005 to 2008 (p < 0.05). The generalized additive model showed that air temperature was positively correlated with disease risk from January to June, and that relative humidity was negatively correlated with risk from July to December. From January to June, with an increase in temperature, after 15 lags, the cumulative risk of disease increased at low temperatures. From July to December, the cumulative risk decreased with an increase in the relative humidity. Rural medical staff, farmers, men, and middle-aged individuals were at a high risk of HFRS. Moreover, air temperature and relative humidity are important factors that affect disease occurrence. These associations show lagged and differing effects according to the season.
... In recent years, numerous HFRS cases have been reported in the Fujian, Zhejiang and Shaanxi provinces of China. According to the Chinese Center for Disease Control and Prevention, the number of cases in Zhejiang, Jiangsu and other provinces have shown an upward trend since 2011 (Liu et al., 2016;Zou et al., 2016;Wu H. et al., 2018;Xiang et al., 2018). In this study, we analyzed the gene sequences of HV isolated from different regions of China and other countries, and traced their evolution through genetic analysis. ...
Hemorrhagic fever with renal syndrome (HFRS) is caused by hantavirus (HV) infection, and is prevalent across Europe and Asia (mainly China). The genetic variation and wide host range of the HV family may lead to vaccine failure. In this study, we analyzed the gene sequences of HV isolated from different regions of China in order to trace the molecular evolution of HV and the epidemiological trends of HFRS. A total of 16,6975 HFRS cases and 1,689 HFRS-related deaths were reported from 2004 to 2016, with the average annual incidence rate of 0.9674 per 100,000, 0.0098 per 100,000 mortality rate, and case fatality rate 0.99%. The highest number of cases were detected in 2004 (25,041), and after decreasing to the lowest numbers (8,745) in 2009, showed an incline from 2010. The incidence of HFRS is the highest in spring and winter, and three times as many men are affected as women. In addition, farmers account for the largest proportion of all cases. The main hosts of HV are Rattus norvegicus and Apodemus agrarius, and the SEOV strain is mainly found in R. norvegicus and Niviventer confucianus. Phylogenetic analysis showed that at least 10 HTNV subtypes and 6 SEOV subtypes are endemic to China. We found that the clustering pattern of M genome segments was different from that of the S segments, indicating the possibility of gene recombination across HV strains. The recent increase in the incidence of HFRS may be related to climatic factors, such as temperature, relative humidity and hours of sunshine, as well as biological factors like rodent density, virus load in rodents and genetic variation. The scope of vaccine application should be continuously expanded, and surveillance measures and prevention and control strategies should be improved to reduce HFRS infection in China.
