Spatial-temporal characteristics and the epidemiology of haemorrhagic fever with renal syndrome from 2007 to 2016 in Zhejiang Province, China

Epidemiology of Hemorrhagic Fever with Renal Syndrome and Host Surveillance in Zhejiang Province, China, 1990-2021

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).

Epidemiological characteristics and prediction model construction of hemorrhagic fever with renal syndrome in Quzhou City, China, 2005-2022

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.

Scaling law characteristics and spatiotemporal multicomponent analysis of syphilis from 2016 to 2022 in Zhejiang Province, China

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.

Zoonotic pathogens identified in rodents and shrews from four provinces, China, 2015-2022

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.

Estimating the Long-Term Epidemiological Trends and Seasonality of Hemorrhagic Fever with Renal Syndrome in China

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.

Epidemiology of hemorrhagic fever with renal syndrome in Tai'an area

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.

Epidemic Trend and Molecular Evolution of HV Family in the Main Hantavirus Epidemic Areas From 2004 to 2016, in P.R. China

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.

Epidemic Trend and Molecular Evolution of HV Family in the Main Hantavirus Epidemic Areas From 2004 to 2016, in P.R. China

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.

Analysis of the Spread of COVID-19 in the USA with a Spatio-Temporal Multivariate Time Series Model

With the rapid spread of the pandemic due to the coronavirus disease 2019 (COVID-19), the virus has already led to considerable mortality and morbidity worldwide, as well as having a severe impact on economic development. In this article, we analyze the state-level correlation between COVID-19 risk and weather/climate factors in the USA. For this purpose, we consider a spatio-temporal multivariate time series model under a hierarchical framework, which is especially suitable for envisioning the virus transmission tendency across a geographic area over time. Briefly, our model decomposes the COVID-19 risk into: (i) an autoregressive component that describes the within-state COVID-19 risk effect; (ii) a spatiotemporal component that describes the across-state COVID-19 risk effect; (iii) an exogenous component that includes other factors (e.g., weather/climate) that could envision future epidemic development risk; and (iv) an endemic component that captures the function of time and other predictors mainly for individual states. Our results indicate that maximum temperature, minimum temperature, humidity, the percentage of cloud coverage, and the columnar density of total atmospheric ozone have a strong association with the COVID-19 pandemic in many states. In particular, the maximum temperature, minimum temperature, and the columnar density of total atmospheric ozone demonstrate statistically significant associations with the tendency of COVID-19 spreading in almost all states. Furthermore, our results from transmission tendency analysis suggest that the community-level transmission has been relatively mitigated in the USA, and the daily confirmed cases within a state are predominated by the earlier daily confirmed cases within that state compared to other factors, which implies that states such as Texas, California, and Florida with a large number of confirmed cases still need strategies like stay-at-home orders to prevent another outbreak.

ER stress-related molecules induced by Hantaan virus infection in differentiated THP-1 cells

Endoplasmic reticulum stress (ER stress) can be induced by virus infection. In this part, we explored whether Hantaan virus (HTNV) infection could induce ER stress in differentiated THP-1 (dTHP-1) cells. It showed that the mRNA and protein levels of ER stress-related 78 kDa glucose-regulated protein (GRP78, HSPA5) and mRNA levels of X box-binding protein 1 (XBP-1), activating transcription factor 6(ATF6) and PKR-like ER kinase (PERK) after HTNV infection, were significantly higher than that in uninfected control group. However, the mRNA levels of C/EBP homologous protein (CHOP), glucose-regulated protein 94 (GRP94, HSPC4), and inositol-requiring enzyme1 (IRE1) were not significantly different between the infected group and the untreated group in 2 h after virus infection. It is unusual in activating GRP78 but not GRP94. Meanwhile, dTHP-1 cells infected with HTNV at 12 h did not show obvious apoptosis. These results indicated that the HTNV infection could induce the unfolded protein response (UPR) in dTHP-1 cells, without directly leading to cell apoptosis during 12 h after virus infection.

Spatiotemporal dynamics of hemorrhagic fever with renal syndrome in Jiangxi province, China

Historically, Jiangxi province has had the largest HFRS burden in China. However, thus far, the comprehensive understanding of the spatiotemporal distributions of HFRS is limited in Jiangxi. In this study, seasonal decomposition analysis, spatial autocorrelation analysis, and space–time scan statistic analyses were performed to detect the spatiotemporal dynamics distribution of HFRS cases from 2005 to 2018 in Jiangxi at the county scale. The epidemic of HFRS showed the characteristic of bi-peak seasonality, the primary peak in winter (November to January) and the second peak in early summer (May to June), and the amplitude and the magnitude of HFRS outbreaks have been increasing. The results of global and local spatial autocorrelation analysis showed that the HFRS epidemic exhibited the characteristic of highly spatially heterogeneous, and Anyi, Fengxin, Yifeng, Shanggao, Jing’an and Gao’an county were hot spots areas. A most likely cluster, and two secondary likely clusters were detected in 14-years duration. The higher risk areas of the HFRS outbreak were mainly located in Jiangxi northern hilly state, spreading to Wuyi mountain hilly state as time advanced. This study provided valuable information for local public health authorities to design and implement effective measures for the control and prevention of HFRS.

Spatiotemporal characteristics and the epidemiology of tuberculosis in China from 2004 to 2017 by the nationwide surveillance system

Background

China has always been one of the countries with the most serious Tuberculosis epidemic in the world. Our study was to observe the Spatial-temporal characteristics and the epidemiology of Tuberculosis in China from 2004 to 2017 with Joinpoint regression analysis, Seasonal Autoregressive integrated moving average (SARIMA) model, geographic cluster, and multivariate time series model.

Methods

The data of TB from January 2004 to December 2017 were obtained from the notifiable infectious disease reporting system supplied by the Chinese Center for Disease Control and Prevention. The incidence trend of TB was observed by the Joinpoint regression analysis. The Seasonal autoregressive integrated moving average (SARIMA) model was used to predict the monthly incidence. Geographic clusters was employed to analyze the spatial autocorrelation. The relative importance component of TB was detected by the multivariate time series model.

Results

We included 13,991,850 TB cases from January 2004 to December 2017, with a yearly average morbidity of 999,417 cases. The final selected model was the 0 Joinpoint model (P = 0.0001) with an annual average percent change (AAPC) of − 3.3 (95% CI: − 4.3 to − 2.2, P < 0.001). A seasonality was observed across the 14 years, and the seasonal peaks were in January and March every year. The best SARIMA model was (0, 1, 1) X (0, 1, 1)₁₂ which can be written as (1-B) (1-B¹²) X_t = (1–0.42349B) (1–0.43338B¹²) ε_t, with a minimum AIC (880.5) and SBC (886.4). The predicted value and the original incidence data of 2017 were well matched. The MSE, RMSE, MAE, and MAPE of the modelling performance were 201.76, 14.2, 8.4 and 0.06, respectively. The provinces with a high incidence were located in the northwest (Xinjiang, Tibet) and south (Guangxi, Guizhou, Hainan) of China. The hotspot of TB transmission was mainly located at southern region of China from 2004 to 2008, including Hainan, Guangxi, Guizhou, and Chongqing, which disappeared in the later years. The autoregressive component had a leading role in the incidence of TB which accounted for 81.5–84.5% of the patients on average. The endemic component was about twice as large in the western provinces as the average while the spatial-temporal component was less important there. Most of the high incidences (> 70 cases per 100,000) were influenced by the autoregressive component for the past 14 years.

Conclusion

In a word, China still has a high TB incidence. However, the incidence rate of TB was significantly decreasing from 2004 to 2017 in China. Seasonal peaks were in January and March every year. Obvious geographical clusters were observed in Tibet and Xinjiang Province. The relative importance component of TB driving transmission was distinguished from the multivariate time series model. For every provinces over the past 14 years, the autoregressive component played a leading role in the incidence of TB which need us to enhance the early protective implementation.

Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019

Hemorrhagic fever with renal syndrome (HFRS) is seriously endemic in China with 70%~90% of the notified cases worldwide and showing an epidemic tendency of upturn in recent years. Early detection for its future epidemic trends plays a pivotal role in combating this threat. In this scenario, our study investigates the suitability for application in analyzing and forecasting the epidemic tendencies based on the monthly HFRS morbidity data from 2005 through 2019 using the nonlinear model-based self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) methods. The experimental results manifested that the SETAR and LSTAR approaches presented smaller values among the performance measures in both two forecasting subsamples, when compared with the most extensively used seasonal autoregressive integrated moving average (SARIMA) method, and the former slightly outperformed the latter. Descriptive statistics showed an epidemic tendency of downturn with average annual percent change (AAPC) of −5.640% in overall HFRS, however, an upward trend with an AAPC = 1.213% was observed since 2016 and according to the forecasts using the SETAR, it would seemingly experience an outbreak of HFRS in China in December 2019. Remarkably, there were dual-peak patterns in HFRS incidence with a strong one occurring in November until January of the following year, additionally, a weak one in May and June annually. Therefore, the SETAR and LSTAR approaches may be a potential useful tool in analyzing the temporal behaviors of HFRS in China.

Spatial-temporal characteristics of severe fever with thrombocytopenia syndrome and the relationship with meteorological factors from 2011 to 2018 in Zhejiang Province, China

BACKGROUND

Zhejiang Province has the fifth-highest incidence of severe fever with thrombocytopenia syndrome (SFTS) in China. While the top four provinces are all located in northern and central China, only Zhejiang Province is located in the Yangtze River Delta region of southeast China. This study was undertaken to identify the epidemiological characteristics of SFTS in Zhejiang from 2011 to 2018.

METHODS

The epidemic data from SFTS cases in Zhejiang Province from January 2011 to December 2018 were obtained from the China Information Network System of Disease Prevention and Control. Meteorological data were collected from the China Meteorological Data Sharing Service System. A multivariate time series model was used to analyze the heterogeneity of spatial-temporal transmission of the disease. Random forest analysis was performed to detect the importance of meteorological factors and the dose-response association of the incidence of SFTS with these factors.

RESULTS

In total, 412 SFTS cases (49 fatal) were reported from January 2011 to December 2018 in Zhejiang Province, China. The number of SFTS cases and the number of affected counties increased year by year. The case fatality rate in Zhejiang Province was 11.89%, which was the highest in China. Elderly patients and farmers were the most affected. The total effect values of the autoregressive component, spatiotemporal component and endemic component of the model in all ranges were 0.4580, 0.0377 and 0.0137, respectively. There was obvious heterogeneity across counties for the mean values of the spatiotemporal component and the autoregressive component. The autoregressive component was obviously the main factor driving the occurrence of SFTS, followed by the spatiotemporal component. The importance scores of the monthly mean pressure, mean temperature, mean relative humidity, mean two-minute wind speed, duration of sunshine and precipitation were 10.64, 8.34, 8.16, 6.37, 5.35 and 2.81, respectively. The relationship between these factors and the incidence of SFTS is complicated and nonlinear. A suitable range of meteorological factors for this disease was also detected.

CONCLUSIONS

The autoregressive and spatiotemporal components played an important role in driving the transmission of SFTS. Targeted preventive efforts should be made in different areas based on the main component contributing to the epidemic. For most areas, early measures several months ahead of the suitable season for the occurrence of SFTS should be implemented. The level of reporting and diagnosis of this disease should be further improved.

A case report of empty Sella syndrome secondary to Hantaan virus infection and review of the literature

RATIONALE

Bleeding in the anterior pituitary lobe leading to tissue necrosis occurs in the acute stage of severe clinical forms of hemorrhagic fever with renal syndrome (HFRS), while atrophy of the anterior pituitary lobe with diminution of the gland function occurs after the recovery stage. The relationship between Hantaan virus infection and empty Sella syndrome (ESS) has rarely been reported.

PATIENT CONCERNS

This patient was a 54-year-old previously healthy Chinese male. He presented with fever, headache, and backache with dizziness and oliguria. Physical examination was notable for hypotension and the signs of conjunctival suffusion. His platelets decreased, and his urine protein was positive. Hantaan virus IgM and virus RNA were positive.

DIAGNOSIS

He was diagnosed as having HFRS. In his diuretic phase, his 24-hour urine volume was maintained at 10,000 mL, and his blood pressure was higher for a week. Then, he was diagnosed as having ESS after a series of examinations.

INTERVENTIONS

Hormone replacement therapy was given to this patient after the diagnosis "ESS" was made.

OUTCOMES

The patient's symptoms improved, and he was discharged from the hospital soon after hormone replacement therapy.

LESSONS

Pituitary function examination and brain magnetic resonance imaging (MRI) need to be considered to scan for ESS and panhypopituitarism in the patients with HFRS accompanied by diabetes insipidus.

A case-control study on the risk factors for hemorrhagic fever with renal syndrome

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) is an endemic communicable disease in China, accounting for 90% of total reported cases worldwide. In this study, the authors want to investigate the risk factors for HFRS in recent years to provide the prevention and control advices.

METHODS

A community-based, 1:2 matched case-control study was carried out to investigate the risk factors for HFRS. Cases were defined as laboratory-confirmed cases that tested positive for hantavirus-specific IgM antibodies. Two neighbourhood controls of each case were selected by sex, age and occupation. Standardized questionnaires were used to collect information and identify the risk factors for HFRS.

RESULTS

Eighty-six matched pairs were investigated in the study. The median age of the cases was 55.0 years, 72.09% were male, and 73.26% were farmers. In the multivariate logistic regression analysis, cleaning spare room at home (OR = 3.310, 95%CI 1.335-8.210) was found to be risk factor for infection; storing food and crops properly (OR = 0.279 95%CI 0.097-0.804) provided protection from infection.

CONCLUSION

Storing food and crops properly seemed to be protective factor, which was important for HFRS prevention and control. More attention should be paid to promote comprehensive health education and behaviour change among high-risk populations in the HFRS endemic area.

Improved susceptible-infectious-susceptible epidemic equations based on uncertainties and autocorrelation functions

Compartmental equations are primary tools in the study of disease spreading processes. They provide accurate predictions for large populations but poor results whenever the integer nature of the number of agents is evident. In the latter instance, uncertainties are relevant factors for pathogen transmission. Starting from the agent-based approach, we investigate the role of uncertainties and autocorrelation functions in the susceptible-infectious-susceptible (SIS) epidemic model, including their relationship with epidemiological variables. We find new differential equations that take uncertainties into account. The findings provide improved equations, offering new insights on disease spreading processes.

A One Health perspective to identify environmental factors that affect Rift Valley fever transmission in Gezira state, Central Sudan

BACKGROUND

Rift Valley fever (RVF) is a zoonotic viral vector-borne disease that affects both animals and humans and leads to severe economic consequences. RVF outbreaks are triggered by a favorable environment and flooding, which enable mosquitoes to proliferate and spread the virus further. RVF is endemic to Africa and has spread to Saudi Arabia and Yemen. There is great concern that RVF may spread to previously unaffected geographic regions due to climate change. We aimed to better understand the spatiotemporal pattern of the 2007 RVF outbreak at the human-animal-environment interface and to determine environmental factors that may have effects on RVF occurrence in Gezira state, Sudan.

MATERIALS AND METHODS

We compiled epidemiological, environmental, and spatiotemporal data across time and space using remote sensing and a geographical information system (GIS). The epidemiological data included 430 RVF human cases as well as human and animal population demographic data for each locality. The cases were collected from 41 locations in Gezira state. The environmental data represent classified land cover during 2007, the year of the RVF outbreak, and the average of the Normalized Difference Vegetation Index (NDVI) for 6 months of 2007 is compared with those of 2010 and 2014, when there was no RVF outbreak. To determine the effect of the environmental factors such as NDVI, soil type, and RVF case's location on the Blue Nile riverbank on RVF incidence in Gezira state, a multilevel logistic regression model was carried out.

RESULTS

We found that the outbreak in Gezira state occurred as a result of interaction among animals, humans, and the environment. The multilevel logistic regression model (F = 43,858, df = 3, p = 0.000) explained 23% of the variance in RVF incidence due to the explanatory variables. Notably, soil type (β = 0.613, t = 11.284, p = 0.000) and NDVI (β = - 0.165, t = - 3.254, p = 0.001) were the explanatory environmental factors that had significant effects on RVF incidence in 2007 in Gezira state, Sudan.

CONCLUSIONS

Precise remote sensing and the GIS technique, which rely on environmental indices such as NDVI and soil type that are satellite-derived, can contribute to establishing an early warning system for RVF in Sudan.Future preparedness and strengthening the capacity of regional laboratories are necessary for early notification of outbreaks in animals and humans.

Epidemiological and time series analysis of haemorrhagic fever with renal syndrome from 2004 to 2017 in Shandong Province, China

Shandong Province is an area of China with a high incidence of haemorrhagic fever with renal syndrome (HFRS); however, the general epidemic trend of HFRS in Shandong remains unclear. Therefore, we established a mathematical model to predict the incidence trend of HFRS and used Joinpoint regression analysis, a generalised additive model (GAM), and other methods to evaluate the data. Incidence data from the first half of 2018 were included in a range predicted by a modified sum autoregressive integrated moving average-support vector machine (ARIMA-SVM) combination model. The highest incidence of HFRS occurred in October and November, and the annual mortality rate decreased by 7.3% (p < 0.05) from 2004 to 2017. In cold months, the incidence of HFRS increased by 4%, −1%, and 0.8% for every unit increase in temperature, relative humidity, and rainfall, respectively; in warm months, this incidence changed by 2%, −3%, and 0% respectively. Overall, HFRS incidence and mortality in Shandong showed a downward trend over the past 10 years. In both cold and warm months, the effects of temperature, relative humidity, and rainfall on HFRS incidence varied. A modified ARIMA-SVM combination model could effectively predict the occurrence of HFRS.