Air pollution and hemorrhagic fever with renal syndrome in South Korea: an ecological correlation study

Influence of Climatic Factors on Human Hantavirus Infections in Latin America and the Caribbean: A Systematic Review

BACKGROUND

With the current climate change crisis and its influence on infectious disease transmission there is an increased desire to understand its impact on infectious diseases globally. Hantaviruses are found worldwide, causing infectious diseases such as haemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS)/hantavirus pulmonary syndrome (HPS) in tropical regions such as Latin America and the Caribbean (LAC). These regions are inherently vulnerable to climate change impacts, infectious disease outbreaks and natural disasters. Hantaviruses are zoonotic viruses present in multiple rodent hosts resident in Neotropical ecosystems within LAC and are involved in hantavirus transmission.

METHODS

We conducted a systematic review to assess the association of climatic factors with human hantavirus infections in the LAC region. Literature searches were conducted on MEDLINE and Web of Science databases for published studies according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) criteria. The inclusion criteria included at least eight human hantavirus cases, at least one climatic factor and study from > 1 LAC geographical location.

RESULTS

In total, 383 papers were identified within the search criteria, but 13 studies met the inclusion criteria ranging from Brazil, Chile, Argentina, Bolivia and Panama in Latin America and a single study from Barbados in the Caribbean. Multiple mathematical models were utilized in the selected studies with varying power to generate robust risk and case estimates of human hantavirus infections linked to climatic factors. Strong evidence of hantavirus disease association with precipitation and habitat type factors were observed, but mixed evidence was observed for temperature and humidity.

CONCLUSIONS

The interaction of climate and hantavirus diseases in LAC is likely complex due to the unknown identity of all vertebrate host reservoirs, circulation of multiple hantavirus strains, agricultural practices, climatic changes and challenged public health systems. There is an increasing need for more detailed systematic research on the influence of climate and other co-related social, abiotic, and biotic factors on infectious diseases in LAC to understand the complexity of vector-borne disease transmission in the Neotropics.

Geographical clustering of Hantavirus isolates from Apodemus agrarius identified in the Republic of Korea indicate the emergence of a new Hantavirus genotype

AIM AND METHODOLOGY

Several studies on hantavirus evolution have shown that genetic reassortment plays an important role in the evolution and epidemiology of this disease. To understand the genetic epidemiology of human hantaviruses, samples from rodent reservoirs were subjected to reverse-transcription nested polymerase chain reaction (RT-N-PCR) targeting the L- and S-segments of the hantavirus genome.

RESULTS

Positive isolates from Gwangju, Boseong-gun (Jeollanam-do Province), and Jeju Island were confirmed as Hantaan virus using DNA sequencing. Phylogenetic analysis showed that all isolates grouped together as Hantaan virus but with each region forming a distinct cluster. In addition, these three clusters were distinct from other Hantaan isolates reported in previous studies from Korea and its neighboring countries China and Russia.

CONCLUSION

This suggests Hantaan viruses exhibit a considerable degree of geographical clustering, and there may be a novel Hantaan genotype in southwestern ROK. This study helps expand our knowledge regarding the emergence of new hantavirus strains and their degree of geographical variation.

IMPORTANCE

Hantaan virus, a pathogenic prototype hantavirus carried by Apodemus agrarius, is found throughout China, Russia, and Korea. Here, we examined the genetic diversity of hantaviruses to expand our knowledge regarding the emergence of new hantavirus strains and their degree of geographical variation. We found that hantaan viruses show a considerable degree of geographical clustering, which may allude to the development of a new genotype variant in the southwestern region of the ROK.

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

Objective

The 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.

Methods

Monthly 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.

Results

Statistical 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.

Conclusion

This 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.

Long-term retrospective observation reveals stabilities and variations of hantavirus infection in Hebei, China

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) is an emerging zoonotic infectious disease caused by hantaviruses which circulate worldwide. So far, it was still considered as one of serious public health problems in China. The present study aimed to reveal the stabilities and variations of hantavirus infection in Hebei province located in North China through a long-term retrospective observation.

METHODS

The epidemiological data of HFRS cases from all 11 cities of Hebei province since 1981 through 2016 were collected and descriptively analyzed. The rodent densities, species compositions and virus-carrying rates of different regions were collected from six separated rodent surveillance points which set up since 2007. The molecular diversity and phylogenetic relationship of hantaviruses circulating among rodents were analyzed based on partial viral glycoprotein gene.

RESULTS

HFRS cases have been reported every year in Hebei province, since the first local case was identified in 1981. The epidemic history can be artificially divided into three phases and a total of 55,507 HFRS cases with 374 deaths were reported during 1981-2016. The gender and occupational factors of susceptible population were invarible throughout, however age of that was gradually aging. The annual outbreak peak always present in spring, while the main epidemic region had gradully altered from south to northeast. Surveillance of rodents revealed that residential rodents significantly possessed higher density and virus-carring rate than field rodents. The house rat, Rattus norvegicus, was the dominant rodent species and Seoul virus S3 sub-genotype which is continued but slightly evolving perhaps to be the sole pathogen for local HFRS cases of Hebei province.

CONCLUSIONS

This long-term province-wide surveillance and epidemiological analysis has revealed the stabilities and variations of hantavirus infection in North China. In order to improve current prevention and control strategies of HFRS in China, all surveillance should be continuously enhanced and variations should be paid more attentions.

Comparison of autoregressive integrated moving average model and generalised regression neural network model for prediction of haemorrhagic fever with renal syndrome in China: a time-series study

OBJECTIVES

Haemorrhagic fever with renal syndrome (HFRS) is a serious threat to public health in China, accounting for almost 90% cases reported globally. Infectious disease prediction may help in disease prevention despite some uncontrollable influence factors. This study conducted a comparison between a hybrid model and two single models in forecasting the monthly incidence of HFRS in China.

DESIGN

Time-series study.

SETTING

The People's Republic of China.

METHODS

Autoregressive integrated moving average (ARIMA) model, generalised regression neural network (GRNN) model and hybrid ARIMA-GRNN model were constructed by R V.3.4.3 software. The monthly reported incidence of HFRS from January 2011 to May 2018 were adopted to evaluate models' performance. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were adopted to evaluate these models' effectiveness. Spatial stratified heterogeneity of the time series was tested by month and another GRNN model was built with a new series.

RESULTS

The monthly incidence of HFRS in the past several years showed a slight downtrend and obvious seasonal variation. A total of four plausible ARIMA models were built and ARIMA(2,1,1) (2,1,1)12 model was selected as the optimal model in HFRS fitting. The smooth factors of the basic GRNN model and the hybrid model were 0.027 and 0.043, respectively. The single ARIMA model was the best in fitting part (MAPE=9.1154, MAE=89.0302, RMSE=138.8356) while the hybrid model was the best in prediction (MAPE=17.8335, MAE=152.3013, RMSE=196.4682). GRNN model was revised by building model with new series and the forecasting performance of revised model (MAPE=17.6095, MAE=163.8000, RMSE=169.4751) was better than original GRNN model (MAPE=19.2029, MAE=177.0356, RMSE=202.1684).

CONCLUSIONS

The hybrid ARIMA-GRNN model was better than single ARIMA and basic GRNN model in forecasting monthly incidence of HFRS in China. It could be considered as a decision-making tool in HFRS prevention and control.

Current State of Research on the Risk of Morbidity and Mortality Associated with Air Pollution in Korea

PURPOSE

The effects of air pollution on health can vary regionally. Our goal was to comprehensively review previous epidemiological studies on air pollution and health conducted in Korea to identify future areas of potential study.

MATERIALS AND METHODS

We systematically searched all published epidemiologic studies examining the association between air pollution and occurrence of death, diseases, or symptoms in Korea. After classifying health outcomes into mortality, morbidity, and health impact, we summarized the relationship between individual air pollutants and health outcomes.

RESULTS

We analyzed a total of 27 studies that provided 104 estimates of the quantitative association between risk of mortality and exposure to air pollutants, including particulate matter with aerodynamic diameter less than 10 μm, particulate matter with aerodynamic diameter less than 2.5 μm, sulfur dioxide, nitrogen dioxide, ozone, and carbon monoxide in Korea between January 1999 and July 2018. Regarding the association with morbidity, there were 38 studies, with 98 estimates, conducted during the same period. Most studies examined the short-term effects of air pollution using a time series or case-crossover study design; only three cohort studies that examined long-term effects were found. There were four health impact studies that calculated the attributable number of deaths or disability-adjusted life years due to air pollution.

CONCLUSION

There have been many epidemiologic studies in Korea regarding air pollution and health. However, the present review shows that additional studies, especially cohort and experimental studies, are needed to provide more robust and accurate evidence that can be used to promote evidence-based policymaking.

The ecological dynamics of hantavirus diseases: From environmental variability to disease prevention largely based on data from China

Hantaviruses can cause hantavirus pulmonary syndrome (HPS) in the Americas and hemorrhagic fever with renal syndrome (HFRS) in Eurasia. In recent decades, repeated outbreaks of hantavirus disease have led to public concern and have created a global public health burden. Hantavirus spillover from natural hosts into human populations could be considered an ecological process, in which environmental forces, behavioral determinants of exposure, and dynamics at the human–animal interface affect human susceptibility and the epidemiology of the disease. In this review, we summarize the progress made in understanding hantavirus epidemiology and rodent reservoir population biology. We mainly focus on three species of rodent hosts with longitudinal studies of sufficient scale: the striped field mouse (Apodemus agrarius, the main reservoir host for Hantaan virus [HTNV], which causes HFRS) in Asia, the deer mouse (Peromyscus maniculatus, the main reservoir host for Sin Nombre virus [SNV], which causes HPS) in North America, and the bank vole (Myodes glareolus, the main reservoir host for Puumala virus [PUUV], which causes HFRS) in Europe. Moreover, we discuss the influence of ecological factors on human hantavirus disease outbreaks and provide an overview of research perspectives.

Meteorological factors and risk of hemorrhagic fever with renal syndrome in Guangzhou, southern China, 2006-2015

Background

The epidemic tendency of hemorrhagic fever with renal syndrome (HFRS) is on the rise in recent years in Guangzhou. This study aimed to explore the associations between meteorological factors and HFRS epidemic risk in Guangzhou for the period from 2006–2015.

Methods

We obtained data of HFRS cases in Guangzhou from the National Notifiable Disease Report System (NNDRS) during the period of 2006–2015. Meteorological data were obtained from the Guangzhou Meteorological Bureau. A negative binomial multivariable regression was used to explore the relationship between meteorological variables and HFRS.

Results

The annual average incidence was 0.92 per 100000, with the annual incidence ranging from 0.64/100000 in 2009 to 1.05/100000 in 2012. The monthly number of HFRS cases decreased by 5.543% (95%CI -5.564% to -5.523%) each time the temperature was increased by 1°C and the number of cases decreased by 0.075% (95%CI -0.076% to -0.074%) each time the aggregate rainfall was increased by 1 mm. We found that average temperature with a one-month lag was significantly associated with HFRS transmission.

Conclusions

Meteorological factors had significant association with occurrence of HFRS in Guangzhou, Southern China. This study provides preliminary information for further studies on epidemiological prediction of HFRS and for developing an early warning system.

The prevalence of HFRS was on the rise in recent years, especially in the large and medium-sized cities in China. We obtained data of HFRS cases in Guangzhou from the National Notifiable Disease Report System (NNDRS) during the period of 2006–2015. Meteorological data were obtained from the Guangzhou Meteorological Bureau. A negative binomial multivariable regression was used to explore the relationship between meteorological variables and HFRS. Meteorological factors had significant association with occurrence of HFRS in Guangzhou, Southern China. This study provides preliminary information for further studies on epidemiological prediction of HFRS and for developing an early warning system.

Stochastic Periodic Solution of a Susceptible-Infective Epidemic Model in a Polluted Environment under Environmental Fluctuation

It is well known that the pollution and environmental fluctuations may seriously affect the outbreak of infectious diseases (e.g., measles). Therefore, understanding the association between the periodic outbreak of an infectious disease and noise and pollution still needs further development. Here we consider a stochastic susceptible-infective (SI) epidemic model in a polluted environment, which incorporates both environmental fluctuations as well as pollution. First, the existence of the global positive solution is discussed. Thereafter, the sufficient conditions for the nontrivial stochastic periodic solution and the boundary periodic solution of disease extinction are derived, respectively. Numerical simulation is also conducted in order to support the theoretical results. Our study shows that (i) large intensity noise may help the control of periodic outbreak of infectious disease; (ii) pollution may significantly affect the peak level of infective population and cause adverse health effects on the exposed population. These results can help increase the understanding of periodic outbreak patterns of infectious diseases.

Is short-term exposure to ambient fine particles associated with measles incidence in China? A multi-city study

BACKGROUND

China's rapid economic development has resulted in severe particulate matter (PM) air pollution and the control and prevention of infectious disease is an ongoing priority. This study examined the relationships between short-term exposure to ambient particles with aerodynamic diameter ≤2.5µm (PM_2.5) and measles incidence in China.

METHODS

Data on daily numbers of new measles cases and concentrations of ambient PM_2.5 were collected from 21 cities in China during Oct 2013 and Dec 2014. Poisson regression was used to examine city-specific associations of PM_2.5 and measles, with a constrained distributed lag model, after adjusting for seasonality, day of the week, and weather conditions. Then, the effects at the national scale were pooled with a random-effect meta-analysis.

RESULTS

A 10µg/m³ increase in PM_2.5 at lag 1day, lag 2day and lag 3day was significantly associated with increased measles incidence [relative risk (RR) and 95% confidence interval (CI) were 1.010 (1.003, 1.018), 1.010 (1.003, 1.016) and 1.006 (1.000, 1.012), respectively]. The cumulative relative risk of measles associated with PM_2.5 at lag 1-3 days was 1.029 (95% CI: 1.010, 1.048). Stratified analyses by meteorological factors showed that the PM_2.5 and measles associations were stronger on days with high temperature, low humidity, and high wind speed.

CONCLUSIONS

We provide new evidence that measles incidence is associated with exposure to ambient PM_2.5 in China. Effective policies to reduce air pollution may also reduce measles incidence.

Hantavirus infection: a global zoonotic challenge

Hantaviruses are comprised of tri-segmented negative sense single-stranded RNA, and are members of the Bunyaviridae family. Hantaviruses are distributed worldwide and are important zoonotic pathogens that can have severe adverse effects in humans. They are naturally maintained in specific reservoir hosts without inducing symptomatic infection. In humans, however, hantaviruses often cause two acute febrile diseases, hemorrhagic fever with renal syndrome (HFRS) and hantavirus cardiopulmonary syndrome (HCPS). In this paper, we review the epidemiology and epizootiology of hantavirus infections worldwide.

Modeling and predicting hemorrhagic fever with renal syndrome trends based on meteorological factors in Hu County, China

Background

Hu County is a serious hemorrhagic fever with renal syndrome (HFRS) epidemic area, with notable fluctuation of the HFRS epidemic in recent years. This study aimed to explore the optimal model for HFRS epidemic prediction in Hu.

Methods

Three models were constructed and compared, including a generalized linear model (GLM), a generalized additive model (GAM), and a principal components regression model (PCRM). The fitting and predictive adjusted R² of each model were calculated. Ljung-Box Q tests for fitted and predicted residuals of each model were conducted. The study period was stratified into before (1971–1993) and after (1994–2012) vaccine implementation epochs to avoid the confounding factor of vaccination.

Results

The autocorrelation of fitted and predicted residuals of the GAM in the two epochs were not significant (Ljung-Box Q test, P>.05). The adjusted R² for the predictive abilities of the GLM, GAM, and PCRM were 0.752, 0.799, and 0.665 in the early epoch, and 0.669, 0.756, and 0.574 in the recent epoch. The adjusted R² values of the three models were lower in the early epoch than in the recent epoch.

Conclusions

GAM is superior to GLM and PCRM for monthly HFRS case number prediction in Hu County. A shift in model reliability coincident with vaccination implementation demonstrates the importance of vaccination in HFRS control and prevention.

The spatial analysis on hemorrhagic fever with renal syndrome in Jiangsu province, China based on geographic information system

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) is endemic in mainland China, accounting for 90% of total reported cases worldwide, and Jiangsu is one of the most severely affected provinces. In this study, the authors conducted GIS-based spatial analyses in order to determine the spatial distribution of the HFRS cases, identify key areas and explore risk factors for public health planning and resource allocation.

METHODS

Interpolation maps by inverse distance weighting were produced to detect the spatial distribution of HFRS cases in Jiangsu from 2001 to 2011. Spatio-temporal clustering was applied to identify clusters at the county level. Spatial correlation analysis was conducted to detect influencing factors of HFRS in Jiangsu.

RESULTS

HFRS cases in Jiangsu from 2001 to 2011 were mapped and the results suggested that cases in Jiangsu were not distributed randomly. Cases were mainly distributed in northeastern and southwestern Jiangsu, especially in Dafeng and Sihong counties. It was notable that prior to this study, Sihong county had rarely been reported as a high-risk area of HFRS. With the maximum spatial size of 50% of the total population and the maximum temporal size of 50% of the total population, spatio-temporal clustering showed that there was one most likely cluster (LLR = 624.52, P<0.0001, RR = 8.19) and one second-most likely cluster (LLR = 553.97, P<0.0001, RR = 8.25), and both of these clusters appeared from 2001 to 2004. Spatial correlation analysis showed that the incidence of HFRS in Jiangsu was influenced by distances to highways, railways, rivers and lakes.

CONCLUSION

The application of GIS together with spatial interpolation, spatio-temporal clustering and spatial correlation analysis can effectively identify high-risk areas and factors influencing HFRS incidence to lay a foundation for researching its pathogenesis.