The authors have declared that no competing interests exist.
Conceived and designed the experiments: HX HNL BG. Performed the experiments: HYT HNL LSD. Analyzed the data: BC XLL BX. Contributed reagents/materials/analysis tools: NB HYT LDG HWW. Wrote the paper: HX. XJL HSY BYC.
China 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.
Data 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,
The 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.
Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne disease caused by hantaviruses, is characterized by fever, haemorrhage, headache, back pain, abdominal pain, and acute kidney injury. China has the highest incidence of HFRS worldwide. Reported cases account for 90% of the total global cases. Approximately 1.4 million HFRS cases were reported in China between 1950 and 2010. During the same time period, >46 000 people died from HFRS, and the fatality rate was 3.29%. A great deal of interest and excitement has developed recently for understanding the role of the environment in the transmission of HFRS. Our article provides evidence that rodent density and behavior depend on natural factors. Changes in animal reservoirs may lead to the emergence of new epidemics and threats to human health. However, economic development may promote a more residential environment, which could inhibit disease transmission from animals to humans by limiting their contact. We combined data about the rodent reservoir, the natural environment, and socioeconomic factors in the model. The results will be helpful for making and prioritizing preventive measures.
Hemorrhagic fever with renal syndrome (HFRS) is a natural focal disease characterized by fever, hemorrhagic manifestations, and acute renal dysfunction. HFRS is mainly transmitted by rodents
China has the highest incidence of HFRS worldwide. Reported cases account for 90% of the total number of global cases. Approximately 1.4 million HFRS cases were reported in China between 1950 and 2010
HFRS is widely transmitted from rodents to humans through contact with saliva, urine or excreta from infected rodents
Past evidence has demonstrated that outbreaks of diseases such as schistosomiasis, malaria, tuberculosis and plague are affected by environmental factors (e.g., geography, climate, and zoology), and are affected and restricted by socioeconomic factors (e.g., social institution, economic status, and population mobility)
The aim of this study was to analyze the quantitative relationship between HFRS transmission and environmental variables, to forecast the trend in prevalence of HFRS transmission, and to reveal the transmission pattern from data on HFRS cases, rodent host populations, and environmental variables (natural variables and social variables) in Chenzhou from 2006 to 2010. Natural variables included rodent density, the normalized difference vegetation index (NDVI) for cultivated land, monthly average temperature, monthly average rainfall and monthly average relative humidity. Social variables included gross domestic product (GDP) and the urbanization rate. The results may lead to the discovery of epidemic factors that are important for control of HFRS.
The study area covers Chenzhou, located in a subtropical region of Hunan Province in Central China. Chenzhou is located between latitude 24°53′ and 26°50′ north, and longitude 112°13′ and 114°14′ east. It is 217 km wide and 202 km long, with a total land area of 19,400 km2. The region consists of two municipal districts (Beihu and Suxian) and nine counties (Guiyang, Yizhang, Yongxing, Jiahe, Linwu, Rucheng, Guidong, Anren and Zixing), and a total population of about 4.6 million people (
From 2006 to 2010, data from cases of HFRS in Chenzhou were obtained from the Hunan Center for Disease Control and Prevention (CDC). All of the cases were initially diagnosed based on clinical symptoms using diagnostic criteria from the Ministry of Health of the People's Republic of China. Blood samples were collected for serologic identification from all suspect cases. Samples were analyzed at the Hunan CDC laboratory. Detailed procedures can be found in published articles
Surveillance of hantavirus infections among rodent hosts from 2006 to 2010 was conducted once per month for three consecutive nights. At least 300 medium-sized steel traps were set each night (baited with peanuts) and were recovered in the morning. More than 100 of these traps were placed indoors at approximately 12- to 15-meter intervals, and more than 200 traps were placed outdoors (every 5 meters in each row, with 50 meters between rows). A total of 698 rodents were captured out of 36,243 effective traps. “Relative rodent density”, used as an indicator of abundance, was calculated as the number of rodents captured, divided by the number of traps (
Data | Sources | Illustration |
Patient data | Hunan Center for Disease Control and Prevention | Case reports |
Rodent density | Hunan Center for Disease Control and Prevention | Monitoring reports |
NDVI | International Scientific Data Service Platform | Remote sensing images |
Temperature | China Meteorological Data Sharing Service System | Site data |
Precipitation | China Meteorological Data Sharing Service System | Site data |
Humidity | China Meteorological Data Sharing Service System | Site data |
The urbanization rate | Hunan Statistics Yearbook | Statistics reports |
GDP | Hunan Statistics Yearbook | Statistics reports |
Meteorological data (monthly average temperature, monthly average relative humidity, and monthly precipitation) for the 2006 to 2010 period were obtained from the China Meteorological Data Sharing Service System (
Land use data were obtained from the Second National Land Survey and were categorized as to cultivated land, forest, grass, or residential land. The data set was analyzed in ArcGIS 9.3 (ESRI Inc., Redlands, CA, USA) and included a digital map of Chenzhou (1∶50000), geocoding, case information, and population information. The data for each variable were converted to the same geographic projection and clipped to the study area.
The present study was reviewed by the research institutional review board of the Hunan CDC. The review board determined that utilization of disease surveillance data did not require oversight by an ethics committee. Because the data were publicly available secondary data and were analyzed anonymously, no ethics statement was required for the work. The methods did not include animal experimentation, so it was not necessary to obtain an animal ethics license from the Animal Experiment Board. The species captured in this study were not protected wildlife and were not included in the China Species Red List.
The principal component analysis was performed using the 2006–2010 data on natural factors (relative rodent density, NDVI, monthly average temperature, monthly rainfall and monthly average relative humidity) and social factors (GDP and urbanization rate). Four principal components that included three natural components (F1: rodent density, NDVI for rice paddies and temperature; F2: rainfall and relative humidity, and F3: rodent density and relative humidity) and one social component (F4: GDP and the urbanization rate of the population) were extracted.
Cross correlation analysis, adjusted for seasonality, was performed to infer the time-lag effects between variables. Each sequence of variables was filtered to convert it to white noise before proceeding with the cross correlation analysis. The correlation between the residual sequence of HFRS incidence and the residual sequences of the environmental variables (F1, F2, F3, F4), lagged 0∼6 months, was then calculated.
To confirm the correlation between lagged variables and HFRS incidence, the polynomial distributed lag (PDL) model with a lagged dependent variable was used to examine the contribution of various variables to HFRS incidence. The PDL model was:
A total of 321 HFRS cases were reported in Chenzhou, and yearly average HFRS incidence remained stable, during the study period. Yearly average HFRS was 1.53/100,000 (71 cases) in 2006, 1.59/100,000 (74 cases) in 2007, 1.29/100,000 (61 cases) in 2008, 1.41/100,000 (67 cases) in 2009, and 0.96/100,000 (48 cases) in 2010, Analysis of monthly HFRS cases revealed that HFRS incidence was higher from November to January and lower in March, April, July, and August (
A total of 251 rodents were captured at specific industry monitoring sites (catering industry and processing industry in Beihu and Suxian Districts). Captured rodents consisted mostly of the species
Others species | ||||||
68 | 48 | 35 | 11 | 4 | 8 | |
48 | 24 | 56 | 9 | 1 | 0 | |
47 | 7 | 74 | 5 | 0 | 2 | |
39 | 7 | 93 | 5 | 0 | 1 | |
34 | 7 | 65 | 3 | 0 | 0 |
The monthly NDVI for cultivated land ranged between 0.3 to 0.8. The NDVI increased from January to July, and then decreased each month after the peak of variation from August to October. The peak HFRS incidence was preceded by the peak NDVI and the peak monthly average temperature and monthly average rainfall, with a 3∼4 month lag (
The results of the analysis of the relationship between HFRS incidence and socioeconomic factors (GDP and urbanization rate) indicated that the urbanization rate was significant negatively correlated with HFRS incidence (r = −0.903,
The results of the principal component analysis revealed that component 1 (F1), component 2 (F2) and component 3 (F3) accounted for 91.66% of the total variability in natural factors. F1 was closely associated with rodent density, NDVI, and temperature. F2 was closely associated with rainfall and humidity. F3 was closely associated with rodent density and humidity (
Principal components | Rodent density | NDVI | Temperature | Precipitation | Relative humidity | Urbanization rate | GDP |
0.421 | 0.556 | 0.572 | 0.207 | −0.378 | |||
0.059 | 0.079 | −0.052 | 0.681 | 0.555 | |||
0.759 | 0.015 | −0.946 | −0.400 | 0.504 | |||
0.574 | 0.608 |
The correlation between HFRS incidence, the variables, the three natural components (F1, F2, F3), and the socioeconomic component (F4) were calculated with a lag of 0∼6 months. Monthly HFRS incidence was positively correlated with rodent density with a 6-month lag (r = 0.354,
Variables | Lag 0 | Lag 1 | Lag 2 | Lag 3 | Lag 4 | Lag 5 | Lag 6 |
−0.173 | −0.114 | −0.076 | 0.092 | 0.256 | 0.127 | 0.354 |
|
−0.277 | −0.048 | 0.227 | 0.431 | 0.476 | 0.490 |
0.379 | |
−0.388 | −0.136 | 0.11 | 0.33 | 0.475 | 0.515 |
0.417 | |
−0.23 | −0.217 | −0.153 | 0.247 | 0.162 | 0.411 |
0.396 | |
−0.01 | 0.123 | 0.181 | 0.247 |
0.13 | 0.07 | −0.122 | |
0.002 | 0.131 | −0.087 | −0.102 | 0.179 | −0.166 | 0.125 | |
−0.229 | −0.223 | −0.089 | 0.239 | −0.289 |
0.153 | 0.101 | |
0.204 | 0.045 | 0.093 | −0.054 | −0.077 | −0.523 |
−0.136 | |
−0.376 |
−0.114 | −0.221 | −0.283 |
0.088 | −0.141 | −0.069 |
The PDL model yielded the best fit based on the R-squared and AIC (Akaike information criterion). First, three principal components based on natural factors were used to build Model 1 (R2 = 0.656, AIC = 5.023). Socioeconomic factors (F4) were included in Model 2 (R2 = 0.677, AIC = 5.106). Finally, 2nd-order autoregression was considered in Model 3, which indicated that the number of notified HFRS infections in the current month was related to the numbers of cases occurring in the previous 1 and 2 months (R2 = 0.857, AIC = 4.799).
The results of the optimal model (Model 3) indicated that HFRS incidence was affected not only by the natural factors but also by the socioeconomic factors (
MODEL-1 | MODEL-2 | MODEL-3 | |||||||||||
F1 | F2 | F3 | F1 | F2 | F3 | F4 | F1 | F2 | F3 | F4 | lag-1 | lag-2 | |
Constant term | 0.664 | 0.050 | 0.692 | 0.539 | −0.080 | 0.776 | −2.267 | −0.012 | −0.017 | 0.427 | −0.268 | 0.214 | 0.338 |
Linear coefficient | 0.227 | −0.830 | 0.928 | 0.076 | −1.029 | 0.952 | 0.612 | −0.259 | −1.042 | 0.889 | 5.950 | −0.455 | 0.028 |
Quadratic coefficient | 0.118 | 0.741 | 0.815 | −0.002 | 0.502 | 0.825 | −0.619 | −0.034 | 0.732 | 0.892 | −2.077 | −0.283 | |
Cubic coefficient | 0.337 | 1.910 | 0.355 | 0.307 | 1.712 | 0.395 | 2.004 | 0.665 | 2.045 | 0.435 | −3.800 | ||
R2 | 0.656 | 0.677 | 0.857 | ||||||||||
AIC | 5.023 | 5.106 | 4.799 | ||||||||||
RMSE | 2.441 | 2.366 | 1.573 |
To the best of our knowledge, little is known about the combined effect of environmental variations (animal reservoirs, natural and socioeconomic factors) on the transmission and persistence of HFRS. In general terms, rodent density and the extent of high-risk behaviors depend on natural factors. Changes in the animal reservoir may lead to emergence of new epidemics, and threats to human health. However, economic development may improve the residential environment, which could inhibit disease transmission from rodent vectors to humans through decreased contact.
In our analysis, the optimal model revealed that HFRS incidence was positively correlated with rainfall and relative humidity in Chenzhou. Rainfall is an important factor in HFRS morbidity, because increased rainfall provides better growth conditions for vegetation that directly or indirectly provides rodents with food, which leads to increases in rodent populations
Temperature and NDVI were important factors for HFRS epidemics and were positively associated with HFRS incidence. Temperature can affect rodent pregnancy rate, litter size, birth rate, and survival rate, and is an important factor in the fluctuation of rodent population size
HFRS incidence was positively correlated with rodent density. This result indicates that fluctuations in rodent populations had an important effect on HFRS incidence. Rodent population density peaked in March, April, August, and September. The peaks in HFRS cases were in May and June, and from November to January, indicating that HFRS incidence lagged behind rodent density by approximately 2∼3 months. Hantavirus infection rates can increase with increased rodent density if the infected rodents increase their contact with humans
HFRS incidence was negatively correlated with GDP (r = −0.627) and the urbanization rate (r = −0.903). HFRS incidence decreased with the increase in per capita GDP and urbanization rate. These results suggest that economic development may reduce HFRS transmission, which is consistent with the findings of a previous study
Compared to our previous work in Changsha
There were some limitations of this study. First, the monthly average temperature, which was measured in the air, was different from the surface temperature. Surface temperature has a more direct effect on rodents, so it would be more informative to incorporate surface temperature into models of HFRS incidence. Second, HFRS cases were from a passive, instead of active, surveillance system, so some cases may not have been identified. Patients with less serious or less obvious symptoms may not seek medical care, which would result in an underestimate of the true incidence. Finally, the effects of extreme weather conditions (e.g., high temperature, torrential rain, and drought) on the survival and reproduction of rodents, and on HFRS transmission, needs further study. Furthermore, this was a population-level study, and the potential of the ecological fallacy to affect the results is unavoidable in a study of this kind.
In conclusion, changes in the risk of HFRS may be the result of changes in contact between humans and the rodent reservoir, which are caused by changes in natural and socioeconomic factors. The results of our analysis provide theoretical support for this hypothesis and indicate that further study of variation in HFRS incidence would be beneficial for the prevention and control of this disease.
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We wish to express our appreciation to Professor Peng Bi for his helpful comments on an earlier draft of this article.