Landscape Elements and Hantaan Virus–related Hemorrhagic Fever with Renal Syndrome, People’s Republic of China

Liu, Yan; Fang, Li‐Qun; Huang, Huaguo; Zhang, Long-Qi; Dai, Feng; Zhao, Wenjuan; Zhang, Wenyi; Li, Xiaowen; Cao, Wu‐Chun

doi:10.3201/eid1309.061481

Cited by 121 publications

(149 citation statements)

References 9 publications

(9 reference statements)

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“…Data on eco-geographical variables (EGV) possibly contributing to transmission of hantavirus in rodent hosts were collected (Table 1). 13,[18][19][20] The data of each variable were converted to the study's geographic projection (Krasovsky_1940_Albers) and clipped to the study area. Elevation value was derived from a shuttle radar topography mission database with a spatial resolution of 1 km ( http://srtm.csi.cgiar.org ).…”

Section: Methodsmentioning

confidence: 99%

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

Wei¹,

Qian²,

Wang³

et al. 2011

The American Society of Tropical Medicine and Hygiene

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Abstract. Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in Shandong Province, China. In this study, we combined ecologic niche modeling with geographic information systems (GIS) and remote sensing techniques to identify the risk factors and affected areas of hantavirus infections in rodent hosts. Land cover and elevation were found to be closely associated with the presence of hantavirus-infected rodent hosts. The averaged area under the receiver operating characteristic curve was 0.864, implying good performance. The predicted risk maps based on the model were validated both by the hantavirus-infected rodents' distribution and HFRS human case localities with a good fit. These findings have the applications for targeting control and prevention efforts. Ecological niche models. The ENMs were developed to understand environmental variation associated with the distribution of infected rodent reservoirs. 21 A recent introduced presence-only distribution modeling technique-the Maximum Entropy approach, was applied in various domains and achieved high predictive accuracy, [22][23][24][25][26][27][28] and showed the best predictive power across all sample sizes. 3,25,[29][30][31] Detailed descriptions of the Maximum Entropy program (MAXENT, version 3.3.1) can be found in References 25 and 32. There were 33 sample sites positive for hantavirus infection in rodents from the study areas during [2005][2006][2007][2008], and 10,000 background points (2,500 for each year) are sampled by a spatially random method. The importance of EGVs contributing to the distribution of hantavirus infection was determined by three analyses. In the jackknife analysis of the average gain with training and test data, models were respectively created with each individual variable, all the remaining variables and all variables in turn. Next, corresponding results were compared. Second, the average values of area under the curve (AUC) of 10 iterations were compared. Third, the average percentage contribution of each variable was evaluated. In each iteration of the training algorithm, the increase or decrease in regularized gain was added or subtracted with the input of the corresponding variable, giving a heuristic estimate of variable contribution for the model. 25The final model predictors were selected using a stepwise fashion, as saturated models are likely to be oversized, overfitted, or redundant. 33,34 To determine variable significance, several models using the same occurrence data but different variable sets were examined. These included models with single predictors alone, as well as leaving out individual predictors from suites of variables. The loss in modeling performance for individual models were compared with the model generated using all predictors. The algorithm converges to the optimum probable distribution, and the gain is interpreted as representing how much better the distribution fits the sample points than a uniform distribution. 25,29,32 Model evaluation. To validate the accuracy and p...

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Section: Methodsmentioning

confidence: 99%

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

Wei¹,

Qian²,

Wang³

et al. 2011

The American Society of Tropical Medicine and Hygiene

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“…8,20 In addition, temperature, vegetation type, land use, and ENSO have been shown to be associated with the transmission of HFRS in China. 4,6,7,21 Therefore, climate variables related to rodent population dynamics may serve as indicators for the risk of HFRS transmission in humans. [22][23][24] Heilongjiang Province, which is a cold area in northeastern China, was the first identified epidemic foci of HFRS in China.…”

Section: Introductionmentioning

confidence: 99%

“…Hantaan virus is associated with Apodemus agrarius, whereas Seoul virus is associated with Rattus norvegicus and often causes a less severe symptome. 1,4 Although prevention and control measures such as rodent control, vaccination, and environmental management have been implemented in parts of China most at risk, HFRS remains as a serious public health problem and there are approximately 20,000-50,000 new cases annually.…”

Section: Introductionmentioning

confidence: 99%

“…1 This disease is found in more than 30 countries and has become a major public health problem in China, 2,3 It is endemic to 28 of 31 provinces in China, accounts for 90% of the HFRS cases reported globally, and has a case-fatality rate of 3-10%. 4 In China, HFRS is caused mainly by two types of hantaviruses, Hantaan virus and Seoul virus, which coevolved with distinct rodent hosts. Hantaan virus is associated with Apodemus agrarius, whereas Seoul virus is associated with Rattus norvegicus and often causes a less severe symptome.…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8] However, the role of climate factors in the transmission of HFRS differs in different regions. 4,[6][7][8][9][10][11][12] For example, increased grass seed production followed by heavy precipitation, as a result of the El Niñ o Southern Oscillation (ENSO), was found to be associated with higher Peromyscus maniculatus rodent density in southwestern United States and provides reservoirs for Sin Nombre virus. 13,[15][16][17] However, this result was not duplicated in other areas in the United States.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Cui

Li³

et al. 2013

The American Society of Tropical Medicine and Hygiene

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Abstract. The purpose of this study was to quantify the relationship between climate variation and transmission of hemorrhagic fever with renal syndrome (HFRS) in Heilongjiang Province, a highly endemic area for HFRS in China. Monthly notified HFRS cases and climatic data for [2001][2002][2003][2004][2005][2006][2007][2008][2009] in Heilongjiang Province were collected. Using a seasonal autoregressive integrated moving average model, we found that relative humidity with a one-month lag (β = -0.010, P = 0.003) and a three-month lag (β = 0.008, P = 0.003), maximum temperature with a two-month lag (β = 0.082, P = 0.028), and southern oscillation index with a two-month lag (β = -0.048, P = 0.019) were significantly associated with HFRS transmission. Our study also showed that predicted values expected under the seasonal autoregressive integrated moving average model were highly consistent with observed values (Adjusted R 2 = 83%, root mean squared error = 108). Thus, findings may help add to the knowledge gap of the role of climate factors in HFRS transmission in China and also assist national local health authorities in the development/refinement of a better strategy to prevent HFRS transmission.

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Hantavirus Disease

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Raghuvanshi

2013

Emerging Epidemics

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Landscape Elements and Hantaan Virus–related Hemorrhagic Fever with Renal Syndrome, People’s Republic of China

Cited by 121 publications

References 9 publications

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

Using Geographic Information System-based Ecologic Niche Models to Forecast the Risk of Hantavirus Infection in Shandong Province, China

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

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