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

Abstract

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...

Effects of Humidity Variation on the Hantavirus Infection and Hemorrhagic Fever with Renal Syndrome Occurrence in Subtropical China

“…According to the model results, predicted risks were divided into three levels: low (< 0.15), moderate (0.15-0.30), and high (> 0.30), and the mean relative risks of HV infection of rodents in study area. 13 Annual prediction maps showed high-risk areas always focused on the southwest of the study area (Hengyang city), and sporadically scattered in Changsha city, Shaodong County, and Zhuzhou city; however, low-and moderate-risk of infections always occurred in northwest and east region, north and middle of the study area, respectively. In 2010, high infection risk areas decreased and low infection risk areas appeared in southwest (Figure 3).…”

“…15,23,28 The appropriate precipitation leads to increased rodent food resources, affects the transmission of HFRS by influencing the living conditions and food supplies of rodents and the transmission between rodents and from rodents to humans; temperature affects the population of rodents by influencing the pregnancy numbers, litter size, birthrates, and survival rates, and appropriate temperatures were correlated to increased survival and recruitment, which lead to greater rodent population densities; the infectivity of HV is also influenced by temperature and precipitation. 11,13,15,29 But the higher the temperature, the shorter the survival of the HV outside the host. However, our results showed that TVDI and elevation were the main risk factors, and therefore temperature, precipitation, and NDVI were not included in the final logistic regression model.…”

“…The probability of infected rodents varies with NDVI and elevation, and infection rates for HV in rodents were also significantly associated with rice agriculture. 12,13 In addition, as the incidence of HFRS is affected by environmental conditions, HFRS cases in China mostly occurred in districts with low elevation, high humidity, and cultivated land.…”

Does Land Cover Influence the Spatial Distribution of Reservoir Rodent Necromys Lasiurus?

“…Large variations in altitude and rainfall result in a mosaic of vegetation types, including hills covered by extensive grasslands, narrow gallery forests, and rocky areas dominated by cactus [23]. Given this combination of floral characteristics and edaphic and pedological features, this unique ecosystem is very similar to the savanna of the Cerrado biome.…”

Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China

“…Glass et al [15][16][17] and Boone et al [18,19] used remote sensing images to identify environments associated with a risk of hantaviral disease in humans caused by Sin Nombre virus. Wei et al [20] combined ecologic niche modeling with remote sensing techniques to identify the risk factors for hantavirus infections in rodent hosts. Zhang et al [10] and Goodin et al [21] examined the relationship between natural infection rates for hantavirus in rodents and land cover classes.…”