Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China
Abstract
:1. Introduction
- (1)
- The topographic factors include elevation, slope, and aspect. These factors play important roles in the distribution of ticks and their hosts by affecting the reallocation of the hydrothermal combination. Merler used classification tree method to analyze the distribution of Ixodes ricinus in Trentino, Italian Alps, and concluded that the most important factors determining the distribution of the ticks are the altitude and geological environment [13]. Toomer used the digital elevation model (DEM) and other factors to simulate the general distribution of Pan-African ticks [14]. Randolph et al. used the DEM and Land Surface Temperature (LST) as predictive variables and reported that the distribution of five tick aggregation places in central Europe and around the Baltic Ocean is closely related to the DEM [15]. Materna et al. mentioned in their study that the density of ticks in small-scale research is highly impacted by the aspect [16].
- (2)
- Climate factors, such as temperature, light duration, and rainfall, determine the living range of hosts and vectors to a certain extent, which affects the distribution of natural foci. Lindgren suggested that more ticks might survive in a mild winter in host and reservoir animals [17]. Due to an early arrival of the spring and/or late arrival of the next winter, ticks will be active for an extended period. Eisen reported that the tick density is closely related to the daily maximum temperatures [18]. Süss et al. proposed that an increase in the temperature up to a certain level causes the acceleration and extension of the developmental cycle of the ticks, increase in the egg production and population density, and shift of the risk areas [19]. Kahl et al. concluded that the relative humidity (RH) affects the life circle of ticks due to the transformation and absorption of water vapor in half-saturated air [20].
- (3)
- The vegetation can provide a suitable living environment for ticks and their vectors. The density of ticks correlates to the type and structure of the forest; it is the highest in mixed and deciduous forests [15]. Jackson reported that the Lyme disease incidences in 12 Maryland counties were the highest when the edge-contrast index of the forest–herbaceous edge reached 53% [21].
2. Material and Methods
2.1. Study Area
2.2. Data and Preprocessing
2.2.1. Disease Data
2.2.2. Geographic and Environmental Data
2.3. Methods
2.3.1. Spatial Autocorrelation
2.3.2. Spatial Regression
3. Results and Analysis
3.1. Descriptive Analysis
3.2. Spatial Autocorrelation
3.3. Global Regression Analysis
3.4. Local Regression Analysis
- (1)
- Relative humidity: The RH in the three northern counties and several southern counties and the TBE risk are positively correlated (Figure 6a). The TBE risk in these areas increases with the RH. The TBE risk in the New Barag Right Banner and the RH are negatively related. Regions with relatively high RH values always have a lower risk.
- (2)
- Vegetation index: Based on the spatial distribution of the NDVI coefficient in Figure 6b, the TBE risk is positively correlated with the NDVI in the Old Barag Banner, Oroqen Banner, and several southernmost towns. The TBE risk in these areas increases with increasing vegetation cover. In the middle Yakeshi County, especially in Wunu’er and Miandu, the TBE risk reaches the highest value while it is negatively correlated with the NDVI. No direct relationship between the increasing vegetation coverage and TBE risk was observed in these areas.
- (3)
- Precipitation: The TBE risks in southwestern Hulunbuir are negatively correlated with the precipitation (Figure 6c). The TBE risks in this area decrease with increasing precipitation. The TBE risks and precipitation in the center of the Yakeshi County are positively correlated. The risks increase with increasing precipitation.
- (4)
- DEM: The correlation between the TBE risks and DEM changes from negative to positive from west to east (Figure 6d). Areas with high elevation in western Hulunbuir exhibit less risk than the low regions. The effect of the DEM is the opposite in eastern Hulunbuir.
- (5)
- Slope: The correlation between the slope and TBE risk is negative in the east, while it is highly positive in the west. In western Hulunbuir, where most of the land cover is grassland, the TBE risk is a bit higher at a steep slope than in gentle areas. In contrast, the TBE risk in the broad farmland of eastern Hulunbuir is lower at a steep slope than in the gentle areas (Figure 6e).
- (6)
- Aspect: The TBE risk in northern Hulunbuir is slightly impacted by the change of aspect. In contrast, there are two different situations in the southern area. The TBE risk is negatively correlated with the aspect in the Evenk and New Barag Right banners, while they are positively correlated in the southeastern Arun Banner and Zhalantun County (Figure 6f).
4. Discussion
4.1. Endemic Seasonal Features of TBE
4.2. Predicted Risk Distribution
4.2.1. Central High-Risk Triangle Zone
4.2.2. Western Low-Risk Belt
4.2.3. Eastern Low-Risk Belt
4.3. Regression Models
5. Conclusions
- (1)
- The spatial autocorrelation results show that the distribution of the TBE risk in Hulunbuir was significantly autocorrelated from 2006 to 2013. The high-risk aggregation area gradually changes during the study period. The high-risk TBE aggregation area first extends from the northern part of the Great Khingan Range southward. The aggregation foci return back to the origin in 2011, and the high-risk aggregations continue to expand northward up to Moerdaoga, Ergun County. The statistical data show that the people in Hulunbuir more easily get infected with TBE in spring and summer. The prevalence of the patients has notable occupational and gender characteristics. Male workers inhabiting or working in forests more easily get infected.
- (2)
- The impact degree of the geographic and environmental factors on the TBE risk has the following descending order: temperature, RH, vegetation coverage, precipitation, and topographic information. The temperature and RH in Hulunbuir are strongly negatively correlated. In addition, the spatial distribution of the different coefficients of the variables in the local regression model show that the correlations between the TBE risk and geographic and environmental factors change depending on the spatial location.
- (3)
- The distribution of TBE risk in Hulunbuir was quite particular. Central high-risk region seemed to be a triangle area. The eastern and western belts are at low TBE risk. The high-risk triangle includes Ergun, Genhe, Oroqen Banner, and Yakeshi County. The TBE risk inside the triangle region increases from south to north. The most relevant factor in this triangle is the RH. The TBE risk in most parts of this triangle is positively correlated with NDVI, precipitation, and aspect and negatively correlated with slope; they are negatively correlated in the northern triangle. The local regression results provide a risk evaluation model and data support for the TBE prediction and control.
Acknowledgments
Authors Contribution
Conflicts of Interest
References
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Indices | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2006–2013 Average |
---|---|---|---|---|---|---|---|---|---|
Moran’s I | 0.156 | 0.113 | 0.092 | 0.089 | 0.110 | 0.106 | 0.079 | 0.115 | 0.144 |
E(I) | −0.008 | −0.008 | −0.008 | −0.008 | −0.008 | −0.008 | −0.008 | −0.008 | −0.008 |
Z-score | 3.205 | 2.085 | 1.791 | 1.707 | 2.169 | 2.003 | 1.485 | 2.081 | 2.655 |
p-value | 0.001 | 0.037 | 0.073 | 0.088 | 0.030 | 0.045 | 0.137 | 0.037 | 0.008 |
Elements | Aspect | Slope | DEM | EVI | NDVI | Prep | PF | SH | RH | Temp |
---|---|---|---|---|---|---|---|---|---|---|
Coefficients | 0.15 | 0.56 | 0.46 | 0.46 | 0.63 | 0.28 | 0.68 | −0.40 | 0.60 | −0.60 |
p-value | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 |
Model Code | Factors | F-Value | p-Value | R² | Rc² |
---|---|---|---|---|---|
1 | PF | 1.457E3 | 0.000 | 0.384 | 0.384 |
2 | PF, Slope | 916.339 | 0.000 | 0.440 | 0.439 |
3 | PF, Slope, Temp | 831.242 | 0.000 | 0.516 | 0.516 |
4 | PF, Slope, Temp, NDVI | 670.466 | 0.000 | 0.535 | 0.534 |
5 | PF, Slope, Temp, NDVI, RH | 609.865 | 0.000 | 0.566 | 0.566 |
6 | PF, Slope, Temp, NDVI, RH, EVI | 527.187 | 0.000 | 0.576 | 0.574 |
7 | Slope, Temp, NDVI, RH, EVI | 632.140 | 0.000 | 0.575 | 0.574 |
8 | Slope, Temp, NDVI, RH, EVI, Aspect | 529.974 | 0.000 | 0.577 | 0.576 |
9 | Slope, Temp, NDVI, RH, EVI, Aspect, DEM | 455.911 | 0.000 | 0.578 | 0.577 |
10 | Slope, Temp, NDVI, RH, EVI, Aspect, DEM, Prep | 401.706 | 0.000 | 0.580 | 0.578 |
11 | Slope, Temp, NDVI, RH, EVI, Aspect, DEM, Prep, PF | 358.195 | 0.000 | 0.580 | 0.579 |
12 | Slope, Temp, NDVI, RH, EVI, DEM, Prep, PF | 402.405 | 0.000 | 0.580 | 0.579 |
Independent Variables | Coefficient | Standard Coefficient | t-Value | p-Value |
---|---|---|---|---|
Constant | 15.336 | 11.040 | 0.000 | |
Slope (°) | 0.026 | 0.133 | 4.548 | 0.000 |
DEM (km) | −0.207 | −0.064 | −2.337 | 0.020 |
EVI | −3.978 | −0.379 | −7.723 | 0.000 |
NDVI | 5.148 | 0.897 | 14.969 | 0.000 |
PF (days) | −0.006 | −0.145 | −2.546 | 0.011 |
Prep (mm) | 0.022 | 0.236 | 4.475 | 0.000 |
Temp (°C) | −0.449 | −1.376 | −14.604 | 0.000 |
RH (%) | −0.263 | −1.041 | −11.111 | 0.000 |
Model ID | Involved Factors | R2 | Rc2 | AIC |
---|---|---|---|---|
1 | DEM, Slope, Aspect, Prep, NDVI, RH | 0.98 | 0.99 | 7.55 |
2 | DEM, Slope, Aspect, Prep, EVI, RH | - | - | - |
3 | DEM, Slope, Aspect, Prep, NDVI, Temp | 0.87 | 0.88 | 56.85 |
4 | DEM, Slope, Aspect, Prep, EVI, Temp | 0.96 | 0.96 | 24.46 |
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Li, Y.; Wang, J.; Gao, M.; Fang, L.; Liu, C.; Lyu, X.; Bai, Y.; Zhao, Q.; Li, H.; Yu, H.; et al. Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China. Int. J. Environ. Res. Public Health 2017, 14, 569. https://doi.org/10.3390/ijerph14060569
Li Y, Wang J, Gao M, Fang L, Liu C, Lyu X, Bai Y, Zhao Q, Li H, Yu H, et al. Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China. International Journal of Environmental Research and Public Health. 2017; 14(6):569. https://doi.org/10.3390/ijerph14060569
Chicago/Turabian StyleLi, Yifan, Juanle Wang, Mengxu Gao, Liqun Fang, Changhua Liu, Xin Lyu, Yongqing Bai, Qiang Zhao, Hairong Li, Hongjie Yu, and et al. 2017. "Geographical Environment Factors and Risk Assessment of Tick-Borne Encephalitis in Hulunbuir, Northeastern China" International Journal of Environmental Research and Public Health 14, no. 6: 569. https://doi.org/10.3390/ijerph14060569