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. 2022 Aug 25;11(9):970.
doi: 10.3390/pathogens11090970.

Identifying the Determinants of Distribution of Oncomelania hupensis Based on Geographically and Temporally Weighted Regression Model along the Yangtze River in China

Zhe Wang  1 , Lu Liu  2   3 , Liang Shi  2 , Xinyao Wang  2 , Jianfeng Zhang  2 , Wei Li  2 , Kun Yang  1   2   3
Affiliations

Identifying the Determinants of Distribution of Oncomelania hupensis Based on Geographically and Temporally Weighted Regression Model along the Yangtze River in China

Zhe Wang et al. Pathogens. .

Abstract

Background: As the unique intermediate host of Schistosoma japonicum, the geographical distribution of Oncomelania hupensis (O. hupensis) is an important index in the schistosomiasis surveillance system. This study comprehensively analyzed the pattern of snail distribution along the Yangtze River in Jiangsu Province and identified the dynamic determinants of the distribution of O. hupensis.

Methods: Snail data from 2017 to 2021 in three cities (Nanjing, Zhenjiang, and Yangzhou) along the Yangtze River were obtained from the annual cross-sectional survey produced by the Jiangsu Institute of Parasitic Diseases. Spatial autocorrelation and hot-spot analysis were implemented to detect the spatio-temporal dynamics of O. hupensis distribution. Furthermore, 12 factors were used as independent variables to construct an ordinary least squares (OLS) model, a geographically weighted regression (GWR) model, and a geographically and temporally weighted regression (GTWR) model to identify the determinants of the distribution of O. hupensis. The adjusted coefficients of determination (adjusted R2, AICc, RSS) were used to evaluate the performance of the models.

Results: In general, the distribution of O. hupensis had significant spatial aggregation in the past five years, and the density of O. hupensis increased eastwards in the Jiangsu section of the lower reaches of the Yangtze River. Relatively speaking, the distribution of O. hupensis wase spatially clustered from 2017 to 2021, that is, it was found that the border between Yangzhou and Zhenjiang was the high density agglomeration area of O. hupensis snails. According to the GTWR model, the density of O. hupensis was related to the normalized difference vegetation index, wetness, dryness, land surface temperature, elevation, slope, and distance to nearest river, which had a good explanatory power for the snail data in Yangzhou City (adjusted R2 = 0.7039, AICc = 29.10, RSS = 6.81).

Conclusions: The distribution of O. hupensis and the environmental factors in the Jiangsu section of the lower reaches of the Yangtze River had significant spatial aggregation. In different areas, the determinants affecting the distribution of O. hupensis were different, which could provide a scientific basis for precise prevention and control of O. hupensis. A GTWR model was prepared and used to identify the dynamic determinants for the distribution of O. hupensis and contribute to the national programs of control of schistosomiasis and other snail-borne diseases.

Keywords: Oncomelania hupensis; Yangtze River; geographical and temporal weighted regression; heterogeneity; spatial autocorrelation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A map of the study area and the sites of O. hupensis along the Yangtze River in Jiangsu Province.
Figure 2
Figure 2
Snail density along the Yangtze River in Nanjing, Zhenjiang, and Yangzhou, 2017–2021 (a–e).
Figure 3
Figure 3
Hot-spot map for snail density along the Yangtze River in Nanjing, Zhenjiang, and Yangzhou, 2017–2021 (a–e).
Figure 4
Figure 4
Spatial distribution of impact coefficients of seven factors on snail density in Nanjing, Zhenjiang, and Yangzhou, 2017–2021 (a–g).
Figure 5
Figure 5
Local R2 of GTWR model on snail density in Nanjing, Zhenjiang, and Yangzhou, 2017–2021.
Figure 6
Figure 6
Time series of the standard coefficient of explanatory variables in Yangzhou City, 2017–2021.
Figure 7
Figure 7
Spatial distribution for impact coefficient of LST factor on snail density in Yangzhou City, 2017–2021 (a–e).
Figure 8
Figure 8
Spatial distribution for impact coefficient of Dry factor on snail density in Yangzhou City, 2017–2021.
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