Data and methods
Data source
All echinococcosis infection rates collected through six databases including CNKI, WANFANG DATA, VIP Chinese periodical database, Baidu Library, PubMed and ScienceDirect. The electronic maps were obtained from the National Platform for Common Geospatial Information Services.
Quality Control
To ensure the comprehensiveness of the selected literature and the accuracy of the data, we independently consult the literature, extract, and record the selected research literature data. Discuss the differences and different opinions, determine the use of data, and try to avoid the problem of low data quality caused by imprecise literature selection and non-standard data entry. Finally, 67 articles and 103 sets of valid data were selected(Table 1). At the same time, to ensure the accuracy of data processing, we strictly select articles with clear yak words, sampling time, sampling place and sample number in the process of data processing at the county and district level. the data of hydatid disease of about 130000 yaks in 64 counties in the past 4 decades were obtained. Combined with the county geographic information data of China, the effective data of infection rate of nearly 120000 yaks in 58 counties were selected. Finally, we use WPS Excel and ArcGIS10.8 software for professional data collation and mapping, download the national, provincial and county-level administrative regions and other electronic maps in the national geographic information resources directory service system as basic maps to ensure the accuracy of the basic maps.
Table 2
Echinococcosis infection and distribution of Yaks in China
Province
|
Survey Year
|
Positive Quantity
|
Total Sample Size
|
Infection Rate
|
Contributing Author
|
Gansu
|
1983
|
224
|
535
|
41.87%
|
[7]
|
Gansu
|
1989
|
37
|
49
|
75.51%
|
[8]
|
Gansu
|
1996
|
21
|
125
|
16.80%
|
[9]
|
Gansu
|
1996
|
2
|
125
|
1.60%
|
[9]
|
Gansu
|
2005
|
176
|
1835
|
9.59%
|
[10]
|
Gansu
|
2006
|
95
|
1176
|
8.08%
|
[10]
|
Gansu
|
2006
|
126
|
634
|
19.90%
|
[11]
|
Gansu
|
2006
|
2
|
634
|
0.30%
|
[11]
|
Gansu
|
2007
|
63
|
634
|
9.94%
|
[10]
|
Gansu
|
2013
|
598
|
4213
|
14.19%
|
[12]
|
Gansu
|
2014
|
401
|
2834
|
14.15%
|
[12]
|
Gansu
|
2015
|
326
|
3000
|
10.87%
|
[12]
|
Gansu
|
2016
|
29
|
762
|
3.81%
|
[13]
|
Gansu
|
2016
|
221
|
2230
|
9.91%
|
[12]
|
Gansu
|
2017
|
38
|
874
|
4.35%
|
[13]
|
Gansu
|
2018
|
41
|
868
|
4.72%
|
[13]
|
Qinghai
|
1982
|
2759
|
5689
|
48.49%
|
[14]
|
Qinghai
|
1983
|
11
|
23
|
47.80%
|
[15]
|
Qinghai
|
1983
|
1937
|
1957
|
99.00%
|
[16]
|
Qinghai
|
1984
|
16
|
20
|
80.00%
|
[17]
|
Qinghai
|
1987
|
192
|
257
|
74.70%
|
[18]
|
Qinghai
|
1988
|
67
|
210
|
31.90%
|
[19]
|
Qinghai
|
1990
|
821
|
1018
|
80.65%
|
[20]
|
Qinghai
|
1991
|
378
|
1000
|
37.80%
|
[21]
|
Qinghai
|
1991
|
671
|
1041
|
64.46%
|
[22]
|
Qinghai
|
1991
|
752
|
1072
|
70.15%
|
[23]
|
Qinghai
|
1991
|
706
|
1009
|
69.97%
|
[24]
|
Qinghai
|
1991
|
13
|
400
|
3.25%
|
[25]
|
Qinghai
|
1991
|
16
|
50
|
32.00%
|
[26]
|
Qinghai
|
1992
|
544
|
1033
|
52.67%
|
[27]
|
Qinghai
|
1993
|
3
|
8
|
37.50%
|
[28]
|
Qinghai
|
1993
|
148
|
540
|
27.40%
|
[29]
|
Qinghai
|
1997
|
875
|
1030
|
84.95%
|
[30]
|
Qinghai
|
1997
|
2019
|
2356
|
85.70%
|
[16]
|
Qinghai
|
1998
|
277
|
501
|
55.29%
|
[31]
|
Qinghai
|
2001
|
173
|
219
|
79.00%
|
[32]
|
Qinghai
|
2002
|
72
|
128
|
56.25%
|
[33]
|
Qinghai
|
2003
|
20
|
276
|
7.25%
|
[34]
|
Qinghai
|
2004
|
58
|
100
|
58.00%
|
[35]
|
Qinghai
|
2005
|
803
|
1254
|
64.00%
|
[16]
|
Qinghai
|
2006
|
105
|
576
|
18.20%
|
[36]
|
Qinghai
|
2006
|
52
|
280
|
18.57%
|
[37]
|
Qinghai
|
2007
|
14
|
53
|
26.40%
|
[38]
|
Qinghai
|
2007
|
165
|
515
|
32.04%
|
[39]
|
Qinghai
|
2008
|
306
|
629
|
48.65%
|
[40]
|
Qinghai
|
2008
|
263
|
540
|
48.70%
|
[41]
|
Qinghai
|
2009
|
71
|
327
|
21.71%
|
[42]
|
Qinghai
|
2010
|
97
|
474
|
15.61%
|
[43]
|
Qinghai
|
2011
|
16
|
62
|
25.81%
|
[44]
|
Qinghai
|
2011
|
80
|
300
|
26.67%
|
[45]
|
Qinghai
|
2012
|
22
|
305
|
7.21%
|
[46]
|
Qinghai
|
2012
|
57
|
301
|
18.94%
|
[45]
|
Qinghai
|
2013
|
72
|
203
|
35.47%
|
[47]
|
Qinghai
|
2013
|
99
|
262
|
37.79%
|
[47]
|
Qinghai
|
2013
|
18
|
160
|
11.25%
|
[45]
|
Qinghai
|
2014
|
22
|
100
|
22.00%
|
[45]
|
Qinghai
|
2015
|
19
|
360
|
5.28%
|
[48]
|
Qinghai
|
2015
|
17
|
272
|
6.25%
|
[49]
|
Qinghai
|
2015
|
11
|
60
|
18.30%
|
[50]
|
Qinghai
|
2015
|
150
|
300
|
50.00%
|
[51]
|
Qinghai
|
2016
|
175
|
340
|
51.47%
|
[51]
|
Qinghai
|
2017
|
46
|
566
|
11.31%
|
[52]
|
Qinghai
|
2017
|
88
|
200
|
44.00%
|
[51]
|
Qinghai
|
2018
|
30
|
130
|
23.10%
|
[53]
|
Qinghai
|
2018
|
33
|
633
|
5.21%
|
[52]
|
Qinghai
|
2018
|
37
|
160
|
23.13%
|
[51]
|
Qinghai
|
2018
|
33
|
129
|
25.60%
|
[54]
|
Qinghai
|
2019
|
9
|
85
|
10.59%
|
[55]
|
Qinghai
|
2019
|
24
|
723
|
3.32%
|
[52]
|
Qinghai
|
1952–1998
|
32144
|
54091
|
59.43%
|
[56]
|
Qinghai
|
1981–1982
|
139
|
188
|
73.94%
|
[57]
|
Qinghai
|
1989 ~ 1990
|
306
|
384
|
79.90%
|
[58]
|
Qinghai
|
1989–1990
|
18
|
384
|
4.70%
|
[58]
|
Qinghai
|
1990–1991
|
357
|
394
|
90.61%
|
[59]
|
Qinghai
|
1990–1991
|
220
|
276
|
79.71%
|
[59]
|
Qinghai
|
1992–1999
|
296
|
370
|
80.00%
|
[60]
|
Qinghai
|
1992–1999
|
163
|
207
|
78.74%
|
[60]
|
Qinghai
|
1992–1999
|
541
|
989
|
54.70%
|
[60]
|
Qinghai
|
1992–1999
|
34
|
60
|
56.67%
|
[60]
|
Qinghai
|
1992–1999
|
110
|
230
|
47.87%
|
[60]
|
Qinghai
|
1992–1999
|
112
|
280
|
40.00%
|
[60]
|
Qinghai
|
1992–1999
|
84
|
104
|
80.77%
|
[60]
|
Qinghai
|
1992–1999
|
48
|
94
|
51.06%
|
[60]
|
Qinghai
|
1992–1999
|
285
|
504
|
56.50%
|
[60]
|
Qinghai
|
1992–1999
|
24
|
75
|
32.00%
|
[60]
|
Qinghai
|
1992–1999
|
235
|
494
|
47.57%
|
[60]
|
Qinghai
|
1992–1999
|
417
|
664
|
62.80%
|
[60]
|
Qinghai
|
1992–1999
|
276
|
569
|
48.51%
|
[60]
|
Qinghai
|
1992–1999
|
90
|
104
|
86.54%
|
[60]
|
Qinghai
|
2003–2008
|
72
|
343
|
20.99%
|
[61]
|
Sichuan
|
1982
|
422
|
475
|
88.84%
|
[62]
|
Sichuan
|
1986
|
1820
|
3645
|
49.90%
|
[63]
|
Sichuan
|
1987
|
6
|
97
|
6.70%
|
[64]
|
Sichuan
|
1991
|
66
|
766
|
8.50%
|
[65]
|
Sichuan
|
2017
|
363
|
2047
|
17.73%
|
[66]
|
Sichuan
|
1981–1984
|
805
|
2821
|
28.54%
|
[67]
|
Sichuan
|
1997–1998
|
222
|
429
|
49.70%
|
[68]
|
Tibet
|
1991
|
2
|
36
|
5.56%
|
[65]
|
Tibet
|
1994
|
30
|
36
|
83.30%
|
[69]
|
Tibet
|
2014
|
18
|
114
|
15.79%
|
[70]
|
Xinjiang
|
1990
|
17
|
41
|
41.44%
|
[71]
|
Xinjiang
|
2012
|
3
|
156
|
1.92%
|
[72]
|
Xinjiang
|
2012 ~ 2013
|
0
|
36
|
0.00%
|
[73]
|
Statistical Analysis Method
The database of yak hydatid disease infection rate was established by WPS Excel (version 11.1.0.11045-release). The literature name, the first author, the total number of yak samples and positive samples, the types of hydatid disease, the year of publication, sampling time and place were counted.
Taking the electronic map of provincial administrative region (1∶4000000) obtained in the the National Platform for Common Geospatial Information Services as the basic map, the provincial boundaries are vectorized by ArcGIS10.8 software, and the vector maps of national and provincial boundaries and centers are obtained[74]. Taking the total number of yaks in different regions of China and the infection rate of hydatid disease of yaks in recent 40 years as attribute data, the data were matched with the basic map, and the spatial distribution map was drawn.
Spatial autocorrelation analysis method
In this study, global Moran index (Moran's Index, referred to as Moran's I) statistics is used. The inverse distance method is used to define the spatial weight matrix of yak hydatid disease infection rate in each county, in which the influence of yak hydatid disease infection rate in similar counties on the weight calculation of target counties is greater than that of distant counties. By applying this method, the spatial weight decreases with the increase of distance. Global spatial autocorrelation is used to test whether variables are clustered in space, reflecting the similarity between far-away regional units and adjacent regional units in the whole study area[75]. The value range of Moran's I is (-1,1), if I = 0, there is no spatial correlation; If I > 0, there is a positive spatial correlation; I < 0 has negative spatial correlation; and the larger the absolute value of I is, the greater the correlation of spatial distribution is, that is, there is a phenomenon of aggregated distribution in space. The Moran's I calculation formula is as follows:
Among them, Zi is the deviation between the attribute of element i and its average value (\({\text{X}}_{\text{i}}\)-\(\overline{\text{X}}\)), \({\text{W}}_{\text{i},\text{j}}\) is t, e spatial weight between elements i and j, n is equal to the sum of elements, and \({\text{S}}_{0}\) is the set of all spatial weights[76].
Spatial clustering analysis methods
We use the high and low clustering statistics of ArcGIS to measure the clustering degree of high or low values. The inverse distance method is also used to define the spatial weight matrix of yak hydatid disease infection rate in each county. The calculation formula is as follows:
Where \({\text{X}}_{\text{i}}\),\({\text{X}}_{\text{j}}\), s the attribute value of elements i and j,\({\text{W}}_{\text{i},\text{j}}\) is th, spatial weight between elements i and j,n equals the total number of features, is such that arbitrary i with j cannot occur as the same element[77].
Clustering and outlier analysis methods
Use of ArcGIS10.8 space clustering distribution drawing in statistical tools Anselin Local Moran I function, the yak hydatid disease infection rates in each county district were weighted as cluster and outlier analyses to generate morani values, Z scores, P values, and codes representing cluster types.It identifies hot spots (high high), cold spots (low low), and spatial outliers (high low and low high)[78]. On the basis of repeated simulation tests and empirical revisions, a cluster and outlier distribution plot of yak hydatid disease infection rates at each county district for nearly four decades in China was obtained by analysis. It was calculated as follows:
Among them,\({\text{X}}_{\text{i}}\),\({\text{X}}_{\text{j}}\), s the attribute value of elements i and j, \({\text{W}}_{\text{i},\text{j}}\)is the, spatial weight between elements i and j, n is equal to the total number of elements, W means that arbitrary i and j cannot appear as the same elements[79].