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. 2021 Mar 22;15(3):e0009152. doi: 10.1371/journal.pntd.0009152

Spatio-temporal clustering of Mountain-type Zoonotic Visceral Leishmaniasis in China between 2015 and 2019

Yuwan Hao 1, Xiaokang Hu 1, Yanfeng Gong 1, Jingbo Xue 1, Zhengbin Zhou 1, Yuanyuan Li 1, Qiang Wang 1, Yi Zhang 1,2, Shizhu Li 1,2,*
Editor: Johan Van Weyenbergh3
PMCID: PMC8016304  PMID: 33750970

Abstract

With several decades of concerted control efforts, visceral leishmaniasis(VL) eradication had almost been achieved in China. However, VL cases continue to be detected in parts of western China recent years. Using data of reported cases, this study aimed to investigate the epidemiology and spatio⁃temporal distribution, of mountain-type zoonotic visceral leishmaniasis (MT-ZVL) in China between the years 2015 and 2019. Epidemiological data pertaining to patients with visceral leishmaniasis (VL) were collected in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei provinces between the years 2015 and 2019. Joinpoint regression analysis was performed to determine changes in the epidemic trend of MT-ZVL within the time period during which data was collected. Spatial autocorrelation of infection was examined using the Global Moran’s I statistic wand hotspot analysis was carried out using the Getis-Ord Gi* statistic. Spatio-temporal clustering analysis was conducted using the retrospective space-time permutation flexible spatial scanning statistics. A total of 529 cases of MT-ZVL were detected in the six provinces from which data were collected during the study time period, predominantly in Gansu (55.0%), Shanxi (21.7%), Shaanxi (12.5%) and Sichuan (8.9%) provinces. A decline in VL incidence in China was observed during the study period, whereas an increase in MT-ZVL incidence was observed in the six provinces from which data was obtained (t = 4.87, P < 0.05), with highest incidence in Shanxi province (t = 16.91, P < 0.05). Significant differences in the Moran’s I statistic were observed during study time period (P < 0.05), indicating spatial autocorrelation in the spatial distribution of MT-ZVL. Hotspot and spatial autocorrelation analysis revealed clustering of infection cases in the Shaanxi-Shanxi border areas and in east of Shanxi province, where transmission increased rapidly over the study duration, as well as in well know high transmission areas in the south of Gansu province and the north of the Sichuan province. It indicates resurgence of MT-ZVL transmission over the latter three years of the study. Spatial clustering of infection was observed in localized areas, as well as sporadic outbreaks of infection.

Author summary

Leishmania parasite. It was defined as a neglected tropical disease (NTD) by the World Health Organization (WHO) since 2010. The eradication of VL had almost been achieved in the country since 1960s`last century but the parts of western China. Although the numbers of annual reported cases of VL declined, the mountain-type zoonotic visceral leishmaniasis (MT-ZVL) continued to increase in recent years. In this study, the epidemiological characters and spatio⁃temporal distribution of MT-ZVL were investigated in China between the years 2015 and 2019. A total of 529 cases of MT-ZVL were reported in six provinces and predominantly in Gansu (55.0%), Shanxi (21.7%), Shaanxi (12.5%) and Sichuan (8.9%) provinces. Significant differences in the Moran’s I statistic were observed, indicating spatial autocorrelation in the spatial distribution of MT-ZVL. Spatio⁃temporal hotspot analysis revealed clustering of infection cases in the Shaanxi-Shanxi border areas and eastern of Shanxi province, as well as in the south of Gansu province and the north of the Sichuan province. Therefore, the reinforcement of VL control in conventionally high-risk areas, attention to areas where VL re-emergence is likely, timely survey of vectors, assessment of transmission risk, and targeted interventions are strongly recommended to reduce risk of MT-ZVL infection.

Introduction

Visceral leishmaniasis (VL), also known as kala-azar, is a zoonotic infectious disease caused by the protozoan Leishmania parasite and transmitted by the bite of infected sandflies [1]. Currently, this zoonosis is prevalent in 88 countries across East Africa, South Asia, South America and the Mediterranean [2]. Global incidence of infection is estimated at 200 to 400 thousand cases each year, with approximately 60 thousand VL attributed deaths due to failure in timely treatment [3]. Consequently, VL related mortality ranks second only to malaria among all parasitic diseases in terms of the mortality [4]. In 2010, VL was defined as a neglected tropical disease (NTD) by the World Health Organization (WHO). NTDs are a group of parasitic and bacterial diseases intimately linked to poverty and affecting more than one billion people worldwide annually [5–6]. Although VL transmission in China was once prevalent across 16 provinces north of the Yangtze River, eradication had almost been achieved at the beginning of the 1960s following several decades of concerted control efforts [7]. Currently however, VL cases continue to be detected in parts of western China, including Kashgar in Xinjiang, the southern Gansu province and northern Sichuan province, with localised clustering of VL occasionally reported [8].

The three main classifications of VL infection in China are anthroponotic visceral leishmaniasis (AVL), mountain-type zoonotic visceral leishmaniasis (MT-ZVL) and desert-type zoonotic visceral leishmaniasis (DT-ZVL) [9]. Among these, significant variations exist in the in geographical predominance and ecology of transmission, at-risk populations and vector species [10]. AVL and DT-ZVL transmission occurs predominantly in Xinjiang, while MT-ZVL is prevalent across other areas of China, including parts of Gansu, Sichuan, Shaanxi, Shanxi, Henan and Hebei provicce, which locating in the extension region of Loess Plateau. Since 2016, VL has been included in the National Control Program for Major Parasitic Diseases in China (2016–2020) and given a high priority of management. As a result, transmission control of VL has been achieved in the country, with the number of cases declining over the same time period [11]. Despite this, cases of MT-ZVL have increased each year where MT-ZVL is endemic, and resurgence and clustering of MT-ZVL has been reported in multiple endemic foci of China. In 2019, with a total of 52 MT-ZVL cases detected in Shanxi province, an incidence much higher than the mean provincial prevalence reported between 2014 through 2018, and a 13-fold increase compared with cases detected in 2014 (4 cases). Also of importance was the observed transmission of MT-ZVL to neighboring regions [11–12].

Experiences and lessons learned from the Chinese VL control program have demonstrated that the VL transmission is likely to rebound once the control efforts are weakened [13]. As transmission of VL and the distribution of sandfly populations are greatly affected by natural, biological and social factors [14–15], timely identification of VL cases, and consolidated development and optimization of control strategies, are needed for the interruption and elimination of VL transmission. [16–17]. This study aimed to investigate the epidemiology, and assess the temporal and spatial distribution pattern, of MT-ZVL in China between 2015 to 2019, in order to provide insights into the development of targeted interventions for MT-ZVL.

Materials and methods

Data acquisition

Visceral leishmaniasis case data, reported in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei provinces between 2015 and 2019 were obtained from the National Notifiable Communicable Disease Reporting System [18]. Counties from each of the six provinces where MT-ZVL cases were detected were selected as sampling sites, and longitude and latitude coordinates determined for each site.

Analysis of changes in epidemic trend of MT-ZVL

All epidemic data pertaining to MT-ZVL were loaded into Microsoft Excel 2013. The epidemic trend of MT-ZVL were analyzed using descriptive epidemiology approach and the long-term changing of MT-ZVL incidence were tested using Joinpoint Model of the Joinpoint Regression Program (Version4.3.1). The T-tests were used to determine whether there is significant difference in the long-term changing of the incidence within a certain period of time [19], the long-term trend in linear segments were described according to the best fitting results, and Annual Percentage Change (APC) values were calculated [20].

Spatial autocorrelation analysis

Spatial autocorrelation is defined as the correlation of values of a single variable at different geographical locations using a measurement of spatial clustering based on feature locations and attribute values [21]. Spatial autocorrelation and hotspot analysis were performed using the global Moran’s I and Getis-Ord Gi* statistics, respectively, in ArcGIS software, version 10.3[22]. The Global Moran’s I statistic estimates the overall degree of spatial correlation for a dataset [6–7], and is calculated using the following formula:

I=n∑i=1n∑j=1nwij(xi−x¯)(xj−x¯)∑i=1n∑j=1nwij∑j=1n(xi−x¯)2

where I is indicative of the Moran’s I statistic, with values ranging from -1 (perfect dispersion) to 1 (perfect correlation). Negative values indicate negative spatial autocorrelation, positive values indicate positive spatial autocorrelation, and a value of zero value indicates a random spatial pattern (no spatial correlation).

The Getis-Ord Gi* statistic, a spatial autocorrelation index based on a weighted distance matrix, ascertains spatial clustering of locations using high (hot spot) or low values (cold spot) with statistically significance ascertained by use of Z scores and P values [4–5,16]. It is calculated using the following formula:

Gi*=∑j=1nwijxj−x¯∑j=1nwij[n∑j=1nwij2−(∑j=1nwij)2]n−1s

If the value Gi* is greater than 0, it indicates that the neighbor attribute value of its spatial unit i is high; otherwise, the neighbor attribute value is low.

Spatio-temporal clustering analysis

Spatio-temporal statistics were employed to describe the temporal and spatial distribution of MT-ZVL, and to identify geographic and temporal clusters, of disease within the 2015 to 2019 time period. A Poisson model using a retrospective space-time permutation scan statistic was used to identify spatio-temporal clusters of MT-ZVL using SatScan software version 9.4.2[23]. Space time scanning defined as a dynamic scan using a cylindrical window in dimensions of time scales and geographical locations, was also used in the identification of spatial and temporal disease clusters. The log likelihood ratio (LLR), a statistic which tests the difference between observed and expected numbers, in and outside the window, was employed as a measure of change in the time and space in window [24]. A Monte Carlo simulation was used for permutation testing. Statistical significance was determined by a p-value of < 0.05. Spatial clustering of MT-ZVL incidence was detected based a Poisson model using a flexible spatial scan statistic in FlexScan software, version 3.1.2 [24]. All incidence data were processed separately for each year for which data were available and potential spatial clusters detected using restricted log likelihood ratio (RLLR). P-values for RLLR were calculated, and the most likely cluster (MLC) estimated. A p-value of < 0.05 was indicative of a statistically significant cluster.

Results

Epidemic trend of MT-ZVL from 2015 to 2019

A total of 529 MT-ZVL cases were detected in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei provinces between 2015 and 2019, with annual incidence of 82, 95, 113, 117 and 112 cases each year, respectively (Fig 1). Among all MT-ZVL cases reported in the six provinces during the 5-year period, the highest number of cases was reported in Gansu province (55.0%), followed by Shanxi (21.7%), Shaanxi (12.5%), Sichuan (8.9%) and Henan (1.5%), with the lowest number reported in Hebei province (0.4%).

Fig 1. The number of local infected patient of MT-ZVL in 6 provinces of Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei, China from 2015 to 2019.

Fig 1

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The counties (districts) with the greatest cumulative incidence of MT-ZVL were located predominantly in the Gansu-Sichuan, Shaanxi-Shanxi and Shanxi-Hebei-Henan border areas. In addition to this, 31 MT-ZVL cases were detected in 13 re-emergent counties (districts) of Gansu, Shaanxi, Shanxi, Henan and Hebei provinces, with the majority of cases in Shaanxi (9 cases) and Shanxi provinces (11 cases; Fig 2).

Fig 2. Cumulative incidence and reemergence of mountain-type zoonotic visceral leishmaniasis in China from 2015 to 2019.

Fig 2

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Joinpoint regression analysis revealed a decline in total VL incidence (t = –5.66, P < 0.05), and an increase in MT-ZVL incidence in China during the 2015 to 2019 time period (t = 4.87, P < 0.05). A significant change in MT-ZVL incidence was observed in Shanxi province during the 5-year period (t = 16.91, P < 0.05), however, no significant changes were detected in MT-ZVL incidence in other five provinces (Table 1, Figs 3–5).

Table 1. Joinpoint regression analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provences, China from 2015 to 2019.

T value P value APC Lower 95% CI Upper 95% CI
Ganshu -0.71 0.53 -5.70 -27.30 22.30
Shaanxi 1.60 0.21 23.70 -18.90 88.60
Sichuan -0.84 0.46 -10.10 -39.90 34.40
Shanxi 16.91 0.00 65.61 50.60 82.10
Henan 0.91 0.43 13.00 -26.30 73.10
Hebei 2.70 0.07 47.80 -6.80 134.30
six provences 4.87 0.02 9.62 3.20 16.40
China -5.66 0.01 -26.24 -37.80 -12.50
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Fig 3. The joinpoint regression analysis for determining changes in the trend of visceral leishmaniasis incidence in China from 2015 to 2019.

Fig 3

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Fig 5. The joinpoint regression analysis for determining changes in the trend of mountain-type zoonotic visceral leishmaniasis incidence in Shanxi province from 2015 to 2019.

Fig 5

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Fig 4. The joinpoint regression analysis for determining changes in the trend of mountain-type zoonotic visceral leishmaniasis incidence in main endemic areas (6 provinces of Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei) from 2015 to 2019.

Fig 4

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Global spatial autocorrelation and hotspots of MT-ZVL incidence

Positive spatial autocorrelation was observed using the Global Moran’s I statistic among different counties (districts) in each province (P < 0.05; Table 2).

Table 2. Global autocorrelation analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provences, China from 2015 to 2019.

Year Moran’s I Variance Expected value Z value P value
2015 0.056441 0.000089 -0.001196 6.098942 <0.05
2016 0.050108 0.000072 -0.001196 6.030147 <0.05
2017 0.055217 0.000071 -0.001196 6.675749 <0.05
2018 0.048587 0.000054 -0.001196 6.756551 <0.05
2019 0.087472 0.000102 -0.001196 8.786115 <0.05
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Hotspots of MT-ZVL infection were detected in 16, 10, 12, 11 and 29 counties (districts) in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei, respectively, with highest-incidence cluster defined as hotspots detected with a 99% confidence interval. MT-ZVL hotspots were identified predominantly in the southern Gansu province, northern Sichuan province and central Shaanxi province in 2015, the southern Gansu-Sichuan border areas in 2016, and in the southeastern Shaanxi province in 2017. Infection hotspots were also detected in the southern Gansu-Sichuan border areas, the eastern Shanxi province in 2018, and were widely identified in Gansu-Sichuan-Shaanxi border areas, eastern Shaanxi-Shanxi border areas and local areas of the eastern Shanxi province in 2019 (Figs 6–10).

Fig 6. Hotspot analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2015.

Fig 6

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Fig 10. Hotspot analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2019.

Fig 10

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Fig 7. Hotspot analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2016.

Fig 7

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Fig 8. Hotspot analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2017.

Fig 8

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Fig 9. Hotspot analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2018.

Fig 9

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Spatio-temporal clusters of MT-ZVL incidence

Based on the Satscan soft analysis, retrospective space-time permutation scan statistics also detected statistically significant clusters of infection at county level in each of the six study provinces, with 3, 2, 3, 3 and 6 clusters detected for each year within the 2015 to 2019 time period, respectively, (Table 3). The degree of infection clustering was also observed to be reduced during successive years of the study period using LLR estimates. During the 2015 to 2018 study period, grade I clusters of MT-ZVL incidence were identified in the southern Gansu province, while in 2019, grade I clusters were detected only in the eastern Shanxi province. For the other clusters, they were detected in the central Sichuan province and Shaanxi-Shanxi border areas in 2015, in the central Sichuan province in 2016 and 2017. in the eastern parts of Shaanxi province and eastern parts of Shanxi province in 2018, and in southern Gansu province, southeastern Shaanxi province and Shaanxi-Shanxi border areas in 2019, respectively. The clustering regions and their grade were presented with different circles and colors in the Figs 11–15.

Table 3. Spatiotemporal clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provences, China from 2015 to 2019.

Year Cluster center(°) Radius(km) No. of clustered counties No. of observed No. of expected Relative risk LLR P value
Latitude Longitude
2015 33.6298 104.3176 83.90 5 50 0.56 228.65 195.01 <0.05
36.0647 110.1792 74.14 10 11 0.87 14.42 18.42 <0.05
31.5754 103.0129 65.63 4 8 0.39 22.08 16.98 <0.05
2016 33.6298 104.3176 83.90 5 64 0.64 302.11 259.75 <0.05
32.1609 103.0473 65.15 3 7 0.33 22.56 14.88 <0.05
2017 33.6298 104.3176 83.90 5 69 0.77 229.48 269.26 <0.05
37.8777 113.5332 6.06 3 10 0.39 27.79 23.17 <0.05
31.5754 103.0129 65.63 4 6 0.53 11.82 9.19 <0.05
2018 33.6298 104.3176 83.90 5 59 0.79 148.86 213.87 <0.05
37.8777 113.5332 19.99 4 23 0.66 43.38 61.76 <0.05
35.5764 110.3812 0.00 1 8 0.14 61.54 24.81 <0.05
2019 37.8777 113.5332 19.99 4 33 0.68 65.74 100.33 <0.05
33.6298 104.3176 83.90 5 34 0.83 56.54 98.16 <0.05
35.5764 110.3812 0.00 1 16 0.15 126.56 60.47 <0.05
34.4076 109.8007 27.55 3 9 0.21 45.38 25.20 <0.05
34.5278 110.0465 45.24 5 9 0.36 26.98 20.67 <0.05
35.9191 110.9317 40.15 6 9 1.00 9.66 12.07 <0.05
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Fig 11. Spatio-temporal clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2015.

Fig 11

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Fig 15. Spatio-temporal clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2019.

Fig 15

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Fig 12. Spatio-temporal clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2016.

Fig 12

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Fig 13. Spatio-temporal clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2017.

Fig 13

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Fig 14. Spatio-temporal clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2018.

Fig 14

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For each year during the study time period, 3, 2, 4, 4 and 4 statistically significant clusters of MT-ZVL were identified in each of the six study provinces, respectively, based on flexible spatial scan statistics (Table 4). Most likely clusters were identified predominantly in the southern Gansu province during the five year study period. Secondary clusters of infection were detected in the central Sichuan province and eastern Shaanxi province in 2015, Gansu-Sichuan border areas and central Sichuan province in 2016, Gansu-Sichuan border areas, Shaanxi—Shanxi border areas, eastern parts of Shanxi province in 2017 and 2018, and in the Shaanxi-Shanxi border areas and eastern Shanxi province in 2019 (Figs 16–20).

Table 4. Spatial clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provences, China from 2015 to 2019.

Year No. of clusters Most likely clusrer
No. of counties Max distance(km) No. of infected cases No. of expected cases Overall relative risk P value
2015 3 4 164.95 48 0.21 226.07 <0.05
2016 2 3 101.88 59 0.19 316.23 <0.05
2017 4 4 164.95 63 0.29 297.80 <0.05
2018 4 3 101.88 50 0.23 217.60 <0.05
2019 4 4 164.95 33 0.32 104.46 <0.05
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Fig 16. Spatial clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2015.

Fig 16

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Fig 20. Spatial clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2019.

Fig 20

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Fig 17. Spatial clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2016.

Fig 17

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Fig 18. Spatial clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2017.

Fig 18

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Fig 19. Spatial clustering analysis of mountain-type zoonotic visceral leishmaniasis incidence in 6 provinces, China in 2018.

Fig 19

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Discussion

As major parasitic diseases control has been reinforced by the central government of China and special funds have been given to the VL control program [25], VL transmission has been under effective control in China. Recently, however, cases of MT-ZVL have re-emerged in multiple endemic areas of China, with a gradual increase in cases each year [26]. The present study, therefore, aimed to retrospectively analyze the epidemiology, and identify the spatio-temporal distribution, of MT-ZVL in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei provinces between 2015 and 2019, in order to explore the spatio-temporal within the specified time period.

Between 2015 and 2019, a total of 529 MT-ZVL cases were reported in endemic areas of China, with the majority of cases identified in Gansu (55.0%), Shanxi (21.7%), Shaanxi (12.5%) and Sichuan (8.9%), indicating rebounded resurgence of MT ZVL in the Shanxi and Shaanxi provinces. Recently, MT-ZVL have been detected in the historically endemic and non-endemic areas of Henan and Hebei provinces, indicating re-emergence, and emergence of MT-ZVL in these two provinces. Joinpoint regression analysis determined a decline in VL incidence in China during the 2015 to 2019 study period, but an increase in MT-ZVL incidence (t = 4.87, P < 0.05), most notably significant in Shanxi province (t = 16.91, P < 0.05). These findings demonstrate that transmission of MT-ZVL is increasing, rather than declining, in some part of China, with an observable increase in MT-ZVL transmission during the past three years, and re-emergence of MT-ZVL in multiple transmission-controlled areas [26].

Spatio-temporal analysis show higher consistent hotspots and clustering regions by different methods, especially for the high risk region(grade I), using Global Moran’s I statistic, revealed spatial clustering of MT-ZVL in each of the six study provinces for each year of the study period (P < 0.05). Spatial hotspot analysis revealed infection clustering in 16, 10, 12, 11 and 29 counties (districts) in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei provinces respectively, with increased incidence during the past three years. Among those hotspots, they have always been found in the southern Gansu province and northern Sichuan province, and multiple hotspots were detected in southern and southwestern parts of Shaanxi province, and in local regions of southwestern and eastern Shanxi province during later three years. Retrospective space-time scanning analysis identified 3, 2, 3, 3 and 6 clusters of MT-ZVL incidence in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei between 2015 and 2019, which corresponded to spatial-temporal distribution determined by hotspot analysis. Two grade I clusters were detected in southern Gansu province and eastern Shanxi province, demonstrating that Yangquan city, in eastern Shanxi province, is a recent high-risk region of MT-ZVL, in addition to the southern Gansu province. Flexible spatial scan statistic identified 3, 2, 4, 4 and 4 clusters of MT-ZVL incidence in Gansu, Shaanxi, Sichuan, Shanxi, Henan and Hebei provinces between 2015 and 2019, with similar annual MLCs identified predominantly in southern Gansu province. Secondary clusters varied between years, however, with a gradual shift observed from southern Gansu and northern Sichuan province to Shaanxi-Shanxi border areas and eastern Shanxi province.

Reinforced control efforts have resulted in a substantial decline in number of VL cases in China during the 2015 to 2019 time period [26], however, transmission of MT-ZVL appears to be increasing, rather than declining. Although sporadic outbreaks of MT-ZLV appear in China, there has been a substantial increase in the MT-ZVL epidemics during the past three years [11], with an increase in high-incidence clusters in localised regions [27]. Although the majority of MT-ZVL clusters were detected the southern Gansu and northern Sichuan provinces [28], regions traditionally at higher risk of MT-ZVL infection, re-emergence of MT-ZVL epidemics have also recently been detected in Shanxi, Henan and Hebei provinces, where MT-ZVL transmission had been controlled [11]. These areas are extension regions of the Loess Plateau and are mainly hilly settings, where the Yanshan-Taihangshan mountain deciduous broad-leaved forest ecological zone and Fenwei Basin Agro-ecological zone are located [29]. As temperate continental monsoon climate-covered regions, it is hot and rainy in summer, and the hilly and frondent environments provide a favorable condition for the breeding and reproduction of wild sandflies. Moreover, local loess cave dwellings and dwellings made of cement, bricks and tiles provide a suitable habitat for sandfly breeding grounds [30]. Infected animals also carry a higher risk of VL transmission and re-emergence in localised areas [31]. As a result of sustained periods of neglected VL, diagnosis, screening and management of this disease have been weakened, which also may have contributed to resurgence of VL transmission[32]. Therefore, the reinforcement of VL control in conventionally high-risk areas, attention to areas where VL re-emergence is likely, timely survey of vectors, assessment of transmission risk, and targeted interventions are strongly recommended to reduce risk of MT-ZVL infection.

There are also some limitations in this study. First, MT-ZVL mainly occurs in remote rural areas, and some cases may not go to the doctor in time due to mild symptoms or traffic restrictions. These cases were not reported to the local CDC, so the situation of MT-ZVL may be underestimated in this study. Secondly, this study focuses on MT-ZVL re-emergence and clustering areas, and there is no definite conclusion on the causes of high cluster in local areas. In future studies, accurate case data can be obtained through field investigations, and the reasons for high cluster in local areas can be further studied, so as to provide further technical support for the VL control in this region.

Data Availability

Not all data can be shared publicly because the data includes individual information, such as the name, gender, age, address, ID number, and so on, and the information is strictly protected and not permitted to be distributed without approval from the government. Therefore, the case information cannot be shared after analysis. However, if any researchers are interested in accessing the data without commercial purpose, they can contact Dr. Shang Xia, who will reach out to the government for permission. The contact information: Dr Shang Xia, sxia@nipd.chinacdc.cn 008621-54241570 Chief of Informatics Center Department National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention No. 207, Ruijin Er Road, Shanghai 200025, China.

Funding Statement

This study was supported by the National Special Science and Technology Project for Major Infection Diseases of China (No. 2016ZX10004222-004) (YH, XH, YG, QW, SL), the scientific investigation on regional climate-sensitive diseases in China (No.2017FY101203) (JX, ZZ, YL, YZ), the Fifth Round of Three-Year Public Health Action Plan of Shanghai (No. GWV-10.1-XK13) (YH, JX, SL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0009152.r001

Decision Letter 0

Nadira D Karunaweera, Johan Van Weyenbergh

10 Nov 2020

Dear Dr. Li,

Thank you very much for submitting your manuscript "Spatio-temporal clustering of Mountain-type Zoonotic Visceral Leishmaniasis in China between 2015 and 2019" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.  

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[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. 

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[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Sincerely,

Johan Van Weyenbergh

Associate Editor

PLOS Neglected Tropical Diseases

Nadira Karunaweera

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: The statistical and models are well written but some conceptual information needs to be reviewed. For instance, it is not clear which variables were selected as input for the clustering model and why. Also, a general description of the study area is missing. Understanding the region and main attributes of the size provinces are essential for the discussion. Moreover, the mentioned data source for the samples is not included as a reference.

Reviewer #2: (No Response)

Reviewer #3: The objectives for this study are clearly articulated, and I feel that the overall study design is appropriate for the stated objectives.

The population is clearly described and appropriate for this study.

The sample size is sufficient to support the conclusions drawn.

One of the questions that I have regarding the statistical analysis used is that three different types of cluster analysis were used to describe the clustering of this disease. What is the benefit of using the different types of cluster analysis? This research did not discuss how the different methods could contrast or support each other. Also some of the terms used is the results, specifically a grade 1 cluster, were not explained before hand.

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: In general, the results are disorganized. The annual incidences presented in the text do not match the total number (529) nor the labels in the graph in the first figure. It is noticeable, that whenever results are presented for the six provinces respectively, it creates confusion for the reader due to miswriting. For instance, 5 different values ​​are written as hotspots of infection respectively for the 6 provinces. Regarding results discussion, it gets confused with the introduction. The first three paragraphs are not analyzing the results but reporting general and conceptual facts as in the introduction. The discussion needs to be rewritten. In addition, it is important to represent cities mentioned in the text on the map, therefore readers who do not know the region would not get lost in the discussion.

Reviewer #2: (No Response)

Reviewer #3: The analysis presented does match the analysis described in the methods.

The results are clearly presented, however, as mentioned in my comments for the methods, it was not made clear my three different cluster analysis methods were used when the results indicate very similar outcomes. More information either in this section or the discussion about the comparison and contrasts between the methods would be beneficial, or reducing the number of cluster analysis methods used.

Overall, the figures present the information presented, however there are some small additions that I feel would help deliver the information better. First, I would include a map of study area within the context of China as a whole for audience members that may not be familiar with the locations of Chinese provinces. Second, since the disease is a "Mountain-type" disease and the paper mentions that there are geographic and ecological variations in may be useful to include information on the elevation in the selected study area to help people unfamiliar with the provinces understand if the incidence areas are close to mountains or not. Third, some minor clarifications and improvements to the maps presented would present a clearer picture of the results. I think that the figures in general should include a neat line around each individual map, that the word "legend" should be removed from the legends, and the scale bars should be standardized to either 500 km or 1000 km. Fourth, in Figure 5, a-e, it is not readily apparent what the circles on the map are indicating. Is this a part of the SatScan analysis used? In the main body of the text there is no indication of what these circles mean.

--------------------

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: The conclusion does not make a clear statement of limitations, recommendations, or relevance of the study. Neither supports the article's objective. It is included in the objective the will to indicate important insights identified through the study for developing interventions aimed at the disease in question. However, nothing is mention in the conclusion.

Reviewer #2: (No Response)

Reviewer #3: There is material in the conclusion section in the general discussion of MT-ZVL that I feel would be better supported in the introduction of the paper rather than in the conclusions. Specifically, I feel that the second to fifth paragraphs, starting with "Currently, there are three types of VL in China..." and continuing for the next two paragraphs, feels like it should be included in the introduction as a general description of the disease. Aside from this, I feel that the conclusions are supported by the data and analysis presented, although a discussion of what differences are coming from the different cluster analyses used would be helpful.

The limitations are clearly described.

The authors do discuss the relevance of the analysis to the current medical field and addresses the relevance to public health.

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: The map panels must be reviewed. It is not clear, neither on the image nor in the figure's description, the referenced year for each map. For maps with the same legend (eg. Fig 4), I suggest adding a bigger legend only once. Colors' differences between groups in Figures 5 and 6 are not clear - different colors would be better for highlighting groups than a gradient of a unique color. It is important to standardize the clusters' names in text and figures. It is sometimes referred to as level or sometimes as a grid. Moreover, it is necessary to review the references. In general, it does not follow the right pattern for referencing digital media and websites.

Reviewer #2: (No Response)

Reviewer #3: As discussed in the results section, there are some general points about the maps that will help with the data presentation:

Overall, the figures present the information presented, however there are some small additions that I feel would help deliver the information better. First, I would include a map of study area within the context of China as a whole for audience members that may not be familiar with the locations of Chinese provinces. Second, since the disease is a "Mountain-type" disease and the paper mentions that there are geographic and ecological variations in may be useful to include information on the elevation in the selected study area to help people unfamiliar with the provinces understand if the incidence areas are close to mountains or not. Third, some minor clarifications and improvements to the maps presented would present a clearer picture of the results. I think that the figures in general should include a neat line around each individual map, that the word "legend" should be removed from the legends, and the scale bars should be standardized to either 500 km or 1000 km. Fourth, in Figure 5, a-e, it is not readily apparent what the circles on the map are indicating. Is this a part of the SatScan analysis used? In the main body of the text there is no indication of what these circles mean.

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: The article lacks a discussion on how its results impact society and local institutions of disease control.

Reviewer #2: This is interesting research to explore the dynamics of Mountain-type Zoonotic Visceral Leishmaniasis in China between 2015 and 2019. The authors did a great work to analyze the data by using some modern analysis methods. The manuscript is well written and acceptable. However, I have some comments for the authors.

1.In material and methods section, how long was the time interval in your data acquisition? Monthly or Yearly? If the MT-ZVL incidence is annual data, how to ensure validity in the Joinpoint model by limited values?

T-test is not suitable because the data within a certain period of time was dependent, unless chi square test checked its independence. Or use chi square test to check incidence of the same year between 6 provinces.

2.In spatial autocorrelation analysis, which disease indicator was employed to calculate Moran’I index? It should be clarified by illustrative statements.

3.In results section, for hotspots, it should be explained more clearly in methods section about the map. And the year should be annotated in the legend.

Reviewer #3: I have no additional comments

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PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0009152.r003

Decision Letter 1

Nadira D Karunaweera, Johan Van Weyenbergh

15 Jan 2021

Dear Dr. Li,

We are pleased to inform you that your manuscript 'Spatio-temporal clustering of Mountain-type Zoonotic Visceral Leishmaniasis in China between 2015 and 2019' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Johan Van Weyenbergh

Associate Editor

PLOS Neglected Tropical Diseases

Nadira Karunaweera

Deputy Editor

PLOS Neglected Tropical Diseases

***********************************************************

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PLoS Negl Trop Dis. doi: 10.1371/journal.pntd.0009152.r004

Acceptance letter

Nadira D Karunaweera, Johan Van Weyenbergh

13 Mar 2021

Dear Dr. Li,

We are delighted to inform you that your manuscript, "Spatio-temporal clustering of Mountain-type Zoonotic Visceral Leishmaniasis in China between 2015 and 2019," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any scientific or type-setting errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Note: Proofs for Front Matter articles (Editorial, Viewpoint, Symposium, Review, etc...) are generated on a different schedule and may not be made available as quickly.

Soon after your final files are uploaded, the early version of your manuscript will be published online unless you opted out of this process. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

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    Data Availability Statement

    Not all data can be shared publicly because the data includes individual information, such as the name, gender, age, address, ID number, and so on, and the information is strictly protected and not permitted to be distributed without approval from the government. Therefore, the case information cannot be shared after analysis. However, if any researchers are interested in accessing the data without commercial purpose, they can contact Dr. Shang Xia, who will reach out to the government for permission. The contact information: Dr Shang Xia, sxia@nipd.chinacdc.cn 008621-54241570 Chief of Informatics Center Department National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention No. 207, Ruijin Er Road, Shanghai 200025, China.

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