ABSTRACT
Objective To investigate the prevalence of mountain-type zoonotic visceral leishmaniasis (MT-ZVL) in Yangquan City, Shanxi Province from 2015 to 2020, so as to provide the scientific evidence for formulating the MT-ZVL control strategy. Methods The epidemiological data pertaining to MT-ZVL cases in Yangquan City from 2015 to 2020 were collected and descriptively analyzed. A Joinpoint regression model was created to analyze the trend in the MT-ZVL incidence in Yangquan City from 2015 to 2020 using annual percent change (APC). The sandflies surveillance data and the prevalence of Leishmania infections in dogs were collected in Yangquan City in 2020, and the regional distribution of sandflies density and sero-prevalence of Leishmania infections in dogs were calculated. In addition, the associations of sandflies density and sero-prevalence of Leishmania infections in dogs with the incidence of human MT-ZVL were examined using the linear correlation analysis. Results A total of 162 MT-ZVL cases were reported in Yangquan City, Shanxi Province from 2015 to 2020, with annual mean incidence of 1.9/105, and there were 4, 7, 16, 27, 33 cases and 75 cases with MT-ZVL reported from 2015 to 2020, appearing a tendency towards a rapid rise (APC = 72.79%, t = 11.10, P < 0.01). MT-ZVL cases were reported across the five counties (districts) of Yangquan City, and the cases predominantly occurred in Jiaoqu District (35.2%, 57/162) and Pingding County (33.3%, 54/162). MT-ZVL cases were predominantly detected in residents at ages of 15 years and older (71.6%, 116/162) and at ages of 0 to 2 years (22.2%, 36/162), with farmers (37.4%, 61/162) and diaspora children (24.5%, 40/162) as predominant occupations. The mean density of Phlebotomus chinensis was 6.3 sandflies per trap per night in Yangquan City from during the period from May to September, 2020, with the highest density observed in Jiaoqu District (12.6 sandflies per trap per night) and the lowest in Yuxian County (1.1 sandflies per trap per night), and there was a region-specific mean density of Ph. chinensis in Yangquan City (H = 17.282, P < 0.01). The sero-prevalence of serum anti-Leishmania antibody was 7.4% (2 996/40 573) in domestic dogs in Yangquan City, with the highest sero-prevalence seen in Jiaoqu District (16.6%, 1 444/8 677), and the lowest in Yuxian County (2.3%, 266/11 501), and there was a region-specific sero-prevalence rate of anti-Leishmania antibody in domestic dogs in Yangquan City (χ2 = 1 753.74, P < 0.01). The sero-prevalence of anti-Leishmania antibody was significantly higher in stray dogs (20.0%, 159/794) than in domestic dogs (χ2 = 176.63, P < 0.01). In addition, there were significant associations among the sandflies density, sero-prevalence of anti-Leishmania antibody in domestic dogs and the incidence of human MT-ZVL (r = 0.832 to 0.870, all P values < 0.05). Conclusions The prevalence of MT-ZVL appeared a tendency towards a rapid rise in Yangquan City from 2015 to 2020, and systematic interventions are urgently needed for MT-ZVL control.
ABSTRACT
OBJECTIVE@#To predict the trends for fine-scale spread of Oncomelania hupensis based on supervised machine learning models in Shanghai Municipality, so as to provide insights into precision O. hupensis snail control.@*METHODS@#Based on 2016 O. hupensis snail survey data in Shanghai Municipality and climatic, geographical, vegetation and socioeconomic data relating to O. hupensis snail distribution, seven supervised machine learning models were created to predict the risk of snail spread in Shanghai, including decision tree, random forest, generalized boosted model, support vector machine, naive Bayes, k-nearest neighbor and C5.0. The performance of seven models for predicting snail spread was evaluated with the area under the receiver operating characteristic curve (AUC), F1-score and accuracy, and optimal models were selected to identify the environmental variables affecting snail spread and predict the areas at risk of snail spread in Shanghai Municipality.@*RESULTS@#Seven supervised machine learning models were successfully created to predict the risk of snail spread in Shanghai Municipality, and random forest (AUC = 0.901, F1-score = 0.840, ACC = 0.797) and generalized boosted model (AUC= 0.889, F1-score = 0.869, ACC = 0.835) showed higher predictive performance than other models. Random forest analysis showed that the three most important climatic variables contributing to snail spread in Shanghai included aridity (11.87%), ≥ 0 °C annual accumulated temperature (10.19%), moisture index (10.18%) and average annual precipitation (9.86%), the two most important vegetation variables included the vegetation index of the first quarter (8.30%) and vegetation index of the second quarter (7.69%). Snails were more likely to spread at aridity of < 0.87, ≥ 0 °C annual accumulated temperature of 5 550 to 5 675 °C, moisture index of > 39% and average annual precipitation of > 1 180 mm, and with the vegetation index of the first quarter of > 0.4 and the vegetation index of the first quarter of > 0.6. According to the water resource developments and township administrative maps, the areas at risk of snail spread were mainly predicted in 10 townships/subdistricts, covering the Xipian, Dongpian and Tainan sections of southern Shanghai.@*CONCLUSIONS@#Supervised machine learning models are effective to predict the risk of fine-scale O. hupensis snail spread and identify the environmental determinants relating to snail spread. The areas at risk of O. hupensis snail spread are mainly located in southwestern Songjiang District, northwestern Jinshan District and southeastern Qingpu District of Shanghai Municipality.