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.
Subject(s)
Animals , Bayes Theorem , China/epidemiology , Ecosystem , Gastropoda , Supervised Machine LearningABSTRACT
A blueprint on Shanghai’s ecological space design between 2021 and 2035 was released in 2021, aiming to build an ecological city and improve the development of ecological civilization. The transmission of parasitic diseases is strongly associated with climate and ecological environments. Currently, the prevalence of parasitic diseases has been maintained at extremely low-transmission levels, and there are almost no local cases; however, the alteration of ecological environments may results in a potential transmission risk of parasitic diseases. Hereby, the current status of key parasitic diseases in Shanghai Municipality was described, and the potential transmission risk of parasitic diseases and responses to this risk were analyzed during the construction of an ecological city in Shanghai Municipality. In addition, the suggestions pertaining to surveillance and management of parasitic diseases were proposed during the mid- and long-term construction of an ecological city in Shanghai Municipality.
ABSTRACT
Objective@#To analyze the current status and equity of mental health resources allocation in Shanghai Municipality, so as to provide data supports to formulate mental health action plans and relevant policies. .@*Methods@#The data pertaining to mental health institutions, actual beds opened, certified or assistant psychiatrists and registered nurses was collected from the Survey of the Current Status on Mental Health Resources in Shanghai Municipality in 2020. The equity of mental health resources allocated by population and geographical area in Shanghai Municipality was evaluated with Lorenz curve and Gini coefficient@*Results@#There were 96 mental health institutions, 15 060 actual beds opened, 257 certified or assistant psychiatrists and 2 887 registered nurses in Shanghai Municipality in 2020, with a physician-to-nurse ratio of 1∶2.30. The greatest numbers of actual beds opened in the department of psychiatrics, the number of certified or assistant psychiatrists and the number of registered nurses per 10 000 residents and per km2 were all found the central urban areas. The numbers of actual beds opened in the department of psychiatrics, the number of certified or assistant psychiatrists and the number of registered nurses per 10 000 residents were 6.06 beds, 0.51 physicians and 1.16 nurses, with Gini coefficients of 0.36, 0.42 and 0.44, respectively, and the numbers of actual beds opened in the department of psychiatrics, the number of certified or assistant psychiatrists and the number of registered nurses per km2 were 2.38 beds, 0.20 physicians and 0.46 nurses, with Gini coefficients of 0.72, 0.76 and 0.75, respectively. @*Conclusions@#There was a gross equity in mental health resources allocated by population and geographical area in Shanghai Municipality in 2020, which showed an improvement as compared to 2015. The equity in mental health resources allocated by geographical area was lower than that by population in Shanghai Municipality.