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Determining the rural domestic water consumption (RDWS, L/person-day) and rural sewage production (RSP, L/person-day) of rural residents at the county scale can provide decision support for rural domestic sewage treatment. However, methods for predicting county-scale RDWS and RSP based on geographical, social, and governance factors are rarely reported. This paper proposes an RDWS and RSP prediction method based on Automated Machine Learning (AutoML) for the Inner Mongolia Autonomous Region. The RDWS and RSP in the Inner Mongolia Autonomous Region were successfully predicted from multi-source data. The AutoML model accurately predicted RDWS (mean absolute error = 1.7072, R2 = 0.7759) and RSP (mean absolute error = 0.6580, R2 = 0.65107). Average years of education (AYOS), proportion of resident population, and proportion of elderly and children (POEAC) were key driving factors related to RDWS prediction. The most important factors associated with RSP prediction were AYOS, POEAC, and annual average precipitation (AAP). An explanatory analysis further revealed dependence between the input variables, RDWS, and RSP. The framework was demonstrated to be a practical tool for qualitative and quantitative analyses of rural domestic water consumption and domestic sewage production in the Inner Mongolia Autonomous Region. Overall, this study provides a solution for predicting county-level RDWS and RSP, providing a reference for the design of rural domestic sewage treatment. [Display omitted] • Less than 24% of rural domestic sewage is currently treated in Inner Mongolia. • Planning needs county-scale domestic water consumption and sewage production data. • Best of many models based on data from multiple sources were selected automatically. • Nonlinear relationships between input output variables were quickly revealed. [ABSTRACT FROM AUTHOR]

• The five subtypes of C. parvum we detected were all new subtypes. • All the positive samples were C. parvum. • The overall prevalence was 12.85% in diarrhea children. • There were significant differences in the prevalence at different ages. • Positive samples were detected throughout the year. Cryptosporidia (Cryptosporidium) is a protozoan that is widely parasitic in the intestinal cells of humans and animals, and it is also an important zoonotic parasite. However, there is no epidemiological investigation on Cryptosporidium spp. infection in infants with diarrhea of Inner Mongolia, the largest livestock region in China. To investigate the prevalence of Cryptosporidium , 2435 fresh fecal samples were collected from children with diarrhea in Inner Mongolia Maternal and Child Health Care Hospital. Molecular characterization of Cryptosporidium was carried out based on its 18S rRNA and gp60 gene sequences. The overall prevalence was 12.85% (313/2435), and in Hohhot (12.15%), it was lower than that in the surrounding city (14.87%) (P < 0.05). Moreover, Cryptosporidium was detected in different seasons and sexes. Concerning the age of children with diarrhea, the prevalence of those age groups between 0 and 1 was obviously lower than others, and there were significant differences in the prevalence at different ages (P < 0.001). Analysis of the 18S rRNA gene sequence revealed that all the positive samples were Cryptosporidium parvum , and there were 5 subtypes (IIdA23G3, IIdA24G3, IIdA24G4, IIdA25G3, and IIdA25G4). To the best of our knowledge, the above subtypes have not been reported. Our results provide a relevant basis for control and education on food safety and foodborne illness prevention. [ABSTRACT FROM AUTHOR]