Technology has been increasingly applied in search for excellence in water resource management. Tools such as demand-forecasting models provide information for utility companies to make operational, tactical and strategic decisions. Also, the performance of water distribution systems can be improved by anticipating consumption values. This work aimed to develop models to conduct monthly urban water demand forecasts by analyzing time series, and adjusting and testing forecast models by consumption category, which can be applied to any location. Open language R was used, with automatic procedures for selection, adjustment, model quality assessment and forecasts. The case study was conducted in the city of Joinville, with water consumption forecasts for the first semester of 2018. The results showed that the seasonal ARIMA method proved to be more adequate to predict water consumption in four out of five categories, with mean absolute percentage errors varying from 1.19 to 15.74%. In addition, a web application to conduct water consumption forecasts was developed.

Objectives: to identify the prevalence of neonatal near miss morbidity in the city of Joinville, SC and the associated factors. Methods: a populational based cross-sectional study including all live births in 2012 registered at SINASC. The near miss cases were identified based on the weight <1500g, Apgar scores at 5th minute <7, gestational age <32 weeks, use of mechanical ventilation or presence of congenital malformation. The gross odds ratios (OR) and its respective 95% confidence intervals (95% CI) were calculated and the logistic regression was performed to obtain the adjusted odds ratios and its respective 95% CI. Results: the prevalence of near miss was 33 per thousand live births (95% CI: 29-37). In the final model, a risk classification of live births according to the City Program (Programa Municipal) (ORaj= 19.7; 95% CI: 14.2 to 27.2), cesarean section (ORaj= 2.1; 95% CI:1.5 to 2.8) and public hospital (ORaj= 1.7; 95% CI: 1.2 to 2.3) remained associated to morbidity near miss. Conclusions: near miss morbidity was 7.3 times higher than neonatal mortality. To know its determinants in different national contexts may include some changes in the focus of public health actions by redirecting to preventive interventions.

This study aims to identify the factors that may influence water consumption in single-family households in the city of Joinville, Southern Brazil. Through questionnaires, data were collected from 108 households in several neighborhoods of the city. The questionnaires contained open-ended and closed-ended questions involving the surrounding infrastructure, socio-economic and demographic characteristics, constructive characteristics, installed plumbing fixtures, and water-use habits, totaling 57 variables. The independent variables were correlated to monthly water consumption (m³/month/household) and per capita consumption (liters/person/day) of each household. The statistically significant variables that affected households water consumption were related to demographic characteristics such as number of residents and educational level, construction features (i.e., number of bathrooms, building age, and built area), the presence of water-efficient appliances and water conservation habits. The results obtained can contribute to the development of new studies on water consumption and sustainable policies and awareness on the importance of water conservation.

Urban water consumption can be influenced by several factors and may have different patterns due to regional and local specificities. This research aims to determine the influence of climatic, geographic, demographic, economic, social and water-supply-service-related factors on the per capita water consumption in Brazil using a dataset with 1746 municipalities. For this purpose, a multiple linear regression model using log-transformed data was fitted. The results show that household income, water tariff, number of residents per household, temperature, percentage of households with washing machine, city population, gender, percentage of households with piped water and the municipality’s GDP significantly influence urban water consumption.