Forecasting daily Covid-19 cases in the world with a hybrid ARIMA and neural network model.

The use of models to predict disease cases is common in epidemiology and related areas, in the context of Covid-19, both ARIMA and Neural Network models can be applied for purposes of optimized resource management, so the aim of this study is to capture the linear and non-linear structures of daily Covid-19 cases in the world by using a hybrid forecasting model. In summary, the proposed hybrid system methodology consists of two steps. In the first step, an ARIMA model is used to analyze the linear part of the problem. In the second step, a neural network model is developed to model the residuals of the ARIMA model, which would be the non-linear part of it. The neural network model was superior to the ARIMA when considering the capture of weekly seasonality and in two weeks, the combination of models with the capture of seasonality in two weeks provided a mixed model with good error metrics, that allows actions to be premeditated with greater certainty, such as increasing the number of nurses in a location, or the acceleration of vaccination campaigns to diminish a possible increase in the number of cases.

Determinants of mild-to-moderate malnutrition in preschoolers in an urban area of Northeastern Brazil: a hierarchical approach.

OBJECTIVE: To investigate the determinants of mild-to-moderate malnutrition in preschoolers. DESIGN: Cross-sectional study conducted in October and November 1996, with a representative sample of 1740 children less than 5 years old from the city of Salvador, situated in the Brazilian Northeastern region. Socio-economic and dietary data were collected through a structured questionnaire. Anthropometric measures were performed in duplicate and data analysis was based upon the hierarchical model approach. Logistic regression analysis was used to estimate the prevalence ratio and to identify the determinants of mild-to-moderate deficits in weight-for-age and height-for-age Z-scores. RESULTS: Family monthly income under US$67.00 per capita and family headed by a woman were the main basic determinants of mild-to-moderate weight-for-age and height-for-age deficits in the studied children. Household agglomeration, an underlying determinant, was associated with weight-for-age and height-for-age deficits. Among the immediate determinants, age above 6 months and dietary caloric availability in the lowest tertile (<930 kcal day-1) were also associated with weight-for-age deficits. In addition to these, hospitalisation in the 12 months preceding the interview was shown to be a predictor of mild-to-moderate weight-for-age and height-for-age deficits. CONCLUSION: Adverse social and economic factors interact with family environmental factors to define food consumption and morbidity patterns that culminate in a high prevalence of mild-to-moderate malnutrition. The strengthening and restructuring of nutrition and healthcare actions, the definition of public policies that improve family income, and the adequate insertion of women in the labour market are possible strategies to reduce mild-to-moderate malnutrition and to sustain the decline already observed in severe malnutrition.