Predicción de la epidemia del virus respiratorio sincitial en Bogotá D.C. utilizando variables climatológicas. - González-Parra, Gilberto; Querales, José F.; Aranda, Diego (Biomédica: Revista del Instituto Nacional de Salud, 2016)
Introduction: The respiratory syncitial virus is the most common cause of bronchiolitis and pneumonia in children younger than 1 year of age in the United States and is being recognized more often as a significant cause of respiratory illness in older adults. Objective: Predict the initial week of the outbreak using climatological measurements as predictor variables. Use the Naïve Bayes classifiers and the receiver operating characteristic curves to estimate the initial outbreak week and find the most important climatological variables for the prediction of the outbreak. Material and methods: The initial dates of the outbreaks for children younger than five years old for the period 2005-2010 were obtained for Bogota D.C. We selected the climatological variables using the correlation matrix and 1020 models were constructed using different climatological variables and data from different weeks previous to the initial outbreak. In addition, the models were also selected using six years period (2005-2010), four years (2005-2008) and two years (2009-2010). Using the Naïve Bayes classifiers and the receiver operating characteristic curves we obtained the best predictive models and the most relevant climatological variables to predict the outbreak. Results: The best models were the ones that use the two year period (2009- 2010) and the week 0, with a 52% and 60% of effectiveness respectively. The humidity was the variable that appeared most in the best models with a 62%. We used the Naïve Bayes classifiers to investigate which are the best models to predict correctly the initial week of the outbreak Conclusions: The results suggest that the best models should use humidity, wind speed and minimum temperature in order to be able to predict the outbreak.