The objective of the simple open comparative clinical research is the development of a mathematical model for forecasting severe bronchial obstruction syndrome (BOS) in children with acute respiratory viral infections at the pre-hospital stage. The sample range with the confidence interval of 95% and a possible error of 5% constituted 384 people; the total number of examined children was 386. The criteria of patient selection: age ranging from one month to five years; acute respiratory viral infection complicated with the bronchial obstruction syndrome; absence of any chronic concurrent diseases; absence of the confirmed pre-existing bronchial asthma diagnosis; negative results of Mycoplasmae spp., Chlamidia spp. tests; informed consent to participation in the survey. The first main group (I) consisted of 94 children with severe BOS; the comparison group (II) was made up of 292 patients of the same age with light and medium syndrome. For every patient, 34 features characterizing the family history, specificity of the pregnancy and birth, feeding specificity, presence of allergies, and environmental conditions of the residence area. The collected data were converted to the scoring system and statistically processed with the Statistica 10.0 (StatSoft Inc., USA) and IBM SPSS Statistics software. The impact made by the independent variables of the index in question was assessed with the multifactor logistic regression model, which included the variables that reached the value of p ≤ 0,05 in the two-dimensional analysis. After that, all the indexes were analyzed with the Kullback information measure and Wald test to produce a diagnostic table for forecasting severe disease development. The quality of the produced model was qualified as excellent (area under the ROC curve AUC = 0.912, p < 0.001) with high sensitivity (Se = 88.2%) and specificity (Sp = 94.1%) values. Wald consistency test and diagnostic table development can be qualified as a high precision method for forecasting the BOS progress in children at the pre-hospital stage. #COMESYSO1120.