The objective of the study was to determine the possible incidence of the ROR methodology in the modelling of the total number of monthly mosquito outbreaks in Villa Clara province, Cuba. The research covered the 13 municipalities of the province, as well as the number of outbreaks reported in the different months of the period analyzed (2010-2020). A descriptive, ecological, retrospective and statistical study was carried out, for which all the information on the work cycles established for surveillance and vector control was collected. The data were organized in the Windows Excel application, by years and months. The forecast of the outbreaks was modelled employing the Objective Regressive Regression Methodology (ROR), with the use of dichotomous variables DS, DI, and NoC. Forty-five mosquito species distributed in 13 genera were identified, with the best represented and distributed species being Anopheles albimanus, Aedes aegypti, Ae. albopictus, Ae. scapularis, Culex quinquefasciatus, Cx. nigripalpus and Psorophora confines. The summary of the ROR model for the total monthly foci in Villa Clara explained 91% of the variance, with a low error for the focal point, where the analysis of variance corroborated that the model was valid and significant at 100%. The model parameters, SD, DI, and trend were significant, with a tendency to increase the number of foci per month. We conclude that it is possible to model mosquito outbreaks in a given geographical area, however large it may be, and even to predict the behaviour of the outbreaks in the short, medium, and long term.