Resumen

CHIRINO BELTRAN, Marisoly; CRUZ RODRIGUEZ, Elba; AMARAN GONZALEZ, Rocio  y  GONZALEZ BENITEZ, Neilys. Bayesian network to determine the presence of Rotavirus according to seasonality, climate variability and climate change. Serie Científica [online]. 2025, vol.18, n.1, pp. 147-161.  Epub 01-Ene-2025. ISSN 2306-2495.

The aim of the study was to determine the relationship between the presence of rotavirus and factors such as seasonality, climate variability and climate change using Bayesian networks. To do so, historical information on the incidence of rotavirus in different regions was collected, as well as relevant climate data, including temperature, precipitation and humidity. The methodology used consisted of building a Bayesian network model that integrated these variables, allowing the analysis of their interactions and their impact on the transmission of the virus. The results obtained indicated that the incidence of rotavirus showed a clear seasonality, with peaks during the warmer and rainier months. In addition, it was observed that climate variability influenced the frequency of outbreaks, suggesting that changes in climate conditions could alter the dynamics of the disease. The model also indicated that climate change projections could increase the incidence of rotavirus in certain regions, highlighting the need to monitor these factors continuously. In conclusion, the findings of the study underline the importance of considering interactions between climate and public health in planning rotavirus prevention and control strategies. The application of Bayesian networks not only allowed a better understanding of these factors, but also provided a valuable tool to anticipate future outbreaks, thus contributing to the protection of children's health.

Palabras clave : rotavirus; seasonality; climate variability; Bayesian networks; public healt.

        · resumen en Español     · texto en Español     · Español ( pdf )