Integrated Framework for Climate and Epidemiological Data Analysis: Potential for Monitoring

• Franklin Sales de Oliveira UFPel
• Dulcinéia Esteves Santos UFPel
• Bianca Conrad Bohm UFPel
• Brenda Salenave Santana UFPel

DOI: https://doi.org/10.5753/wcama.2025.9235

Abstract

This paper proposes an automated framework to integrate climatic and epidemiological data, considering time lags, in order to capture the interactions between these factors. The system generates datasets that align arboviral disease cases with climatic variables, performing analyses to identify correlations. To demonstrate its applicability, a case study in Belo Horizonte, MG, was conducted, highlighting temporal patterns between environmental factors and disease incidence.

References

Araújo, R. A. F., Uchôa, N. M., and Alves, J. M. B. (2019). Influência de variáveis meteorológicas na prevalência das doenças transmitidas pelo mosquito aedes aegypti. Revista Brasileira de Meteorologia, 34:439–447.

Harshit and Harjule, P. (2024). Mathematical modelling and analysis of dengue transmission dynamics. Procedia Computer Science, 235:539–548. International Conference on Machine Learning and Data Engineering (ICMLDE 2023).

Rocklöv, J. and Tozan, Y. (2019). Climate change and the rising infectiousness of dengue. Emerging Topics in Life Sciences, 3(2):133–142.

Soares, P. V., Araújo, R. A. F. d., and Almeida, M. E. (2022). A influência das variáveis meteorológicas na ocorrência de casos de dengue em fortaleza, ceará. Revista Brasileira de Meteorologia, 36:759–766.

World Health Organization (2024a). Chikungunya. Accessed: 23-Feb-2025.

World Health Organization (2024b). Dengue and severe dengue. Accessed: 23-Feb-2025.