Bryan Ignatius P. Sanchez, Francis James A. Lagang, Christine D. Bandalan, Frances E. Edillo, Gerard D. Ompad
Climate change affects public health, particularly through increased Aedes-borne dengue illnesses. This study investigated dengue transmission across time and space in Cebu Province, Philippines using wavelet-based time-series analysis and causal inference. Continuous Wavelet Transform (CWT) revealed strong annual cycles of dengue incidence between 2013 and 2023, with disruptions during the COVID-19 pandemic. Post-pandemic recovery of seasonality was fastest in Cebu city, while Mandaue city and Lapu-Lapu city experienced extended disturbances. Using the Peter and Clark Momentary Conditional Independence (PCMCI) algorithm, lagged causal effects from relative humidity and the minimum infection rates (MIR) of DENV-2 and DENV-4 transmitted by Aedes albopictus (Skuse) in Cebu and Mandaue cities were determined. In contrast, Lapu- Lapu city was influenced by temperature, precipitation, and the MIR of DENV-1 and DENV-4. These findings underscore the value of integrating climate and arboviral data in a dengue vector species in localized outbreak forecasting. © 2025 IEEE.
University of San Carlos, Dept. of Computer, Info. Sci. & Math., Cebu City, Philippines; University of San Carlos, Mosquito Research Lab, Dept. of Biology, Cebu City, Philippines; Kobenhavns Universitet, Dept. of Drug Design & Pharmacology, Copenhagen, Denmark
