Modelling spatio-temporal data of dengue fever using generalized additive mixed models.

Area of Science:

• Environmental Science
• Epidemiology
• Biostatistics

Background:

• Weather patterns significantly influence dengue fever transmission.
• Understanding these complex associations is crucial for effective public health interventions.

Purpose of the Study:

• To develop a Spatio-temporal model for dengue fever using a Generalized Additive mixed model (GAMM).
• To identify key climatic and non-climatic factors affecting dengue transmission dynamics.

Main Methods:

• Utilized a Generalized Additive mixed model (GAMM) incorporating unknown smoother functions.
• Integrated extensive datasets from Venezuela's Ministry of Health and NASA satellite climate data.
• Analyzed climatic covariates for statistically significant lagged effects.

Main Results:

• Climatic factors demonstrated statistically significant lagged effects on dengue transmission.
• Optimal temperature ranges for critical dengue case levels were identified between 26°C and 30°C.
• The El Niño phenomenon, within 26.5°C-28.0°C, was found to have a severe impact on dengue transmission.

Conclusions:

• The developed Spatio-temporal GAMM effectively captures dengue fever dynamics.
• Temperature and El Niño are critical predictors for dengue outbreaks.
• Findings support enhanced disease surveillance and targeted interventions based on climatic forecasting.