Analysing dengue fever spread in Kenya using the Zero-Inflated Poisson model.

Abstract

Background: Dengue fever (DF), transmitted by Aedes mosquitoes, remains a major public health concern in tropical and subtropical regions. Understanding the influence of climatic variables on DF incidence is essential for improving outbreak prediction and control measures.

Aim: This study analysed the impact of climatic factors on DF incidence in Kenya using a Zero-Inflated Poisson (ZIP) model.

Setting: The study focused on DF cases in Kenya from 2019 to 2021.

Methods: A ZIP model was applied to monthly dengue case data and associated climatic variables, such as temperature, rainfall, and humidity. The model addresses over-dispersion and excess zeros in the data, providing a more accurate depiction of DF dynamics.

Results: The ZIP model revealed significant associations between climatic variables and DF incidence. Humidity (β = 0.0578, standard error [s.e.] = 0.0024, z = 24.157, p < 2e-16) and temperature (β = 0.0558, s.e. = 0.0053, z = 10.497, p < 0.01) showed a positive relationship with dengue cases, while rainfall (β = -0.0045, s.e. = 0.0003, z = -16.523, p < 0.01) had a significant negative effect. The over-dispersion test confirmed excess variability in the data (O statistic = 456.3, p = 0.004), and the Vuong test supported the use of the ZIP model over a standard Poisson model. Model comparison indicated superior fit for the ZIP model (akaike information criterion [AIC] = 5230.959 vs. 27061.367 for Poisson), effectively accounting for zero-inflation.

Conclusion: The results suggest that higher humidity and temperature favor dengue transmission, while heavy rainfall may disrupt mosquito breeding, reducing cases. These findings provide a basis for targeted public health interventions.

Contribution: This study enhances understanding of DF-climate interactions in Kenya, supporting the application of ZIP modelling for improved disease surveillance and control strategies.