Modelling of potential risk areas of pertussis cases in the Philippines using bioclimatic envelopes.

OBJECTIVES

Pertussis remains a pressing public health concern in the Philippines despite being vaccine-preventable. The resurgence of the disease, driven by waning immunity, vaccine hesitancy and erratic outbreaks, underscores the need for innovative surveillance methods. Thus, this study intends to create a predictive model of potential risk areas for pertussis outbreaks in the Philippines using bioclimatic variables.

METHODS

This study employs the maximum entropy algorithm to predict pertussis risk areas in the Philippines based on 19 bioclimatic variables. The occurrence data of reported pertussis cases were obtained from two sources: the National Disaster Risk Reduction and Management Council, covering 30 March-11 June 2024, and the Weekly Epidemiological Surveillance Report, spanning 1 January-12 October 2024.

RESULTS

Key variables such as Mean Temperature of the Wettest Quarter (BIO8), Precipitation of the Wettest Quarter (BIO16) and Precipitation of the Driest Quarter (BIO17) were identified as significant predictors. Results revealed high-risk areas concentrated in northern Luzon, particularly Central Luzon (Region III), the major coastal areas of Ilocos (Region I), National Capital Region, MIMAROPA (Region IV-B), and an isolated area in Bicol (Region V). The model performance indicates excellent predictive accuracy (AUC = 0.972).

CONCLUSION

The findings highlight how climatic factors shape pertussis distribution through anthropogenic means (e.g., higher humidity increases the chances of acquiring respiratory problems), providing a framework for eco-epidemiological risk assessment. This approach enhances targeted intervention planning, resource allocation, and early warning systems, particularly in resource-limited settings like the Philippines. The study underscores the role of Maximum Entropy in addressing re-emerging diseases, contributing to sustainable public health preparedness and mitigation strategies in tropical regions given that there is currently no predictive model for pertussis cases in the Philippines.