Spatiotemporal trends in Anopheles funestus breeding habitats

Abstract

Effective identification and control of malaria vector larval breeding habitats are crucial for the management and eradication of malaria. Despite its importance, the last decade has seen a decline in data availability and intervention efforts due to reduced attention and prioritization. This study addresses the geographic data scarcity concerning Anopheles funestus larval breeding habitats in a malaria-prone region of western Kenya. Employing a two-step methodological approach, we integrated multi-criteria decision analysis (MCDA) and rule-based fuzzy logic analysis to evaluate the spatiotemporal similarity or divergence of these habitats. The analysis spanned a five-year interval, 2008, 2013, and 2018 with 2013 serving as the base year for both hindcast and forecast predictions. The MCDA utilized categorical land use/land cover (LULC) and edaphic variables to identify potential breeding habitats, while climatic and topographic variables and spectral indices were analysed using fuzzy logic to assess the similarity or divergence of these habitats over time. Validation of the MCDA and fuzzy logic models was performed using a flight buffer distance based on adult An. funestus presence points (n = 136), supplemented by a limited number of larval breeding locations (n = 12) respectively. Our findings identified 147 potential An. funestus larval breeding habitats across the study area. The fuzzy logic analysis predicted a high degree of similarity (85.03%) in potential breeding habitats between the study years compared to the base year, with a divergence of 14.97%. This study demonstrates the feasibility of using semi-automated methods to detect both permanent and impermanent An. funestus breeding habitats under conditions of limited data. The methodologies developed provide a timely, cost-effective tool for enhanced surveillance and management of An funestus mosquito larval breeding, offering valuable insights for stakeholders involved in malaria vector monitoring and control.

Keywords

• Anopheles
• Earth observation
• Larval breeding
• Larval source management
• Modelling
• Remote sensing