Rapid Assessment of Mosquito Larvae Distribution in Three Micro Habitats in Port Harcourt Metropolis Using Geospatial Methods

The technique of Geographic Information System (GIS) was used in the rapid assessment of the abundance and distribution of mosquito larvae in three micro habitats (stagnant drainage, transient puddles and transient water in tyre) in Port-Harcourt metropolis in 2018. Mosquito larvae were collected over a period of four months in 7 zones (21 stations), reared to adult stage; identified up to species level and their abundance recorded. A total of 830 mosquito larvae were caught, belonging to three genera and five species. A GIS mapping showed a spatial variation in the abundance of the five species of mosquito which varied significantly, Culex quinquefasciatus had the highest mean abundance (15.5), followed by Anopheles gambiae (10.3), Aedes aegypti (7.5), Aedes albopictus (4.42) and Culex tigripes (2.41). Mosquito larvae species abundance was highest in stagnant drainage (51%), followed by transient puddles (30%) and then transient water in tyre (19%). All the mosquito genera have the capacity to occur as the only species within the microhabitats chosen for this study even as the microhabitats are in different landuse classes. Culex and the Anopheles genera occurred together in combinations as dominants and co-dominants within two (stagnant drainage and transient puddles) out of the three microhabitats. Aedes genus occurred insignificantly in combination with Anopheles only in the water-in-tyre microhabitat and with Culex and Anopheles in transient puddle microhabitat.

A GIS modeling based on maximum Anopheles mosquito flight distance (9km) was used to develop a surface hazard model of mosquito infestation. Spatial results produced individual hazard assessments from different cardinal points. Combined results of the individual hazard assessments confirmed a composite multi-hazard risk assessment where the distribution of risk was not based on the environmental attributes of the land use class. Provided the potential capacity to predict the health vulnerability of the population within each composite hazard class. This study offer insight on the promising nature of GIS-based models in a data scarce environment to help in a meaningful way extrapolate evidence-based mitigation planning and resource allocation in mosquito control programmes.