Modelling and monitoring house fly M. domestica using remote sensing data and geographic information system

Recent advances in remote sensing (RS) techniques and geographic information system (GIS) analyses have enhanced field studies of environmental factors affecting the spatial distribution of vector-borne diseases. These techniques have been used previously to map and monitor the fly vector in Egypt. Using RS and GIS were applied in relation to contextual environmental factors, including land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and house fly density to model and predict the house fly density in a given location. Results found a strong correlation between the house fly density and Wetness (r = 0.98), NDWI35 (r = −0.98) and LST (r = 0.90). These three variables with the NDVI index were found forming the best accurate model to predict the house fly density with the highest coefficient of determination (r2 = 0.97) and lowest Variance Inflation Factor (VIF = 5.67) and Akaike Information Criterion (AIC = 7.95) among other tested models. This study suggests that future studies should include other factors related to vector abundance, vector competence, and human population to produce more comprehensive risk maps, thus helping in planning effective prevention and control strategies, also these studies have emphasized the importance of community efforts toward fly control.