Considerable interest exists in confirming that meteorological variables may play determinant roles in dengue vector abundance. The principle vector for dengue is 'Aedes aegypti'. Dengue Fever has been considered the most important vector-borne viral disease in Jeddah, Saudi Arabia, and is susceptible to climate variability. The aim of this study is to describe the association between adult female 'Aedes aegypti' mosquitoes and meteorological variables and to develop models for predicting the mosquito abundance using Pearson's correlation and regression analyses. Our results show that mosquitoes have the highest correlation with temperature at lag 0 time and relative humidity at lag 5 weeks. The highest two correlations were found between the mosquitoes and minimum temperature (r=-0.57) and maximum relative humidity (r=0.46). Two models were created based on the regression analysis results. The first model shows that 86% of mosquito values were within the upper and lower limits of agreement. The second model shows that 94% of the values were within the limits of agreement. The study findings could contribute to the forecasting of mosquito abundance peaks and subsequently guide a plan for mosquito control operations ahead of time that would help to minimize the outbreak of dengue occurrence and prevent the spread of dengue infections.

Mosquito-borne diseases have spatial and temporal patterns, because mosquito density and longevity are based on a number of factors, such as temperature, precipitation and mosquito breeding habitats. Geographic Information System (GIS) and Remote Sensing (RS) and their related tools for mapping and modeling provide new and expanding opportunities for mosquito-borne diseases (MBD) research because they can display and model the temporal and spatial relationships between causes and diseases.