Dengue fever disease had threatens more than 4 billion people worldwide. Current studies in this disease focus on the relation between environmental data and dengue cases directly without considering the stage of the mosquito's life. In fact, dengue case is associated with different dominant stages under different circumstances. Therefore, it is important to analyze and select the dominant stage of mosquito for different situation to select an adequate strategy for disease-spreading prevention. In this research, we introduce the new system to select the stage based on context-dependency by using Mathematical Model of Meaning (MMM).The feature of this research is in calculating the similarity pattern analysis between environmental data with disease stage. The objective of the research is to build effective prevention system due to dominant mosquito's stage that possible to trigger dengue case. In this study, we focus on abiotic contexts with features such as rainfall, temperature, humidity, sunshine duration, CO2 and wind speed. This model consist of (a) subspace creation based on stage classification from ecological model of dynamic energy budget, (b) context-similarity calculation by using MMM, and (c) matching-prediction by Hilbert Transform. Through this system, we can determine an effective strategy to prevent dengue case in every different situation. Therefore, this system can contribute to reduce environmental damage and probability of health problem caused by an improper strategy of disease prevention. In this research, we employ real weather data of Surabaya from 2007 to 2011. The result shows that the dominant stage in wet season and dry season is different, also in specific case such as disaster, the dominant stage also different.