Time-series infectious disease data analysis using SVM and genetic algorithm

Dengue represents a serious health threat in the Tropics, owing to the year-round presence of Aedes mosquito vectors, and the lack of any anti-viral drugs or vaccines. Climatic factors are important in influencing the incidence of dengue. It is important to determine the relationships between climatic factors and disease incidence trends, which would be helpful for relevant environment and health agencies in planning appropriate pre-emptive control measures. Climatic factors and dengue case records vary over time. It is therefore difficult to justify the time-lag when a climatic factor affects the mosquito-to-human and human-to-mosquito loops. In this paper, we propose to use support vector machine (SVM) classifiers for analyzing the time- series dengue data and genetic algorithm (GA), to determine the time-lags and subset of climatic factors as effective factors influencing the spread of dengue. It is shown that the proposed model is able to detect important climatic factors and their time-lags which affect the disease, and the GA-based SVM classifiers could improve the classification accuracy significantly.