Accounting for about 290,000 to 650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modelling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on Spatial On Line Analytical Processing (S-OLAP) technology. Although the S-OLAP technology is useful in analysing multidimensional spatial datasets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this paper is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this paper, a well-known multicriteria classification method, the Dominance-based Rough Set Approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of the analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.
- Seasonal influenza
- Risk assessment
- Dominance-based Rough Set Approach
- Multicriteria classification