Fuzzy C-Means and Social Network Analysis Combination for Better Understanding the Patient-based Spread of Dengue Fever with Climate and Geographic Factors

Climate and geography factors significantly influence the spread of dengue fever. It's critical to figure out the specifics of climatic and geographic conditions and the relationships between current patients. For further preventive measures, it is also necessary to identify the transmission source patients. This study aims to improve the understanding of dengue fever patients spreading under climate and geographic locations. Patients are clustered based on climatic and geographical variables, and influential patients are found in the established network using Fuzzy C-Means and Social Network Analysis. The scenario of cluster numbers' alteration and degree of fuzziness with Fuzzy C-Means made the three groups of patient clusters. A total of 52% of the patients are included in the lowland cluster, based on climate conditions and altitude. The patients grouped better with this approach than with the other compared methods. The Calinski Harabasz score has an average difference of 1644.105. The following relationship in the network is constructed once the cluster has been formed. It is given a fuzzy rule representing the distance of residence and the period of illness between patients. According to the Social Network Analysis approach applied to the region and month scenario, the three areas sensitive to the spreading center are Dau, Kepanjen, and Karangploso. The most significant patient data distribution occurred during the rainy season, peaking in January-February. The centrality algorithm shows that patients with male characteristics and the age range of children and adults could be the source of the disease's spread every month. Kepanjen area is the site of residence with the most significant impact with a proportion of 62.5 % in a year, and the initial illness is the fever related to dengue. Analysis results of this study can use by the Public Health Office to plan and manage resources to prevent extensive spread.