Disease spread analysis in dynamic networks

Due to the rapid escalation of the Covid-19 disease to pandemic levels, all attention has turned to the field of epidemiology. Traditional SIR-based compartmental prediction models frequently used in epidemiology have certain limitations. One of these limitations is the assumption of homogeneity within the community, where all individuals are assumed to have the same characteristics. In this study, alternative methods that could be used instead of traditional approaches to gain insights into disease transmission in communities are discussed. In real life, communities have a heterogeneous structure, and individuals possess different attributes. Therefore, the first step was to obtain fundamental centrality values for individuals. As a different approach, the Louvain method was used to identify sub-communities within the larger community, and the fundamental centrality values of individuals were calculated accordingly. Lastly, the Node2Vec method was employed to extract attribute features for individuals. The obtained data were input into the TOPSIS method, a multi-criteria decision support technique. The TOPSIS method was utilized to accommodate changes such as the addition or modification of new attributes related to individuals. The selection of infected individuals was done randomly, while immune individuals were selected based on the rankings obtained from the TOPSIS results. After the selection of infected and immune individuals, the spread of the disease in the network was monitored, and the total number of infected individuals resulting from the simulation was calculated. The values of infected individuals obtained from simulations for all examples were compared and analyzed. As a result, the combination of the Louvain and TOPSIS methods yielded the best results for two datasets created based on human-human interactions. In the other dataset created based on friendship relationships on social media, the degree centrality method was successful. It is believed that this study will contribute to the Turkish literature in the relevant field and provide decision-makers and implementers with assistance in individual selection for preventive activities aimed at slowing down or stopping the spread of the disease, such as isolation and vaccination, by utilizing limited resources in the most effective way possible.