Complex Network based Modelling and Analysis of COVID-19 pandemic cases in Balochistan

Complex networks are widely applied in many research fields for modeling the complex relationships found within real-world observations. Such networks find various potential applications in biological, social, technological, and chemical sciences, among many others. The subject of complex networks is especially very important in modeling epidemic diseases to study their dynamics. The COVID-19 pandemic has left deep marks on humankind globally, including quite extensive effects in Pakistan. Since the outbreak of COVID-19 cases in Pakistan, efforts have been made to analyze patterns of disease diffusion. Following this, in this study, we examine the diffusion of COVID-19 cases in Pakistan's Balochistan province. We introduce a network model with location-location associations, which acts as an intermediary for disease diffusion. In this model, weeks and locations are nodes, and the heterogeneous weighted links are used to show the frequency of confirmed cases. The dataset of COVID-19 cases is modeled and analyzed, taking the confirmed cases from every location and forming a weighted two-mode network. We perform an analysis of this two-mode network based on global and local measures to understand its structure from the point of view of Balochistan. In addition, we project the two-mode network based on well-known projection techniques. The findings indicate that the transmission of COVID-19 in Balochistan has adversely affected some localities, which have a high number of confirmed cases.