Spatiotemporally targeted lockdown as an alternative non-medical pandemic management measure – simulating a scattered accumulation as a result of localized closures

Real-time tracking of the spatial diffusion of contact-based diseases, especially COVID-19, is a crucial point of recent academic studies and policymaking. Mobility networks provide a useful approach to grasp the complex and interrelated nature of spatiotemporal movement and the geographical spread of infectious diseases. In this simulation, we aimed at disclosing the spatial component of infection carried over by interpersonal encounters. The mobile call detail record (CDR) dataset we applied here is especially suited to estimate the number of interpersonal encounters enabled by co-locating the same space with an infected person within a definite timeframe. Based on the dataset, we elaborated a real-time lockdown model which can substantiate the smart urban pandemic management that fits into the smart city concept. A simulation was elaborated to prove that our model is suitable to scatter crowdedness around a central place and decrease personal exposure to infection. As our raster-level simulation showed that a wider area around the target site could be denoted with higher contamination risk, neighbourhoods are advised to be the spatial scale of smart intervention, which contributes to keeping infection risks at a manageable level. However, static visualisation can hardly represent the spatiotemporal dynamics that is taken place in the compelling interaction of smart closure at a central site and enhancing crowdedness at the boundaries. To overcome this, we elaborated a raster-rendered dynamic model throughout the day. The model addresses issues of real-time mobility sensing, spatiotemporally targeted actions, responsiveness to community problems and local resilience