Simulation of the geographical spread of COVID-19 disease based on mobile cell data: animated dispersal of undiagnosed infected and modelling the mobility behaviour under displacement restrictions
Description
By applying archive mobile cell data, we can visualise how real mobility patterns can contribute to the geographical spread of COVID-19 infection within a particular geographic space. The spatial component of airborne pathogens’ diffusion is limitedly analysed due to its excessive data demand. Professionals in the field of computational biology, epidemiology, signal processing, network science and especially spatially-focused social sciences can benefit from the visualisation of spatio-temporal mobility data. We presented a data procession method that can be useful for analysing and visualising raw mobile cell data, to provide easily comprehensible insights on mobility behaviour. By proper parameter setting, the model can provide an analytical framework for the investigation of geographic spreading of any kind of infections, which is conceived as a location network. Visualising mobility behaviour enables to quickly identify infection hubs, directions and volumes of spread and areas at risk, along with the spatio-temporal dynamics. Geographical component of mobile cellular data is registered, collected and processed all over the world, using similar technologies for data retrieval, which makes the analysis replicable.