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Over the past two decades there has been a number of global outbreaks ofviral diseases. This has accelerated the efforts to model and forecast thedisease spreading, in order to find ways to confine the spreading regionallyand between regions. Towards this we have devised a model of geographicalspreading of viral infections due to human spatial mobility and adapted it tothe latest COVID-19 pandemic. In this the region to be modelled is overlaidwith a two-dimensional grid weighted with the population density defined cells,in each of which a compartmental SEIRS system of delay difference equationssimulate the local dynamics (microdynamics) of the disease. The infectionsbetween cells are stochastic and allow for the geographical spreading of thevirus over the two-dimensional space (macrodynamics). This approach allows toseparate the parameters related to the biological aspects of the disease fromthe ones that represent the spatial contagious behaviour through differentkinds of mobility of people acting as virus carriers. These provide sufficientinformation to trace the evolution of the pandemic in different situations. Inparticular we have applied this approach to three in many ways differentcountries, Mexico, Finland and Iceland and found that the model is capable ofreproducing and predicting the stochastic global path of the pandemic. Thisstudy sheds light on how the diverse cultural and socioeconomic aspects of acountry influence the evolution of the epidemics and also the efficacy ofsocial distancing and other confinement measures.

Modelling Covid-19 epidemic in Mexico, Finland and Iceland