Spatial modelling of virus transfer and exposure using Bayesian inference with Integrated Nested Laplace Approximation

Airborne transmission is a key element in the spread of viral contagion. To prevent this, health organization publish guidelines for every major disease outbreak. However, they are often based on researches carried out without access to modern solutions. In this paper, we propose usage of Bayesian inference as an additional, to computationally-intensive methods such as CFD or FEM, way to analyse short - range virus exposure. We build a spatial model, using INLA package, which allows us to optimize the complicated computational process and deal with conundrums of virus exposure modeling.