Forecasting COVID-19 Infections with the Use of Simple Cellular Automata

The outbreak of the COVID-19 pandemic led policy makers and public health officials around the world to implement non-pharmaceutical interventions to suppress the spread of the virus. The goal was to reduce human mobility and social contacts, considered as the main factors of virus diffusion. However, these containment measures needed to be revised on a frequent basis to avoid serious economic and social costs. Moreover, spatial disparities needed to be taken into consideration, since the behavior of the virus was different according to the geographical context. Therefore, a frequent update on the short-term forecasts of the epidemic course, which considered spatial heterogeneities, was crucial for planning appropriate mitigation strategies. In this article, we present a simple epidemiological model based on Cellular Automata, that takes into account human mobility and produces short-term forecasts of daily virus infections. Cellular Automata allow the discretization of time and space and thus, the spatio-temporal dynamics of the disease can be explored at the desired scale. We apply our model on real daily infection and mobility data from Spain and show that it is reliable in predicting the short-term daily infections trajectory both at the country level and at the regional level of Autonomous Communities. Furthermore, compared against four state-of-the-art methods, the proposed method achieves comparable forecasting performance with significantly lower computational resources.