Long-term spatial and population-structured planning of non-pharmaceutical interventions to epidemic outbreaks

In this paper, we consider the problem of planning non-pharmaceutical interventions to control the spread of infectious diseases. We propose a new model derived from classical compartmental models; however, we model spatial and population-structure heterogeneity of population mixing. The resulting model is a large-scale non-linear and non-convex optimisation problem. In order to solve it, we apply a special variant of covariance matrix adaptation evolution strategy. We show that results obtained for three different objectives are better than natural heuristics and, moreover, that the introduction of an individual’s mobility to the model is significant for the quality of the decisions. We apply our approach to a six-compartmental model with detailed Poland and COVID-19 disease data. The obtained results are non-trivialand sometimes unexpected; therefore, we believe that our model could be applied to support policy-makers in fighting diseases at the long-term decision-making level. [Display omitted] •The application of mobility and spatial epidemiology models allows for long-term optimization of decisions diversified within the country.•Non-linear and non-convex approach enables the optimization of NPI decisions that differ in terms of spatial distribution and population structure.•Higher granularity of the model allows for better decisions but brings a computational burden; we propose the covariance matrix adaptation evolution strategy to solve a large-scale model with a detailed spread disease model and aggregated.•NPIs have wide-ranging effects on the social well-being of populations, and the proposed model and algorithm can support policy-makers in making NPI policy plans.