Distributed Identification of a Network Model for Pandemic Spreading

The recent outburst of the Covid-19 epidemic has made clear the necessity of developing dynamical models for the prediction and the control of large scale epidemic systems. Due to the large impact they have on the life of people and on the economy of the countries, it is extremely important to design models that are able to predict the evolution of such complex phenomena. In the thesis a compartmental model for epidemics is developed and implemented in a Python toolbox suited for distributed computation. Based on the proposed model and on available data, an identification algorithm, based on a gradient free descent method, is proposed to find model parameters that best fit the data. The distributed nature of the system allows for the implementation of a scheme in which computation is distributed among different spatial regions.