Modelling strong control measures for epidemic propagation with networks -- A COVID-19 case study

Small, Michael; Cavanagh, David

We show that precise knowledge of epidemic transmission parameters is not required to build an informative model of the spread of disease. We propose a detailed model of the topology of the contact network under various control regimes and demonstrate that this is sufficient to capture the salient dynamical characteristics and to inform decisions. Our model of city-level transmission of an infectious agent (SEIR model) characterises spread via a (a) scale-free contact network (no control); (b) a random graph (elimination of mass gatherings; and (c) small world lattice (partial to full lockdown --"social"distancing). This model exhibits good agreement between simulation and data from the 2020 pandemic spread of COVID-19. Also, estimates of the relevant rate parameters of the SEIR model are obtained and we demonstrate the robustness of our model predictions under uncertainty of those estimates. Finally, the social context and utility of this work is identified.