Using Agent-Based Models in Contagion Modelling
Traditionally, predictive models cannot be created very quickly impacting how efficiently governments, local authorities and businesses can make data-driven decisions. Coronavirus (COVID-19) is affecting thousands of people and understanding its spread is an extremely complex scientific and engineering problem that must be addressed so that governments can take necessary steps as quickly as possible. We have created an agent-based model of COVID-19 that demonstrates the risk of infection and transmission across the UK. Over the course of three days, we’ve designed the initial agent-based model involving 100,000 agents (people) but could be scaled to mimic the entire population if required. Currently, we don’t know the R0 number for COVID-19, and in the context of complex, large-scale simulations, small errors become dramatically magnified. If one part of the framework is misjudged or data is input incorrectly, the repercussions can be significant – especially if decisions are made based upon the inaccurate output. Indeed, as it stands data sources for such an agent-based model are diverse and create a complex picture, including virus genomes, crowdsourced data and social data. One way around this is to create models which don’t rely on these inputs. The Aether Engine simulation focusses on movement patterns to try and isolate specific scenarios. Focussing on two different hubs – Liverpool and London – the simulation shows how the pathogen might spread from different locations along the UK road network.