The Importance of Scaling for an Agent Based Model: An Illustrative Case Study with COVID-19 in Zimbabwe

Abstract

Agent-based models frequently make use of scaling techniques to render the simulated samples of population more tractable. The degree to which this scaling has implications for model forecasts, however, has yet to be explored; in particular, no research on the spatial implications of this has been done. This work presents a simulation of the spread of Covid-19 among districts in Zimbabwe and assesses the extent to which results vary relative to the samples upon which they are based. It is determined that in particular, different geographical dynamics of the spread of disease are associated with varying population sizes, with implications for others seeking to use scaled populations in their research.