Two Distinct Dynamic Process Models of COVID-19 Spread with Divergent Vaccination Outcomes

Kinematic models of contagion-based viral transmission describe patterns of events over time (e.g., new infections), relying typically on systems of differential equations to reproduce those patterns. By contrast, agent-based models of viral transmission seek to relate those events or patterns of events to causes, expressed in terms of factors (parameters) that determine the dynamics that give rise to those events.

This paper is concerned with the dynamics of contagion-based spread of infection. Dynamics that reflect time homogeneous vs inhomogeneous transmission rates are generated via an agent-based infectious disease modeling tool (CovidSIMVL - <ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://github.com/ecsendmail/MultiverseContagion">github.com/ecsendmail/MultiverseContagion</ext-link> ). These different dynamics are treated as causal factors and are related to differences in vaccine efficacy in an array of simulated vaccination trials. Visualizations of simulated trials and associated metrics illustrate graphically some cogent reasons for not effectively hard-coding assumptions of dynamic temporal homogeneity, which come ‘pre-packaged’ with the mass action incidence assumption that underpins typical equation-based models of infection spread.