  Modern Bayesian approaches and workflows emphasize in how simulation is
important in the context of model developing. Simulation can help researchers
understand how the model behaves in a controlled setting and can be used to
stress the model in different ways before it is exposed to any real data. This
improved understanding could be beneficial in epidemiological models, specially
when dealing with COVID-19. Unfortunately, few researchers perform any
simulations. We present a simulation algorithm that implements a simple
agent-based model for disease transmission that works with a standard
compartment epidemiological model for COVID-19. Our algorithm can be applied in
different parameterizations to reflect several plausible epidemic scenarios.
Additionally, we also model how social media information in the form of daily
symptom mentions can be incorporate into COVID-19 epidemiological models. We
test our social media COVID-19 model with two experiments. The first using
simulated data from our agent-based simulation algorithm and the second with
real data using a machine learning tweet classifier to identify tweets that
mention symptoms from noise. Our results shows how a COVID-19 model can be (1)
used to incorporate social media data and (2) assessed and evaluated with
simulated and real data.
