  Pandemics, in addition to affecting the health of populations, can have huge
impacts on their social and economic behavior. These factors, on the other
hand, have the potential to feed back to and influence the disease spreading.
It is important to systematically study these interrelations, to determine
which ones have significant effects, and whether the effects are adverse or
beneficial. Our recently developed epidemic model with agent-based and
geographical elements is used in this study for such a purpose. We perform an
extensive parameter space exploration of the socio-economic part of the model,
including factors like the attitudes (called values) of the agents towards the
disease spreading, health, economic situation, and regulations by government
agents. We search for prominent patterns from the resulting simulated data
using basic classification tools, namely self-organizing maps and principal
component analysis. We seek to isolate the most important value parameters of
the population and government agents influencing the disease spreading speed
and patterns, and monitor different quantities of the model output, such as
infection rates, the propagation speed of the epidemic, economic activity,
government regulations, and the compliance of population. Out of these, the
ones describing the epidemic spreading were resulting in the most distinctive
clustering of the data, and they were selected as the basis of the remaining
analysis. We relate the found clusters to three distinct types of disease
spreading: wave-like, chaotic, and transitional spreading patterns. The most
important value parameter contributing to phase changes between these phases
was found to be the compliance of the population agents towards the government
regulations.
