  Data-driven risk networks describe many complex system dynamics arising in
fields such as epidemiology and ecology. They lack explicit dynamics and have
multiple sources of cost, both of which are beyond the current scope of
traditional control theory. We construct the global risk network by combining
the consensus of experts from the World Economic Forum with risk activation
data to define its topology and interactions. Many of these risks, including
extreme weather, pose significant economic costs when active. We introduce a
method for converting network interaction data into continuous dynamics to
which we apply optimal control. We contribute the first method for constructing
and controlling risk network dynamics based on empirically collected data. We
identify seven risks commonly used by governments to control COVID-19 spread
and show that many alternative driver risk sets exist with potentially lower
cost of control.
