  During the COVID-19 pandemic of 2019/2020, authorities have used temporary
ad-hoc policy measures, such as lockdowns and mass quarantines, to slow its
transmission. However, the consequences of widespread use of these
unprecedented measures are poorly understood. To contribute to the
understanding of the economic and human consequences of such policy measures,
we therefore construct a mathematical model of an economy under the impact of a
pandemic, select parameter values to represent the global economy under the
impact of COVID-19, and perform numerical experiments by simulating a large
number of possible policy responses. By varying the starting date of the policy
intervention in the simulated scenarios, we find that the most effective policy
intervention occurs around the time when the number of active infections is
growing at its highest rate -- that is, the results suggest that the most
severe measures should only be implemented when the disease is sufficiently
spread. The intensity of the intervention, above a certain threshold, does not
appear to have a great impact on the outcomes in our simulations, due to the
strongly concave relationship that we identify between production shortfall and
infection rate reductions. Our experiments further suggest that the
intervention should last until after the peak established by the reduced
infection rate, which implies that stricter policies should last longer. The
model and its implementation, along with the general insights from our policy
experiments, may help policymakers design effective emergency policy responses
in the face of a serious pandemic, and contribute to our understanding of the
relationship between the economic growth and the spread of infectious diseases.
