  The COVID-19 pandemic left its unique mark on the 21st century as one of the
most significant disasters in history, triggering governments all over the
world to respond with a wide range of interventions. However, these
restrictions come with a substantial price tag. It is crucial for governments
to form anti-virus strategies that balance the trade-off between protecting
public health and minimizing the economic cost. This work proposes a
probabilistic programming method to quantify the efficiency of major
non-pharmaceutical interventions. We present a generative simulation model that
accounts for the economic and human capital cost of adopting such strategies,
and provide an end-to-end pipeline to simulate the virus spread and the
incurred loss of various policy combinations. By investigating the national
response in 10 countries covering four continents, we found that social
distancing coupled with contact tracing is the most successful policy, reducing
the virus transmission rate by 96\% along with a 98\% reduction in economic and
human capital loss. Together with experimental results, we open-sourced a
framework to test the efficacy of each policy combination.
