  Since the first recording of what we now call Covid-19 infection in Wuhan,
Hubei province, China on Dec 31, 2019, the disease has spread worldwide and met
with a wide variety of social distancing and quarantine policies. The
effectiveness of these responses is notoriously difficult to quantify as
individuals travel, violate policies deliberately or inadvertently, and infect
others without themselves being detected. In this paper, we attempt to
interpret and extrapolate from publicly available data using a mixed
first-principles epidemiological equations and data-driven neural network
model. Leveraging our neural network augmented model, we focus our analysis on
four locales: Wuhan, Italy, South Korea and the United States of America, and
compare the role played by the quarantine and isolation measures in each of
these countries in controlling the effective reproduction number $R_{t}$ of the
virus. Our results unequivocally indicate that the countries in which rapid
government interventions and strict public health measures for quarantine and
isolation were implemented were successful in halting the spread of infection
and prevent it from exploding exponentially. We test the predictive ability of
our model by matching predictions in the duration 3 March - 1 April 2020 for
Wuhan and in the duration 25 March - 1 April 2020 for Italy and South Korea. In
the case of the US, our model captures well the current infected curve growth
and predicts a halting of infection spread by 20 April 2020. We further
demonstrate that relaxing or reversing quarantine measures right now will lead
to an exponential explosion in the infected case count, thus nullifying the
role played by all measures implemented in the US since mid March 2020.
