  Epidemiological models with constant parameters may not capture satisfactory
infection patterns in the presence of pharmaceutical and non-pharmaceutical
mitigation measures during a pandemic, since infectiousness is a function of
time. In this paper, an Epidemiology-Informed Neural Network algorithm is
introduced to learn the time-varying transmission rate for the COVID-19
pandemic in the presence of various mitigation scenarios. There are
asymptomatic infectives, mostly unreported, and the proposed algorithm learns
the proportion of the total infective individuals that are asymptomatic
infectives. Using cumulative and daily reported cases of the symptomatic
infectives, we simulate the impact of non-pharmaceutical mitigation measures
such as early detection of infectives, contact tracing, and social distancing
on the basic reproduction number. We demonstrate the effectiveness of
vaccination on the transmission of COVID-19. The accuracy of the proposed
algorithm is demonstrated using error metrics in the data-driven simulation for
COVID-19 data of Italy, South Korea, the United Kingdom, and the United States.
