  The COVID-19 pandemic has challenged scientists and policy-makers
internationally to develop novel approaches to public health policy.
Furthermore, it has also been observed that the prevalence and spread of
COVID-19 vary across different spatial, temporal, and demographics. Despite
ramping up testing, we still are not at the required level in most parts of the
globe. Therefore, we utilize self-reported symptoms survey data to understand
trends in the spread of COVID-19. The aim of this study is to segment
populations that are highly susceptible. In order to understand such
populations, we perform exploratory data analysis, outbreak prediction, and
time-series forecasting using public health and policy datasets. From our
studies, we try to predict the likely % of the population that tested positive
for COVID-19 based on self-reported symptoms. Our findings reaffirm the
predictive value of symptoms, such as anosmia and ageusia. And we forecast that
% of the population having COVID-19-like illness (CLI) and those tested
positive as 0.15% and 1.14% absolute error respectively. These findings could
help aid faster development of the public health policy, particularly in areas
with low levels of testing and having a greater reliance on self-reported
symptoms. Our analysis sheds light on identifying clinical attributes of
interest across different demographics. We also provide insights into the
effects of various policy enactments on COVID-19 prevalence.
