  A novel coronavirus disease has emerged (later named COVID-19) and caused the
world to enter a new reality, with many direct and indirect factors influencing
it. Some are human-controllable (e.g. interventional policies, mobility and the
vaccine); some are not (e.g. the weather). We have sought to test how a change
in these human-controllable factors might influence two measures: the number of
daily cases against economic impact. If applied at the right level and with
up-to-date data to measure, policymakers would be able to make targeted
interventions and measure their cost. This study aims to provide a predictive
analytics framework to model, predict and simulate COVID-19 propagation and the
socio-economic impact of interventions intended to reduce the spread of the
disease such as policy and/or vaccine. It allows policymakers, government
representatives and business leaders to make better-informed decisions about
the potential effect of various interventions with forward-looking views via
scenario planning. We have leveraged a recently launched open-source COVID-19
big data platform and used published research to find potentially relevant
variables (features) and leveraged in-depth data quality checks and analytics
for feature selection and predictions. An advanced machine learning pipeline
has been developed armed with a self-evolving model, deployed on a modern
machine learning architecture. It has high accuracy for trend prediction
(back-tested with r-squared) and is augmented with interpretability for deeper
insights.
