  Non-pharmaceutical interventions (NPIs) such as quarantine, self-isolation,
social distancing, and virus-contact tracing can greatly reduce the spread of
the virus during a pandemic. In the wave of the COVID-19 pandemic, many
countries have implemented various NPIs for infection control and mitigation.
However, the stringency of the NPIs and the resulting impact among different
countries remain unclear due to the lack of quantitative factors. In this study
we took a further step to incorporate the effect of the NPIs into the pandemic
dynamics model using the concept of policy intensity factor (PIF). This idea
enables us to characterize the transition rates as time varying quantities
instead of constant values, and thus capturing the dynamical behavior of the
basic reproduction number variation in the pandemic. By leveraging a great
amount of data reported by the governments and the World Health Organization,
we projected the dynamics of the pandemic for the major economies in the world,
including the numbers of infected, susceptible, and recovered cases, as well as
the pandemic durations. It is observed that the proposed variable-rate
susceptible-exposed-infected-recovered (VR-SEIR) model fits and projects the
pandemic dynamics very well. We further showed that the resulting PIFs
correlate with the stringency of NPIs, which allows us to project the final
affected numbers of people in those countries when their current NPIs have been
imposed for 90, 180, 360 days. It provides a quantitative insight into the
effectiveness of the implemented NPIs, and sheds a new light on minimizing both
affected people from COVID-19 and the economic impact.
