The toolset comprises a disease progression simulator and several machine learning (ML) models to test the impact of various interventions.
First, the ML models help bootstrap the system by estimating the disease progression and comparing the outcomes to historical data. Next, researchers can run the simulator with the help of learned parameters to play out what-if scenarios for various interventions.
The team behind the project said that their open-source code simulates COVID-19 case projections at various regional granularity levels.
The team added that “The output is the projection of the total confirmed cases over a specific timeline for a target state or a country, for a given degree of intervention.”
Further, the solution generates the projections of daily and cumulative confirmed cases, starting from the beginning of the outbreak uptil a specified length of time in the future.