PRACRITI

PRACRITI predicts COVID-19 transmission dynamics at granular geographical scales using an adaptive, interacting, cluster-based mathematical modeling approach to estimate incidence, effective reproduction number (R_t), and asymptomatic/undetected infections.

Key Features:

• Adaptive Modeling: PRACRITI employs an adaptive framework that adjusts to spatial variations in the effective reproduction number (R_t).
• Interacting Cluster-Based Modeling: The tool uses interacting cluster-based mathematical models to analyze transmission across administrative units including districts and states.
• Granular Predictions and Forecasting: It produces district- and state-level incidence forecasts and R_t estimates, including three-week case projections, tested on incidence data from Italy, the United States, and India.
• Estimation of Asymptomatic and Undetected Cases: PRACRITI estimates asymptomatic and undetected infections to account for hidden transmission in incidence estimates.

Scientific Applications:

• Resource Allocation: Forecasting potential hotspots and case numbers to inform allocation of hospital beds, ventilators, and healthcare personnel.
• Control Strategy Design: Enabling design of targeted interventions such as lockdowns, testing campaigns, and vaccination drives based on regional transmission trajectories.
• Public Health Planning: Supporting preparation for subsequent waves and long-term public health planning through scenario-based forecasts.

Methodology:

Adaptive, interacting, cluster-based mathematical modeling with estimation of effective reproduction number (R_t) and asymptomatic/undetected cases applied to incidence data from Italy, the United States, and India to generate district- and state-level forecasts including three-week case projections.