EpiControl

EpiControl fits semi-mechanistic models to routine data and leverages feedback-control and model-based learning to balance complex policy-simulation with real-time responsiveness for optimizing epidemic interventions and automating scenario planning.

Key Features:

• Semi-mechanistic model fitting: Fits semi-mechanistic models to routine data for policy support.
• Feedback-control: Leverages feedback-control to balance complex policy-simulation with real-time responsiveness.
• Model-based learning: Incorporates model-based learning for adaptive scenario generation.
• Automatic scenario updating: Generates intervention scenarios that automatically update with unfolding dynamics.
• Cost minimization: Minimizes costs while meeting user-defined policy targets.
• Robustness to uncertainty: Remains robust to unanticipated changes in epidemic dynamics.
• Policy target specification: Allows users to define policy targets such as suppressing epidemic peaks.

Scientific Applications:

• Epidemic intervention optimization: Discovers optimal intervention policies for outbreak control.
• Hospital overload prevention: Rapidly discovers policies that prevent hospital overload.
• Societal disruption reduction: Reduces societal disruption through optimized interventions.
• Real-time outbreak response: Supports real-time response to unfolding epidemic dynamics.
• Ebola virus disease: Demonstrated capability for Ebola virus outbreak intervention planning.
• COVID-19: Demonstrated capability for COVID-19 intervention policy optimization.
• Immunity uncertainty management: Retains control despite uncertain immunity dynamics.
• Vaccination dynamics: Accounts for uncertain vaccination dynamics in policy optimization.
• Pathogen variant dynamics: Remains robust to uncertain pathogen variant dynamics.

Methodology:

EpiControl is implemented in R and fits semi-mechanistic models to routine data, leveraging feedback-control and model-based learning to generate intervention scenarios.