COVID-19 Scenarios

COVID-19 Scenarios simulates the spread of SARS-CoV-2 using an age-structured SEIR model with additional compartments for severe and critical cases to project healthcare demand and intervention effects.


Key Features:

  • Age-Structured Modeling: Implements age-specific parameters to model differential susceptibility, severity, and outcomes across demographic groups.
  • Severe and Critical Compartments: Extends the SEIR framework with separate compartments for severely and critically ill patients to estimate healthcare burden.
  • Infection Control and Mitigation Parameters: Allows specification of measures that reduce transmission rates and age-group specific isolation protocols.
  • Customizable Simulations: Supports adjustment of a wide range of epidemiological parameters to explore alternative scenarios and hypotheses.
  • Client-Side Execution and Data Export: Runs simulations client-side and provides export of parameter settings and simulation results for further analysis.

Scientific Applications:

  • Healthcare Demand Projection: Projects hospital and critical care needs by incorporating severe and critical case compartments.
  • Intervention Assessment: Evaluates the impact of transmission-reducing measures and age-specific isolation on epidemic trajectories.
  • Demographic-Targeted Analysis: Quantifies age-specific outcomes to inform targeted public health responses.

Methodology:

Uses an age-structured SEIR epidemiological model augmented with compartments for severe and critical cases, with adjustable transmission rates and age-specific isolation measures; simulations execute on the client side.

Topics

Collections

Details

License:
MIT
Tool Type:
web application
Programming Languages:
JavaScript, Python
Added:
1/18/2021
Last Updated:
2/17/2021

Operations

Publications

Noll NB, Aksamentov I, Druelle V, Badenhorst A, Ronzani B, Jefferies G, Albert J, Neher RA. COVID-19 Scenarios: an interactive tool to explore the spread and associated morbidity and mortality of SARS-CoV-2. Unknown Journal. 2020. doi:10.1101/2020.05.05.20091363.

Links