OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• The COVID-19 pandemic necessitates robust tools for predicting disease spread and evaluating public health interventions.
• Agent-based models (ABMs) offer a powerful approach for simulating complex epidemic dynamics.

Purpose of the Study:

• To introduce OpenABM-Covid19, an open-source agent-based model for simulating the COVID-19 epidemic.
• To provide a flexible and scalable platform for assessing non-pharmaceutical interventions and vaccination strategies.

Main Methods:

• Development of an agent-based simulation incorporating detailed age-stratification and realistic social networks.
• Parameterization for UK demographics and calibration to the UK epidemic, with adaptability for other countries.
• Implementation of Python and R interfaces for user accessibility and integration into policy workflows.

Main Results:

• The model can simulate populations of up to 1 million individuals efficiently, enabling rapid parameter sweeps and statistical inference.
• OpenABM-Covid19 facilitates the evaluation of various non-pharmaceutical interventions, including manual/digital contact tracing and vaccination programs.
• The open-source nature and emphasis on testing, documentation, and transparency ensure model reliability and broad usability.

Conclusions:

• OpenABM-Covid19 provides a valuable computational resource for scientists and policymakers to simulate and compare intervention strategies for COVID-19 suppression.
• The model's flexibility, scalability, and accessibility through programming interfaces support dynamic decision-making during the ongoing epidemic.