An Integrated Epidemic Simulation Workflow for Submodular Intervention Strategies

TL;DR

This paper introduces an integrated workflow combining Covasim and PREEMPT tools to simulate COVID-19 spread and evaluate network-based vaccination strategies using submodular optimization.

Contribution

The work presents a novel integrated simulation workflow that combines agent-based and network-based models for epidemic intervention analysis.

Findings

Preliminary results demonstrate effective simulation of COVID-19 spread.

Network-based vaccination strategies outperform random approaches.

The workflow enables testing of various intervention schemes.

Abstract

Owing to the ongoing COVID-19 pandemic and other recent global epidemics, epidemic simulation frameworks are gaining rapid significance. In this work, we present a workflow that will allow researchers to simulate the spread of an infectious disease under different intervention schemes. Our workflow is built using the Covasim simulator for COVID-19 alongside a network-based PREEMPT tool for vaccination. The Covasim simulator is a stochastic agent-based simulator with the capacity to test the efficacy of different intervention schemes. PREEMPT is a graph-theoretic approach that models epidemic intervention on a network using submodular optimization. By integrating the PREEMPT tool with the Covasim simulator, users will be able to test network diffusion based interventions for vaccination. The paper presents a description of this integrated workflow alongside preliminary results of our empirical evaluation for COVID-19.