SICO: Simulation for Infection Control Operations

TL;DR

This paper introduces SICO, a flexible, scalable simulation framework designed to model disease interventions and assess their effectiveness in controlling infections like COVID-19, aiding decision makers.

Contribution

The paper presents a novel modular simulation framework that models various epidemiological scenarios and intervention strategies, including pooled testing, for better epidemic response planning.

Findings

Different intervention strategies significantly impact infection dynamics.

The framework can model multiple viruses and testing methods.

Scalability allows application at various population levels.

Abstract

In response to the COVID-19 pandemic and the potential threat of future epidemics caused by novel viruses, we developed a flexible framework for modeling disease intervention effects. This tool is intended to aid decision makers at multiple levels as they compare possible responses to emerging epidemiological threats for optimal control and reduction of harm. The framework is specifically designed to be both scalable and modular, allowing it to model a variety of population levels, viruses, testing methods and strategies--including pooled testing--and intervention strategies. In this paper, we provide an overview of this framework and examine the impact of different intervention strategies and their impact on infection dynamics.