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.

翻译：为应对COVID-19大流行及新型病毒引发未来疫情的可能威胁，我们开发了一个灵活的疾病干预效果建模框架。该工具旨在帮助各级决策者比较针对新兴流行病威胁的可能应对措施，以实现最优控制并减少危害。该框架专门设计为可扩展且模块化，能够模拟不同人口规模、病毒类型、检测方法与策略（包括混合检测）以及干预策略。本文概述了这一框架，并探讨了不同干预策略及其对感染动态的影响。