# Modeling, state estimation, and optimal control for the US COVID-19 outbreak

arXiv: https://arxiv.org/abs/2004.06291 · HTML: https://mlsanity.com/paper/2004.06291/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [A Comparison of Mesh-Free Differentiable Programming and Data-Driven Strategies for Optimal Control under PDE Constraints](https://mlsanity.com/paper/2310.02286/) (2023)
- [Containing a spread through sequential learning: to exploit or to explore?](https://mlsanity.com/paper/2303.00141/) (2023)
- [Adaptive Epidemic Forecasting and Community Risk Evaluation of COVID-19](https://mlsanity.com/paper/2106.02094/) (2021)
- [Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods](https://mlsanity.com/paper/2111.10627/) (2021)
- [Inverse problem for parameters identification in a modified SIRD epidemic model using ensemble neural networks](https://mlsanity.com/paper/2203.00407/) (2022)
- [The Benefit of Noise-Injection for Dynamic Gray-Box Model Creation](https://mlsanity.com/paper/2310.01517/) (2023)
- [A Multi-Agent Reinforcement Learning Framework for Public Health Decision Analysis](https://mlsanity.com/paper/2311.00855/) (2023)

## Abstract (arXiv)

The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread and continue to cause an increasing number of fatalities worldwide. While vaccines are still under development, social distancing, extensive testing, and quarantining of confirmed infected subjects remain the most effective measures to contain the pandemic. These measures carry a significant socioeconomic cost. In this work, we introduce a novel optimization-based decision-making framework for managing the COVID-19 outbreak in the US. This includes modeling the dynamics of affected populations, estimating the model parameters and hidden states from data, and an optimal control strategy for sequencing social distancing and testing events such that the number of infections is minimized. The analysis of our extensive computational efforts reveals that social distancing and quarantining are most effective when implemented early, with quarantining of confirmed infected subjects having a much higher impact. Further, we find that "on-off" policies alternating between strict social distancing and relaxing such restrictions can be effective at "flattening" the curve while likely minimizing social and economic cost.