# Data-Driven Decision Making in COVID-19 Response: A Survey

arXiv: https://arxiv.org/abs/2202.11435 · HTML: https://mlsanity.com/paper/2202.11435/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2022.

## Built on by (most cited first, 5 of 5)

- [Graph Augmentation Learning](https://mlsanity.com/paper/2203.09020/) (2022)
- [Black-Box Optimization with Implicit Constraints for Public Policy](https://mlsanity.com/paper/2310.18449/) (2023)
- [Computationally Assisted Quality Control for Public Health Data Streams](https://mlsanity.com/paper/2306.16914/) (2023)
- [Algorithmic Fairness: A Tolerance Perspective](https://mlsanity.com/paper/2405.09543/) (2024)
- [PANDORA: Deep graph learning based COVID-19 infection risk level forecasting](https://mlsanity.com/paper/2406.06618/) (2024)

## Abstract (arXiv)

COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly plays a vital role in effective decision making. Data-driven decision making uses data related evidence and insights to guide the decision making process and to verify the plan of action before it is committed. To better handle the epidemic, governments and policy making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, etc. Based on these data, many prevention and control policies are made. In this survey paper, we summarize the progress of data-driven decision making in the response to COVID-19, including COVID-19 prevention and control, psychological counselling, financial aid, work resumption, and school re-opening. We also propose some current challenges and open issues in data-driven decision making, including data collection and quality, complex data analysis, and fairness in decision making. This survey paper sheds light on current policy making driven by data, which also provides a feasible direction for further scientific research.