# A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-survey-on-artificial-intelligence-approaches-in-supporting-frontline-workers/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Jawad Rasheed,Akhtar Jamıl,Alaa Ali Hameed,Usman Aftab,Javaria Aftab,Syed Attique Shah,Dirk Draheim |
| Citations | 127 |
| DOI | 10.1016/j.chaos.2020.110337 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | green |
| OA URL | https://www.ncbi.nlm.nih.gov/pmc/articles/7547637 |
| OpenAlex ID | https://openalex.org/W3092581351 |
| PMID | 33071481 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Syed Attique Shah](https://scholariq.org/researchers/syed-attique-shah/)

## Paper primary topic

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)

## Paper topics

- [COVID-19 diagnosis using AI](https://scholariq.org/topics/covid-19-diagnosis-using-ai/)
- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)
- [Anomaly Detection Techniques and Applications](https://scholariq.org/topics/anomaly-detection-techniques-and-applications/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.