Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone–based survey when cities and towns are under quarantine

By Arni S. R. Srinivasa Rao, J. Vázquez. Infection control and hospital epidemiology, 2020

305 citations recorded by the source corpus.

Abstract We propose the use of a machine learning algorithm to improve possible COVID-19 case identification more quickly using a mobile phone–based web survey. This method could reduce the spread of the virus in susceptible populations under quarantine.

Subfields: Epidemiology, Health Information Management, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/15e18a801c0016766d4544e4440e615c4e5d6952

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Miguel Angel Luengo-Oroz
2. C. Lam
3. Joseph Aylett-Bullock
4. Alexandra Sasha Luccioni
5. Katherine Hoffmann Pham
6. Aishwarya Kumar
7. Wim Naudé

Citations in the corpus

• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “…to identify COVID-19 symptoms (Maghdid et al., 2020)† or phone-based surveys to filter high-risk patients based on responses to key questions (Rao & Vazquez, 2020); while these are important efforts given the accessibility of mobile phone technology, the existing studies are not…”