Artificial Intelligence and Data Science in the Detection, Diagnosis, and Control of COVID-19: A Systematic Mapping Study

Tintín, V.; Florez, H.

On March 11 2020, the World Health Organization (WHO) announced that the new COVID-19 disease, caused by the SARS-CoV2 could be considered a pandemic. Both this new virus and the disease it causes were unknown before the outbreak in Wuhan (China) in December 2019. Since then, the number of infections has grown exponentially causing the collapse of health-care systems, as well as socio-economic structures of countries around the world. The objective of this study is to give an overview of the application of Artificial Intelligence and Data Science in the control of the pandemic through a systematic mapping of scientific literature that determines the nature, scope and quantity of published primary studies. The research was carried out using the databases Scopus, IEEE Xplore, PubMed Central and the global research database of the World Health Organization. Thus, 372 studies were identified that met the inclusion criteria. The application of artificial intelligence techniques was observed, such as neural networks, deep learning, and machine learning in some areas including detection and imaging diagnosis, prediction of new outbreaks and mortality, social distancing, among others. In data analysis, artificial intelligence has become an important tool in the fight against COVID-19 and this study may be useful for the scientific community to direct future research into less-investigated areas. © 2021, Springer Nature Switzerland AG.