Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts

Abstract

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571,527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak.

Keywords: Artificial intelligence (AI); COVID-19; big data; coronavirus; data analytics; deep learning; epidemic outbreak; machine learning.

FIGURE 1.

The global COVID-19 trend (Source: CoronaBoard). Data accurate as of July 13, 2020.

FIGURE 2.

An illustration of DL-based frameworks for the COVID-19 detection and diagnosis.

FIGURE 3.

Big data and its applications for fighting COVID-19 pandemic.

FIGURE 4.

An AI-based framework using mobile phones for COVID-19 diagnosis and surveillance. Adapted from . model can be trained in the cloud, even at a server collocated at the network edge, which is then pushed to mobile phone for further purpose. For example, by using the embedded cameras and biosensors, a mobile phone can collect personal information, for example, X-ray and CT images, cough sound, and heart rate, which may be encrypted and compressed before sending to the cloud for training , . Review on recent advances in biosensors for mobile health platforms can be found in . The massive number of mobile devices connected to the Internet may relax the limited data sent from an individual phone. According to the latest annual Internet report from Cisco , by 2023 around 29.3 billion networked devices will be connected to the Internet, where mobile phones and tablets account for 23% and 3% respectively, and over 70% of the world population will have mobile connectivity.

FIGURE 5.

Illustration of a data-driven framework for discovering antibody sequences to treat the COVID-19 disease .