Abstract
To date, the novel Corona virus (SARS-CoV-2) has infected millions and
has caused the deaths of thousands of people around the world. At the
moment, five antibodies, two from China, two from the U.S., and one from
the UK, have already been widely utilized and numerous vaccines are
under the trail process. In order to reach herd immunity, around 70% of
the population would need to be inoculated. It may take several years to
hinder the spread of SARS-CoV-2. Governments and concerned authorities
have taken stringent measurements such as enforcing partial, complete,
or smart lockdowns, building temporary medical facilities, advocating
social distancing, and mandating masks in public as well as setting up
awareness campaigns. Furthermore, there have been massive efforts in
various research areas and a wide variety of tools, technologies and
techniques have been explored and developed to combat the war against
this pandemic. Interestingly, machine learning algorithms and internet
of Things (IoTs) technology are the pioneers in this race. Up till now,
several real-time and intelligent COVID-19 forecasting, diagnosing, and
monitoring systems have been proposed to tackle the COVID-19 pandemic.
In this article based on our extensive literature review, we provide a
taxonomy based on the intelligent COVID-19 forecasting, diagnosing, and
monitoring systems. We review the available literature extensively under
the proposed taxonomy and have analyzed a significantly wide range of
machine learning algorithms and IoTs which can be used in predicting the
spread of COVID-19 and in diagnosing and monitoring the infected
individuals. Furthermore, we identify the challenges and also provide
our vision about the future research on COVID-19.