ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES TO COVID-19 OUTBREAK : A SURVEY
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Turkish Journal of Physiotherapy and Rehabilitation; 32(3)
ISSN 2651-4451 | e-ISSN 2651-446X
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES
TO COVID-19 OUTBREAK : A SURVEY
Sowmya H.K1, Jesy Janet Kumari J2, Dr. R. Ch. A. Naidu3, K.Vengatesan4
1,2,3
Department of CSE, The Oxford College of Engineering Bommanahalli, Bangalore-560068
Professor, Computer Engineering, Sanjivani College of Engineering, Kopargaon
hk.sowmyakiran@gmail.com,ebijesy@gmail.com,enghodcse@theoxford.edu,
vengicse2005@gmail.com
ABSTRACT
Corona viruses are group of viruses which may cause illness in both animals and humans. A person can easily
get COVID-19 from the other people who have the virus. It is a serious disease, which disseminates through
tiny water droplets from the nose or mouth, which are thrown out when a COVID-19 infected person coughs,
sneezes, or speaks. This disease was first discovered in China and has since spread throughout the world at
breakneck speed. At this pandemic time, the entire world should take an adequate and efficient step to analyze
the disease and get rid of the effects of this epidemic. The applications of Machine Learning (ML) and Artificial
Intelligence (AI) techniques play an important role to detect and predict potential effect of this virus in future
by gathering and examining most recent and past data. Furthermore, it can be used in realizing and
recommending the enhancement of a vaccine for COVID-19. This paper focuses on reviewing the role of AI
and ML approaches used for examining, analyzing, predicting, contact tracking of existing patients and
potential patients.
Keywords: Corona virus, Machine Learning, Artificial Intelligence, contact tracking, epidemic
I. INTRODUCTION
Coronavirus disease (COVID-19) is a newly identified virus that causes an infectious disease. The form of the
infection is due to the Severe Acute Respiratory Syndrome Corona Virus -2 (SARS-CoV-2) from the coronovirus
family. COVID-19 will be spread primarily through droplets produced by infected people coughing, sneezing, or
exhaling. SARS- CoV-2 infections are accepted to spread between people through introduction to respiratory beads
ousted during hacking or wheezing from irresistible people. This infection can stay airborne for longer periods
however may cause rapidly, contingent upon ecological conditions. Individuals will be tainted by breathing the
infection if closeness of somebody who has COVID-19 or by contacting a polluted surface likes eyes, nose or
mouth.
This pandemic emerged in terrain China, in the city of Wuhan, Hubei. The episode keeps on spreading everywhere
on the world, so World Health Organization pronounced the pandemic as a pestilence. The tale Corona infection
(SARS- CoV-2) sickness began spreading to in excess of 185 nations. Individuals can ensure themselves by
washing hands as often as possible or cleaning with liquor based cleanser, trying not to contact the eyes, mouth,
and nose with messy hands, when coughing or sniffling, use a tissue to cover your mouth and nose, remaining at
any rate 6 feet from others.
Artificial Intelligence (AI) and Machine Learning (ML) are assuming an essential function in an imaginative
innovation which is useful to battle against the COVID-19 pandemic. AI is an innovation method to gather
information, to anticipate danger of disease and to foresee who is at high danger. It allows computers to emulate
human intelligence and consume vast amounts of data in order to find patterns and insights quickly. The Covid-19
outbreak will be the subject of this review article, as well as how AI and machine learning technologies are being
used to solve the problems that occur throughout the blaze.
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II. APPLICATIONS OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE TO THE COVID-19
EPIDEMIC
In the COVID-19 pandemic response, machine learning-based approaches are playing an important part. AI is being
used by experts to think about the infection, test potential drugs, evaluate people, and look at the overall health
consequences, among other things. Computer-based intelligence and machine learning (ML) are being used to
increase forecast accuracy for both irresistible and non- irresistible diseases. Recent research demonstrates how
artificial intelligence and machine learning techniques aid health care professionals in combating the spread of
communicable and non-communicable diseases, particularly in low-income countries.
III. COVID-19 SCREENING AND MEDICATION USING MACHINE LEARNING AND ARTIFICIAL
INTELLIGENCE TECHNIQUES
The identification of pandemic infection at prior stage is significant errand to give earlier medicine to protect human
existence. The powerful analytical process aids in the cost-effective prevention of pandemic diseases thereby
speeding up the examination process. In comparison to the traditional approach, developing an expert system for
medical care assistance for the detection, screening, and management of Covid -19 infection is more cost effective.
Different calculations of Machine Learning and Artificial Intelligence are utilized to upgrade the assessment and
screening measure with the assistance of radiology pictures, for example, patient's Computed Tomography (CT)
pictures, X-Ray pictures, and Clinical blood test information. Table 1 depicts the procedure, sort, and size of the
data used to perform the detection and screening of Corona infection illness from this perspective. To improve
conventional strategy for determination and screening, medical care professionals utilize X-Ray and CT images of
the distinguished patient as standard tool. Shockingly, the presentation of such techniques is low and not relevant
during the flare-up of Covid-19 pandemic.
In such manner, late exploration study portrays that, the scientists could separate 11 key blood lists from 253 clinical
blood tests and can be utilized as an instrument to help medical services specialists toward quick analysis of Covid-
19 sickness [1]. The Random Forest algorithm was used by the authors of this paper to extract the features, which
they discovered with an accuracy level of 95.95 percent and a specificity of 96.97 percent. The authors further
presented the Covid – 19 assistant discrimination tools, which takes 11 parameters and calculates and displays
whether a sample is COVID-19 patient with a high chance of being diagnosed. Despite the fact that the proposed
tool must be clinically tested several times, it provides some novel insight into the rapid diagnosis of COIVD-19
infection. Recent examination shows the capability of AI and ML devices by recommending another model that
accompanies fast and legitimate strategy Covid-19 conclusion utilizing Deep Convolutional Network. The
examination [2] shows the exhibition of a specialist framework practicing Artificial Intelligence and Machine
Learning on 1136 CT Images of 723 Covid-19 tainted patients, recommends the utilization of the Deep Learning
Segmentation Classification Model as an assistant device for radiologist coming about 97.4%, 92.2% of
affectability and particularity separately.
Ongoing examinations [3] shows that creation of extra instrument for the analysis of Covid-19 with Convolutional
Neural Network called DarkCovidNet Architecture dependent on Deep Learning calculation raise the exactness.
The developed model was based on 127 contaminated patients' primitive x Ray images, and it accurately predicted
the exhibition, with a precision of 98.08 percent for parallel class and 87.02 percent for multi-class. Moreover,
specialists have utilized Support Vector Machine (SVM) as an element order model [4] on clinical highlights, for
example, mixes of clinical, lab highlights, and segment data utilizing GHS, CD3 rate, all out protein, and patient.
In anticipating critical Covid cases in patients, this latest design is both effective and accurate. The model's
robustness is demonstrated by an AUROC of 0.9996 for training data and 0.9757 for testing data.
Researchers used deep learning algorithms to build a large data repository of X-rays and CT scan images from
different sources, and suggested an efficient COVID-19 detection technique [5]. On the prepared dataset of X-rays
and CT scan images, a simple convolution neural network (CNN) and a modified pre-trained AlexNet model are
used. Empirical findings show that using a pre-trained network and a modified CNN, the proposed models could
produce 98 percent and 94.1 percent accuracy, respectively.
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ISSN 2651-4451 | e-ISSN 2651-446X
TABLE 1. ML AND AI APPROACH FOR COVID - 19 TESTING
Author AI and ML method Kind of data Sample size Accuracy
Wu, J.et. al. [1] Random Forest Clinical, Demographic Total of 253 samples 95.95%
Algorithm from 169 Covid-19
suspicious
patients
Shuo Jin [2] Deep Learning Model Clinical 1136 CT 97%
Images, 723 positives
for COVID-19
Ozturk, T. et.al. CNN and DarkCovi Clinical, 127 X- 98.08%
[3] dNet Mammographic ray images
Sun, L et. al.[4] SVM Clinical, laboratory 336 infected 77.5%
Characteristics patients, 26
Demographic severe/critical cases
Halgurd S [5] CNN and Modified Clinical 170 X- 98%
AlexNet model ray images,
361 CT
images
Xiaowei Xu et. al Deep Learning ResNet Clinical 618 CT 86.7 %
[6] location- attention samples
Model
Ongoing Research [6] uncovers that, authors proposed an early screening model to draw a differentiation of Covid-
19 illness from Influenza-A pneumonia and solid cases with the assistance of CT pictures utilizing profound
learning strategies. An aggregate of 618 CT samples were gathered from patients, including 219 from 110 COVID-
19 patients, 224 from 224 patients with Influenza-A pneumonia, and 175 from healthy individuals. A pulmonary
CT image set was used in this study, and candidate infection sections were fragmented out using a three-
dimensional deep learning model using a location-attention classification model, these isolated images were divided
into three groups: COVID-19, viral pneumonia caused by influenza A, and non-infection. Ultimately, the infection
group and total confidence score of this CT study are computed using the Noisy-or Bayesian method. Models with
a location-attention function correctly classified COVID-19 based on computed tomography with an effective
success rate of 86.7 percent, according to experimental results.
IV. COVID -19 CONTACT TRACING USING MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
TECHNIQUES
To prevent further scattering, contact tracing is a tool for deciding, determining, and coping with people who are
powerless against a disease. An epidemic disease's propagation chain is broken when a contact tracing component
is used consistently. As a result, it is a critical health tool for preventing the spread of infectious diseases. Contact
following for COVID-19 requires distinguishing irresistible people and following them up every day for 14 days
from most recent purpose of weakness.
If an individual has been diagnosed with Covid-19 infection, the subsequent goal is to identify contacts to prevent
the disease from spreading further. The COVID-19 patient's contact tracing team is in charge of collecting a list of
those who interacted with him. Each person should be contacted first to see if they meet the contact definition and,
as a result, need to be monitored. Every individual should initially be reached to decide if they meet the contact
definition and in this manner require observing. Covid 19 is spread from person to person via droplet and contact
transmission, according to the WHO [18]. Medical professionals must use a contact tracing method to cut the link
of person to person transmission to avoid COVID-19 from spreading. This is done in order to reduce the number
of potential infections caused from each reported case. Using a variety of technologies, various contaminated
countries have developed digital contact tracing systems with a Smartphone application. When compared to a non-
computerized system, advanced contact following technique performs much faster. The majority of these mobile
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ISSN 2651-4451 | e-ISSN 2651-446X
apps gather user information that will be examined using machine learning and artificial intelligence (AI) methods
in order to track down an individual who is susceptible to the contagious disease based on their latest contact chain.
Table 2 depicts the respective countries' digital contact tracing apps, which are based on ML and AL approaches.
A study uncovers that more than 43 countries effectively practiced computerized contact tracing App. It uses
centralized, decentralized, or a combination of the two techniques to reduce complexity and enhance the efficacy
of traditional healthcare diagnostic methods.
TABLE 2 CONTACT TRACING APP EMPLOYED IN VARIOUS COUNTRIES
Country Contact Tracking App Protocol
Angola COVID-19 AO
Australia COVIDSafe BlueTrace protocol: Bluetooth
Austria Stopp Corona Bluetooth, Google/Apple
Bahrain Bahrain’s BeAware Bluetooth & GSM
Bangladesh Corona Tracer BD Bluetooth
Brazil The Spread Project
Bulgaria ViruSafe
Canada COVID Shield Google / Apple
privacy- preserving tracing project
Colombia CoronApp GPS
Czech Republic eFacemask BlueTrace protocol: Bluetooth
Denmark Smittestop Google / Apple privacy- preserving
tracing project
Finland Ketju DP-3T
France StopCovid Bluetooth
Germany Corona-Warn-App
Ghana GH Covid-19 Tracker App GPS
Greece DOCANDU Covid Checker
Hong Kong Stay Home Safe
Hungary VírusRadar Bluetooth
Iceland Rakning C-19 GPS
India Aarogya Setu Bluetooth and location generated,
Social graph
Indonesia PeduliLindungi
Iran Mask.ir GSM
Ireland HSE Covid-19 App Bluetooth, Google/Apple
Israel HaMagen Standard location APIs
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Italy Immuni Bluetooth, Google/Apple
Japan Shingata Sesshoku Apuri Koronauirusu Google / Apple Privacy, Preserving
Kakunin tracing project
Jordan AMAN App – Jordan GPS
Latvia Apturi Covid Bluetooth
Malaysia Gerak Malaysia Bluetooth, Google/Apple
New Zealand NZ COVID Tracer QR code
North Macedonia StopKorona Bluetooth
Norway Smittestopp app Bluetooth and GSM
Qatar Ehteraz Bluetooth and GSM
Poland ProteGO Safe Bluetooth
Saudi Arabia Corona Arabia Map Saudi Bluetooth
Singapore TraceTogether Blue Trace protocol, Bluetooth
South Africa Covi-ID PACT,GDPR
Sri Lanka Self Shield
Spain Radar COVID DP-3T, Google / Apple privacy-
preserving tracing project
Switzerland SwissCovid DP – 3T protocol, Bluetooth
Turkey Hayat Eve Sigar Bluetooth, GSM
United Kingdom NHS Covid-19 App Bluetooth
United States NOVID TCN Protocol
V. COVID-19 PREDICTION AND FORECASTING USING MACHINE LEARNING AND ARTIFICIAL
INTELLIGENCE TECHNIQUES
ML and AI prediction models are important for gaining insight into how infectious diseases spread and respond. In
assessment, and prediction machine learning methods are commonly used. In the presence of massive amounts of
data on the occurrence of infectious diseases, machine learning methods aid in the detection of outbreaks so that
further steps needs to be implemented to avoid the disease from spreading among humans. The behavior of the
systems is adopted in this review based on machine learning prediction methods [8] such as support vector
regression (SVR), polynomial regression (PR), and Multilayer Perceptron classifiers. The AI may predict mortality
rate based on patients' age, sex, exposure history, symptoms such as fever, cough, white blood cell counts (WBC),
neutrophil counts, and lymphocyte counts, and by analyzing patient's past data. By community screening, clinical
assistance, warning and disease control proposals, computer-based intelligence will help us to fight against the
infection [9].
To estimate the status of COVID-19, machine learning and artificial intelligence use this combined feature vector
as feedback. Table 3 portrays the ML and AI strategies utilized for anticipating and gauging of COVID-19 by
different scientists and the sorts of information utilized for their expectation. It likewise shows the precision of the
applied calculation in detail.
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TABLE 3. CONTRIBUTIONS OF ML AND AI APPROACHES IN ANTICIPATING AND GAUGING OF COVID – 19
Author AI and ML method Kind of data Results
Vaishy SVR and Ensemble Stacking Clinical Error in range of 0.87%-
et al., [8] 3.51%
Shinde. XGBoost classifier Clinical, Blood samples Precision of 90%
et al., [9]
Croccolo et al.,[11] LSTM network for deep Demographic Average Node degree is 2
learning
Yi-YuKe et al., [13] Regression tree and hybrid Demographic Eight drugs have been
model identified
VI. COVID -19 DRUGS AND VACCINATION DEVELOPMENT USING MACHINE LEARNING AND
ARTIFICIAL INTELLIGENCE TECHNIQUES
ML and AI have a major task to carry out in the battle [11] against COVID-19, especially from a symptomatic and
drug perspective. Artificial intelligence helps in processing a self-learning stage which is increasing on the earlier
clinical, pharma information and constant data. AI aids in the creation of a self-learning platform that is based on
previous clinical, pharmacological, and real-time data. An AI framework may be a valuable tool for rapidly
screening a large number of compounds with allocated training data repository and the goal to discover medicines
for particular uses, like diagnosis of COVID-19.
Infection with the COVID - 19 virus is equivalent to the severe acute respiratory syndrome (SARS) [12]
contagiousness in humans, such as pulmonary lesions. To identify coronavirus-active drugs among pharmaceutical
products, an AI-system built on learning dataset. Table 4 shows that the AI model found [13] a few drugs with
antiviral potential.
In the feline catus whole fetus-4 (Fcwf-4) cells (ATCC®CRL-2787), the virus is spreading and being investigated
[13]. Fcwf-4 cells were enhanced and maintained at 37 °C with 5% CO2 in Dulbecco's modified Eagle's medium
(DMEM) [14] containing 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin. Note: NA: not accessible
to recognize because of cytotoxicity. Chloroquine showed restraint movement against COVID-19 infections at 10
M, which is consistent with the examination directed on its broad protective role. It's a particular medication
designed to help COVID-19 patients to get better.
Coronavirus – 19 medications have explicitly been approved for the administration in randomized controlled
preliminaries. The NLRP3 (NOD)- like receptor protein 3)
[16] inflammasome assumes a significant function in antiviral host protections, its atypical enactment and
downstream go betweens frequently lead to neurotic tissue injury during disease. The NLRP3 is made out of
connector segment apoptosis-related spot like protein conveying a caspase initiation and enlistment area and the
chemically dormant procaspase-1. It has been indicated that few outside and inside upgrades including viral RNA
through systems, for example, arrangement of pores with particle rearrangement and lysosomal disturbance,
bringing about irritation and related cell demise called proptosis.
This inflammasome produces proinflammatory cytokines in up - regulated NFkb macrophages and Th1 cells. These
cytokines aid in the pathogenic inflammation that contributes to COVID-19's toxicant and syndrome. This
information has empowered numerous potential medication contemplations to reduce the threat of COVID-19. This
involves having enough sleep, handling tension, eating plenty of vegetables and fruits with isolated flavonoids,
curcumin, melatonin, and Sambucus nigra.
Artificial Intelligence method for SARS -CoV-2's may be very useful in monitoring holistic health methods for
COVID-19 risk prevention as more is known about SARS pathogenicity. Artificial intelligence treatment
techniques will be utilized to find integrative alternatives which can help resolve the immune reactions to SARS-
CoV-2 disease. Arising approaches consolidating integrative medication with AI could make novel arrangements
and help in the battle against this dangerous pandemic.
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TABLE 4. THE AI MODEL IDENTIFIED FEW DRUGS WHICH HAS POTENTIAL OF ANTIVIRAL EFFECT
AI Assisted Medicines Concentration (μM)
Cytotoxicity Viral Inhibition
Bedaquiline Above 50 50
Bifonazole Above 10 Not Accessible
Brequinar Above 50 2
Clotrimazole Above 2 Not Accessible
Duvelisib Above 50 Above 50
Econazole Above 10 Not Accessible
Fenticonazole Above 2 Not Accessible
Fumaric acid Above 50 Above 50
Lapatinib Above 10 Not Accessible
Miconazole Above 2 Not Accessible
Miconazole (nitrate) Above 10 Not Accessible
Pranlukast Above 50 Above 50
Sertaconazole Above 10 Not Accessible
Sulconazole Above 10 Not Accessible
Sulconazole (nitrate) Above 10 Not Accessible
Tacrolimus Above 50 Above 50
Telmisartan Above 50 Above 50
Tipifarnib Above 10 Not Accessible
Vismodegib Above 50 50
VII. CONCLUSION
The novel COVID-19’s disastrous outburst has posed a global threat to humanity. The entire world is putting forth
exceptional efforts to combat the disease's global spread. It is critical to predict and diagnose COVID-19 patients
quickly in order to prevent the disease from spreading to others As a result, researchers and clinical areas all over
the world have been urged to help fight the pandemic. They were looking for a replacement solution for rapid
screening and treatment, contact tracking, envisioning, and the development of new antibodies or drugs. As a result,
they received assistance from Machine Learning and Artificial Intelligence strategies. This study portrays the usage
of improved models with AI and ML methods essentially expanded the screening, expectation, contact following,
anticipating, and drug/immunization development with high precision. The majority of the authors in this study
used deep learning algorithms and demonstrated that they have greater ability and are more robust than alternative
machine learning methods. Nonetheless, the current pandemic situation necessitates an improved model that
provides high-quality screening results. As a consequence, it's indeed evident that AI and ML techniques will
dramatically enhance Covid-19 pandemic monitoring and prediction, treatment, prescribing, contact tracing,
forecasting, and drug and vaccine production. It greatly aids in the rapid prediction and diagnosis of medical
conditions and eliminates the need for human intervention in medical consultation.
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