Artificial Intelligence and Machine Learning Approaches to Covid-19 Outbreak : a Survey

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 [email protected],[email protected],[email protected], [email protected]

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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Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

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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Turkish Journal of Physiotherapy and Rehabilitation; 32(3) 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 www.turkjphysiotherrehabil.org 692

Turkish Journal of Physiotherapy and Rehabilitation; 32(3) 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 www.turkjphysiotherrehabil.org 693

Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

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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Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

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. www.turkjphysiotherrehabil.org 695

Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X

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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