The Social & Economic Impact of Data Analytics and Artificial Intelligence in Combating Covid-19.
A Critical Review
1.0 INTRODUCTION
Coronavirus is a viral disease and an RNA virus of the family Coronaviridae. Coronavirus disease, which emerged in 2019, is one of the lethal RNA viruses that have traumatized the entire planet and society since late 2019, damaging health and socioeconomic aspects of lives. COVID-19 was given this name as a coinage for when it first appeared which was in 2019, as well as a derivation from its parent RNA family. COVID-19 has had an enormous impact on society since its emergence in a variety of areas, including healthcare, information technology, economics, transportation, operations, politics, and business model, among others.
According to WHO statistics as of November 14, 2022, confirmed cases of COVID-19 globally reached a staggering 631,935,687 with recorded deaths of 6,588,850. This indicates a high mortality rate of 1.05% for all instances of the disease that have been verified i.e One death is recorded for every 100 confirmed cases. Since numerous pharmaceutical firms began developing anti-viral vaccinations, there has been a significant decrease in the death rate. The United States of America, Brazil, India, the Russian Federation and Mexico are the top five countries in the world with the highest counts of cumulative deaths reported to date with recorded deaths of 1,062,721, 688,567, 539,531, 391,024, and 330,418 respectively.
Globally, almost 13 billion vaccination doses had been dispensed as of November 10th, 2022. Vaccination is divided into two doses: dose 1 and dose 2, as well as booster vaccinations for highly susceptible individuals. 5,431,705,796 people have been vaccinated with at least one dose, while 4,976,582,832 people have been completely vaccinated with the recommended dosages. This demonstrates that public perceptions concerning COVID-19 vaccination have improved from the vaccine’s initial deployment stage, largely due to its positive impact in boosting the immune system. Sub-Saharan Africa has the lowest index by 100 population with regards to vaccination.
Different government bodies and corporate organizations have deployed various data science technologies in combating the COVID-19 pandemic since its emergence. Among them are DataRobot, NVIDIA, RunAI, and DarwinAI which are private organizations that are offering their platforms, software, or models for free to users. These open-source software and platforms enable models’ deployment, operations, and management by enthusiastic data science professionals to fight the pandemic. The World Health Organization have a specialized website for COVID-19 (https://covid19.who.int), the Centre for Disease Control covid data tracker (https://covid.cdc.gov) and the White House has formed the Open Reach Dataset which allows text mining to be used by data scientists to effectively handle Big Data using cutting-edge artificial intelligence and machine learning techniques.
The application of these techniques is used by professionals in different industries for diagnosis, prognosis, mortality prediction, vaccination, and monitoring in relation to the social and economic impact of COVID-19 on society. The insights derived from the implementations of these techniques are what key decision-makers rely on in enacting policies to combat the pandemic.
2.0 METHODOLOGIES AND DATA TECHNIQUES
In this critical review paper, the aim is to examine the Social & Economic Impact of Data Analytics and Artificial Intelligence in combating Covid-19 from its emergence in 2019 till date. The benefits and setbacks of using these approaches to combat the pandemic, as well as their relevance and influence on society, will be examined extensively. Recognizing and identifying the benefits and negative consequences of dynamic technological models can help decision-makers, experts, and developers eliminate errors and deliver diversified solutions to society.
The methodology used in this article will involve examining the various effects that Artificial Intelligence and Big Data Analytics have had on the economy and how they have improved or will continue to enhance economies worldwide. There will be different literature reviews of publications, journals, websites, and articles that describe what artificial intelligence (AI) and Big Data are, their various applications, and their advantages and drawbacks in society. One of the major databases used will be google scholar and keywords such as Artificial intelligence and Machine learning will also be applied.
2.1 Literature Reviews
Literature reviews on how the social & economic impact of data analytics and artificial intelligence in combating covid-19 is discussed in this study. In this study, the main criteria used to determine a journal or article’s eligibility include: (i) focused at least on big data analytics or artificial intelligence technique or methodology; (ii) discussed any aspects of COVID-19 implementation from a social or economic perspective; (iii) publication should be from the outbreak in 2019 till date; and (iv) discussed the socioeconomic implication of COVID-19 and the challenges created by these applications. Key search terms applied to select journals from various literature sources include Artificial Intelligence, Machine Learning, COVID-19, Big Data Analytics, Post-Covid Economics, and Diagnosis.
According to numerous papers published, mainly in PubMed, ScienceDirect, ResearchGate, and other repositories, Information technology has been used to combat the COVID-19 pandemic since its emergence in 2019. (Lu et al., 2022) developed a framework that informs AI-enabled sustainable development for SMEs by integrating relevant research in the field. By examining the obstacles SMEs experience in the Artificial Intelligence implementation phase and suggesting solutions, they are able to find potential for the deployment of Artificial Intelligence technology to improve the situation of SMEs in the post-pandemic phase, including the effects on the workforce, organizations, and performances. Professionals were able to differentiate between respiratory abnormalities caused by COVID-19 and those created by chemotherapy and radiation in cancer patients using diagnostic and imaging data paired with Artificial Intelligence. In such circumstances, advances in artificial intelligence (AI) and machine learning algorithms (ML) offer the capacity to boost cancer sufferer diagnosis, therapy, and care via the use of cutting technologies (Boddu et al., 2022).
In Taiwan, Clinical predictors of COVID-19 mortality and a novel Artificial Intelligence prognostic model using chest X-rays were developed by (Wu et al., 2022). During the initial surge, they examined the overall mortality indicators in severely sick COVID-19 patients at Taipei Tzu Chi Hospital in Taiwan. They used Five-Fold Cross-Validation to assess their predictive model accuracy after training the model on data from 64 COVID-19 cases with overall mortality rates of 45%. Chinese researchers demonstrated that their deep learning model can reach an accuracy of 90.1% with a positive predictive value of 84% and a negative predictive value of 98.2% when trained & tested on 630 CT volumes (Fu et al., 2020). This study provides a quick method for detecting a COVID-19 patient, which may be very helpful for prompt isolation and medical intervention.
Human sentiment monitoring during COVID-19 using Artificial Intelligence-based modelling tools was implemented (Umair & Masciari, 2022). In their work, they used mined tweets from Twitter on COVID-19 vaccination to analyze using artificial intelligence the responses of the public to the vaccination campaign. Using the TextBlob() method, they determined the duality of the tweets. Using the BERT model, they divided the Twitter posts into groups that were positive and negative based on their polarity ratings. Overall, a study by (Manoj et al., 2022) presents an overview of COVID-19 detection using deep learning methods and their cost-effectiveness and financial implications from the perspective of insurance claim settlement. The cost-effectiveness of the approaches assessed for identifying COVID-19 in comparison to other methods is also discussed in this study. The usefulness of various COVID-19 detection technologies has been reviewed from several financial perspectives.
In order to assist develop an efficient control strategy, a newly developed model that forecasts the trend of the pandemic was presented in Italy. The model termed “SIDARTHE” discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms (Giordano et al., 2020). Case fatality rates and pandemic spread are misperceived because of the distinction between diagnosed and non-diagnosed individuals. The outcomes of their simulations were compared with actual data on the COVID-19 pandemic in Italy, allowing them to create viable implementation options for countermeasures. According to the findings, it is necessary to put rigorous social distancing policies in place in addition to wide testing and contact monitoring to put a stop to the ongoing COVID-19 pandemic.
In December 2021, an article reviewing patients with “long COVID-19 syndrome” was published outlining the importance of Artificial Intelligence. AI-based models were applied in long-COVID-19 patients to assist health practitioners to reduce the considerable impact on the care and rehabilitation unit (Cau et al., 2022). The study shows that a large percentage of COVID-19 surviving victims reported recurrent symptoms caused by multiple organ involvement. A research journal by (Dennis et al., 2021) analyzes any moderate respiratory damage in symptomatic patients that are over 18 years old who have recovered from an acute COVID-19 infection from two communities in the United Kingdom. Out of 201 patients assessed for the investigation, 70% exhibit organ deficiencies 4 months after their first COVID-19 case, leading to comorbidities and impairments that would undoubtedly damage their social and economic lives.
2.2 Data Techniques
A. Big Data Analytics
Complex data sets may be analyzed statistically using big data analytics. The term Big Data refers to gigantic, larger datasets (volume); more diversified, including structured, semi-structured, and unstructured (variety) data, and arriving faster (velocity) than before (Riahi & Riahi, 2018). Big data means the datasets which cannot be recognized, obtained, managed, analyzed, and processed by present tools (Chahal & Gulia, 2016). The seven properties of big data — volume, vocabulary, value, variety, validity, venue, velocity, veracity, vagueness, and variability — are frequently used to define this accumulation of data from several sources.
The term “Big Data Analytics” entails the process of gathering, integrating, and analyzing massive data sets to identify various patterns and other significant details in the data. Using Big Data Analytics, we can uncover hidden patterns embedded within complicated, huge datasets, usually from sizable datasets, which require unique forms of integration.
Again, big data analytics is where advanced analytic techniques operate on big data (Russom & Org, 2011). Big data analytics is a rapidly expanding and important field of study in Data Science. Big Data Analytics can be categorized into 4 different types which are;
· Descriptive Analytics — What is happening?
· Prescriptive Analytics — What should be done?
· Predictive Analytics — What is likely to happen?
· Diagnostic Analytics — Why caused this happen?
Big Data Analytics is the process of asking questions in whatever area they are being deployed with regard to the set of information being provided. The field of big data analytics has been accepted by all industries and developed into a separate field of specialization in the Data Science industry. However, the act of analyzing this data within the context of big data can occasionally feel rather tedious and time-consuming because of how unstructured most of these data are.
B. Artificial Intelligence
Artificial intelligence is a field of computing that focuses primarily on the transmission of anthropomorphic intelligence and thinking into machines that can assist humans in many ways (Sivasubramanian, 2021). Basically, it refers to the art of creating intelligent machines that can behave, think, and act like humans. John McCarthy introduced the concept of “artificial intelligence” and organized the first AI summit in 1956. When a machine is capable of possessing human-like abilities like learning, thinking, and problem-solving, this is known as artificial intelligence. Artificial Intelligence is achieved by examining information processing and researching the patterns of the human brain.
The different forms of Artificial Intelligence are Self-Aware machines, Theory of Mind machines, Limited Memory machines, and Purely Reactive machines. Large data sets and adaptive, iterative processing algorithms are used to create Artificial intelligence systems. Artificial Intelligence can now learn from patterns and characteristics in the evaluated data attributable to this fusion. The Artificial Intelligence system checks and evaluates its performance after each cycle of data processing, using the outcomes to gain additional insight.
The capability of Artificial Intelligence to learn is provided by machine learning. Artificial intelligence can already simulate the neural network of the human brain thanks to deep learning, a branch of machine learning. It can make sense of trends, distortion, and other elements of data ambiguity.
3.0 DATA APPLICATIONS
Artificial intelligence and Big Data Analytics are in the spotlight throughout the COVID-19 pandemic in the world because of their capacity to offer perceptions and remedies for controlling the pandemic. Big Data Analytics was used in investigating relationships between government policies and responses to the COVID-19 pandemic outbreak in Poland, Turkey and South Korea. Poland and Turkey were chosen because they are both European nations, have comparable economic indexes, fall within the IMF’s category of emerging and developing countries’ economies, and are responding to COVID-19 in essentially the same way as advised by WHO.
BlueDot, a digital health firm established in Canada, discovered unconventional respiratory infections cases in China in December 2019, 10 days before WHO issued their first warnings about an emerging new virus of the corona family. Subsequently, BlueDot Artificial Intelligence analysis accurately predicted 20 cities that would be heavily affected after the Wuhan region in China. Big data analytics are used by BlueDot to monitor and predict the spread of viral epidemics. They used a variety of data sources, including news articles, medical alerts, livestock reports, climate data, demographic data, and more, to train a computer algorithm to recognize 150 viruses (Caulder et al., 2020).
A deep learning-based model for automatic COVID-19 detection on chest CT scans (Fu et al., 2020) was developed in China to counter the outbreak of SARS-CoV-2. The algorithm obtained a very high True Negative of 98.2% and a True Positive of 84% at a probability threshold of 0.5 for classifying COVID-19 positive or negative cases. Performance metrics were calculated for this model using 499 CT scan volumes, with a sensitivity of 0.907, a specificity of 0.911, a receiver operating characteristic AUC of 0.959, and a pattern recognition AUC of 0.976. Using a dedicated Graphics Processing Unit, the algorithm processed a single patient’s CT volume in just 1.93 seconds. The easily trained and high-performance deep learning algorithm provides a fast way to identify COVID-19 patients (Fu et al., 2020).
Geospatial AI applications allow users to key in their symptoms and then predict the likelihood of being COVID-19 positive. Psychologically distressed patients exposed to COVID-19 can benefit from immediate measures recommended by the application, which is useful for those living in remote places where COVID-19 prevalence is high. By analyzing human responses and sentiments across different regions, ML and AI models can be used to develop a decision-making system that determines what actions are appropriate for different countries, based on data collected from Twitter about healthcare, vaccinations, business economies, social change, and psychological stress. The TextBlob() function of python was used to develop the polarity of the tweets relating to COVID-19. The BERT model was developed to classify the tweets into negative and positive classes based on their polarity values (Umair & Masciari, 2022). This was used in developing a vaccination framework in the United States of America for vaccine dissemination to areas that are more susceptible, responsive, and approbatory towards the vaccine during the initial release.
Early mortality rate prediction in ICU patients is urgently needed to prioritize treatment methods because of the rising number of COVID-19 victims. In Iran and the United Kingdom, 797 people who had been diagnosed with COVID-19 were studied and analyzed using key indicators. Kolmogorov-Smirnov and Pearson chi-squared tests were performed to determine the factors with the highest predictive attributes. To create classification models, SVM, Logistic Regression, gradient boosting, Random Forest, and artificial neural network algorithms were used. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (Jamshidi et al., 2022). Also, Machine learning and deep learning were successfully used to predict mortality in patients with spontaneous coronary artery dissection by Chayakrit Krittanawong et al., 2021 on large clinical data. The best-performing Deep Learning algorithm identified in-hospital mortality with an AUC of 0.98 and 95% accuracy (Krittanawong et al., 2021).
In Korea, the invention of a kit for quickly detecting those who may have been exposed as well as cutting-edge testing techniques allowed for the daily testing of thousands of people (Shaw et al., 2020). This AI-enabled technology enabled early diagnosis and prognosis with monitoring and tracking using facial recognition developed by Deep Learning Convolutional Neural Network (DL-CNN).
A reinforcement learning AI created by McKinsey was used to prioritize investments in new technologies based on the socioeconomic benefits that they will provide to society’s health (Craven et al., 2020). According to the World Bank, companies boosted their usage of data-enabled internet platforms by percentages as high as 81% in Indonesia to 11% in Ghana. The average sector-level predicted market growth for the first half of 2021 and corresponding uncertainty were modelled using Linear Regression.
Before COVID-19, 151 Fintech start-ups and incumbent firms in 33 countries were surveyed by the World Economic Forum, and 85% of them were already using BDA & AI, mostly in risk management. By 2022, 77% of businesses expect Artificial Intelligence to have a very high overall importance to their business, and 64% expect to use AI in three or more business areas within the next years. Bank of England reported that about 35% of banks worldwide reported an increase in the levels of active Artificial Intelligence and Data Analytics applications in their operations. But only 23% of banks reported an increase in funding and/or resources for planned applications and 11.5% of banks reported a decrease (David et al., 2020).
The COVID-19 pandemic epidemic caused a 6.7% reduction in the world GDP in 2020. More than 200 million people were anticipated to be unemployed worldwide in 2021 as a result of the COVID pandemic, with women and youths mostly affected. The digitalization of society and the economy over the past two decades has generated vast amounts of data (David et al., 2020). Businesses and organizations seeking data-driven insights are increasingly relying on Big Data Analytics and Artificial Intelligence, these have been increasingly used across a range of industries as a result since the emergence of COVID-19.
4.0 DISCUSSION
From the reviewed studies, we can explore the significance of implementing Data Analytics and Artificial Intelligence in combating The Social & Economic Impact of Covid-19. Data scientists and researchers can handle an incredibly wide range of socioeconomic concerns because of the scalability of Artificial Intelligence and Big Data Analytics techniques.
A. Benefits
The COVID-19 outbreak has highlighted the need for business digitization. AI and BDA technologies provided businesses with a competitive edge in their industries and a chance to survive the COVID-19 pandemic. AI-based solutions may assist businesses and industries with automation, which reduces mistakes, saves time and helps them avoid risks in their operations. Advanced data is gathered and processed to enhance business trend analysis, which optimizes business operations like customer engagement, logistics, strategy, and planning while lowering costs and boosting sales and profits. Additionally, BDA and AI can assist in resolving the issue of labour scarcity during the pandemic caused by government-mandated lockdowns and allow businesses to continue operating with the limited resources at their disposal.
Robots equipped with artificial intelligence have been developed to serve food and medicines to COVID-19 patients in hospitals, namely “Cloud Ginger” (China) and “Nightingale-19” (India). COVID-19 could be detected with AI technology-induced cameras equipped with thermal sensors developed by KroniKare (Pillai & Kumar, 2021). METABIOTA, a USA-based company, developed an AI-enabled heatmap so that individuals and government authorities will know in advance when future outbreaks will occur by gathering spatiotemporal data from various locations, hospitals, and clinics.
In order to effectively screen COVID-19 symptomatic and asymptomatic individuals, the healthcare sector had to resort to ML-enabled applications. The facial temperatures of individuals in open spaces like airports, libraries, and malls are routinely monitored for increases in their body temperatures using computer vision and infrared sensors, and classification models are used to assess COVID-19 risk. To identify potential future risk areas, Baidu used big data to identify clusters of infected people, mobility data, and when factories were reopened (Shaw et al., 2020).
B. Limitations and Challenges
However, this broad scientific functionality also poses a spectrum of ethical problems. Given the detrimental effects of COVID-19 on vulnerable individuals and the life-or-death implications of stereotyped and stigmatizing public access to health care, pre-pandemic doubts that data-driven technologies may operate to reinforce these dynamics of socioeconomic biases have also increased. (Leslie, 2020) introduced a 5-Steps approach that provides a practice-based path to responsible AI design and discovery centred on open, accountable, equitable, and democratically governed processes and products.
Secondly, the absence of standardized datasets is a significant obstacle to the development of AI and big data platforms and apps that can effectively combat the COVID-19 virus. Because different datasets from different spatiotemporal sources were used in modelling different algorithms, we are unable to choose the most effective algorithm for detecting the virus. Also, the successful adoption of AI technology requires the cooperation of many stakeholders (Lu et al., 2022).
Developing a business environment of high-quality data-driven business strategy requires key decision-makers to be vast in the industry and be financially committed to the deployment. Also, any organization’s business model may be impacted by a shared environment of digitalization, necessitating a complete rewrite of the business model. Due to the requirement for a significant volume of data, many businesses do not yet have the high-quality infrastructure necessary to enable the deployment of Artificial Intelligence.
The performance of some these models has been negatively impacted by COVID-19, according to a study conducted by the Bank of England. The reason for this is that Machine Learning algorithms can perform differently other than on the factors for which they were originally modelled. This can happen when the underlying statistical data changes (data drift) (David, Mohammed and Oliver Thew, 2020). This survey showed COVID-19 has led to a rise in the usage of 3rd party data, resources, and custom ML models by small banks.
In terms of ethics, the most well-known issues include privacy, data gathering and usage to stop the spread of the pandemic, and the requirement to secure data from technological abuse. Incompetent users, criminals, hackers, public officials, and other influential social media users might misuse these data science technologies, threatening safety and privacy, resulting in loss of finances, and disrupting socioeconomic norms. There are several benefits and merits to using BDA, ML, and AI to examine the socioeconomic effects of COVID-19, but these are some underlying challenges.
5.0 CONCLUSION
In this review, the importance of Data Science within the socioeconomic demography has been highlighted because of the pandemic and its control measures. More than ever, BDA, ML and AI are being used to promote inclusivity and diversity in our society. The principles and dynamics of ML, AI and Big Data for developing quick and efficient techniques in combatting the COVID-19 pandemic in our society were reviewed. Machine Learning, Big Data Analytics and Artificial Intelligence have greatly impacted the behavioural patterns of organizations and institutions in crucial areas like recruitment, performance review, market research, quality assurance, productivity, and resource management. Additionally, worldwide pandemic prediction is made possible by the availability of big data.
The COVID-19 pandemic is projected from a perspective of data analysis using the information that is currently available, however, the shortage of thorough investigations calls into question the precision of some of these models regarding their predictions’ accuracies and precisions. Discussion is provided on how AI and BDA can be applied to the prediction, diagnosis, prognosis, vaccination, monitoring, and economic implications associated with the COVID-19 disease with their pros and cons weighed. Additionally, data drift was seen owing to the evolution of various variants of the virus over time, leading to changes in the parameters and factors used in training some of these models. To assist businesses and institutions in leveraging Data Science technologies for continuous improvements, additional research studies are suggested.
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