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\dochead{Survey Paper}

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\title{A Survey on Big Data Research in Fighting COVID-19: Contributions and Techniques }

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COVID-19 has induced many problems in various sectors of life for humanity around the world. After one year of pandemic, many studies have been carried out in exposing various technology innovations and applications to combat the coronavirus that has killed more people than most. The pandemic has accelerated the use of Big Data technology to mitigate the threats of COVID-19. This survey aims to explore the Big Data research for COVID-19. We collected and analyzed the relevant academic articles to identify how Big Data technology can cover the challenges faced in overcoming the pandemic. In determining the research areas addressed by the past studies, we highlight the technology contributions to five major areas of healthcare, social life, government policy, business and management, and the environment. We discuss how analytical techniques of machine learning, deep learning, statistics, and mathematics can solve pandemic issues. The Big Data research for COVID-19 used a wide variety of data sources available publicly or privately. At the end of the discussion, we present the data source used in the past studies encompassing government official data, institutional service data, IoT generated data, online media data, and open data. We hope that this survey will clarify the role of Big Data technology in enhancing the research for COVID-19.
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\kwd{Big Data}
\kwd{COVID-19}
\kwd{Research contribution area}
\kwd{Analytical techniques}
\kwd{Data source}
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\section*{Introduction}
Today the world is facing very formidable challenges. In the world history of the last century, the Coronavirus disease 2019 (COVID-19) pandemic is a health threat and the world's most severe humanitarian catastrophe, in addition to the Second World War. The COVID-19 outbreak is an acute respiratory syndrome and was declared a pandemic on 11 March 2020 by the World Health Organization (WHO)\cite{who:web2020}. The outbreak first appeared in Wuhan in December 2019 and continues to spread rapidly throughout mainland China and world wide, causing global panic and significant losses to people's lives and economies. The virus is transmitted through direct person-to-person contact and has caused many deaths. Based on COVID-19 dashboard data \cite{hopkins} as of 28 March 2021, it is confirmed that more than 126 million cases of COVID-19 have spread to 192 countries, with 2,777,684 deaths and 71,789,826 recoveries worldwide.

More than a year this pandemic has hit globally. Many countries have issued various policies to control the spread of the pandemic, such as working from home, learning from home, lockdown, travel restrictions, limiting the number of people in public places, and others \cite{wrycza14,sirinaovakul18,beria39,ashraf41,zheng53,suratnoaji78,gualtieri86,pepin71}. This pandemic created a new norm in society to always wear masks, wash hands frequently, maintain physical distance. This condition certainly affects almost all aspects of life, especially healthcare, social, environmental, economic, and business areas. Organizations and businesses are accelerating digital transformation programs during the pandemic \cite{marston}. Shopping online and cashless transaction to avoid physical contact has now become a necessity. The daily activities of meetings, lectures, graduations, seminars, or conferences must be held online to prevent spreading the virus due to crowds of people \cite{manalu, villegas13}. Pandemic has affected the environment. Air pollution is reduced during the pandemic. The lockdown and work from home policies make many people prefer to stay at home, which impacts reducing traffic on the roads and improving air quality in urban areas \cite{gualtieri86}. Besides, people like to ride bicycles over public transportation to avoid close contact among passengers on the local trip \cite{shang43}. 

The war against COVID-19 is not only carried out by paramedics and volunteers at the forefront, but also researchers take part by following their expertise areas to help find solutions to win against COVID-19. This pandemic provides many opportunities for researchers to offer technology-based solutions. Digital technology and big data play an essential role in adapting a new normal life by reducing the risk of spreading through teleworking, online learning, online shopping, webinars, telemedicine, and others \cite{ziheng, hoosain, doyle, dwivedi}. More than one year of research on Big Data for COVID-19 has shown that this technology has contributed to case tracking, epidemic surveillance, virus spread monitoring, precautionary measures, medical treatment, and drug developments \cite{Wu,HeWu}. Advanced technology and architectures have encouraged big data to solve various life problems and are currently unavoidably utilized to cope with the pandemic. Past studies have been conducted to control the spread of the virus through surveillance and contact tracing. Big data offers a solution to analyze human mobility and their contact data to identify confirmed cases and locations that the coronavirus may contaminate. Big data analysis on social media related to COVID-19 contributes to solving social life problems in gaining opinion, concern, and public response to the policies implemented \cite{albadawi60,alomari82,nguyen88}. Moreover, big data applications have benefited the quality of care by accelerating disease detection and providing better health care services to patients \cite{Ahir}. 

Many studies have been carried out since the discovery of the first case of the Coronavirus in Wuhan. This study aims to explore what research has been done regarding the pandemic and its impact, which utilized big data technology. The next part of this article contains several sections, starting with a methodology discussed literature review process and literature analysis. The review on Big Data research related to COVID-19 will be described in three concerns. The first review concern is the research contribution areas targeted by the studies. Based on the reviewed articles, we cluster the past studies into five areas: healthcare, social life, government policy, business and management, and the environment described in the "Research Contribution Area" section. The literature review undertaken emphasizes the technology offered by big data coping with the pandemic described in the "Analytical Techniques" section. In general, we found several methods and techniques used in big data research related to COVID-19. The section explains the methods and techniques along with the application built and the analysis carried out. The final concern of review from this study is the types of data sources and datasets used to support the application and analysis of big data related to COVID-19, described in the "Data Source" section. The conclusions of all reviews will be provided at the end of this article.

\section*{Methodology}
\subsection*{Literature Review Process}
A literature review aims to synthesize the previous research. The study collected and analyzed the relevant academic articles to identify how Big data technology can be applied to cover the challenges faced in overcoming the pandemic. The study intended to understand the current state of research facing the COVID-19 pandemic.  As a unit of analysis, we selected academic articles from the Scopus citation database with the searching key terms "Big Data" and "COVID-19" published from December 2019 until January 2021. The searching process applied the keywords throughout the title, abstract, and authors' keywords. In this study, Scopus was chosen because it has been identified that this citation database has a broader range of academic articles and most extensive in the engineering and management field \cite{grover, gavel, chadegani}. The selection process resulted in 446 articles that matched the searching key terms distributed in several areas, primarily computer science and medicine.  Further, we selected articles with inclusion criteria of journal, and proceeding articles in English language resulting in 258 articles. Among these studies, 223 articles were published in 2020, 35 articles were published in January 2021, while only one article was published in 2019. The systematic literature review process is shown in Figure \ref{fig:fig01}.

\begin{figure}[ht]
  \includegraphics[width=0.95\textwidth]{images/Figure1.png}
  \caption{Selection process of the literature review.}
  \label{fig:fig01}
\end{figure}

The qualitative analysis applied to the second criteria by reading the full paper form. In this stage, we excluded the articles that are not firmly related to the context and research questions and the articles that cannot be accessed in the form of a full paper. During this stage, 110 articles were filtered for final review. These articles were analyzed and distinguished into research articles and review articles. Articles discussing empirical studies that applied Big Data analytics using various techniques were grouped into research articles. We identified 92 articles as research articles, and the rest are review articles. The distribution of the articles selected for systematic review across journals is presented in Figure \ref{fig:fig02}. Selected articles encompassed different multi-discipline areas of studies determined by Scopus, including computer science, medicine, engineering, social sciences, environmental science, business management and accounting, material science, and energy.

\begin{figure*}[ht]
  \includegraphics[width =0.95\textwidth]{images/Figure2.png}
  \caption{Distribution of the selected articles across journals and research types.}
    \label{fig:fig02}
\end{figure*}

\subsection*{Literature Analysis}
After selecting the articles, we conducted a preliminary analysis to overview the themes concerned. Word Cloud is a valuable tool for document analysis by displaying text data visually and in an easy-to-understand manner. Word cloud allows us to understand the frequency of words in a document and compare the occurrence among words \cite{atenstaedt}. A word cloud approach was adopted to get the dominant topics of the articles reviewed. We applied world cloud techniques on the dataset containing all abstracts of all reviewed articles. Figure \ref{fig:fig02} illustrates the result of using a word cloud to the dataset in which the words were stemmed beforehand. Avoiding the key term "Big Data" and "COVID-19", the figure shows that the words: health, pandemic, disease, technology, model, and analysis are dominant.  For the preliminary examination, we get insight that the previous research on Big Data for COVID-19 explores more about healthcare technology. China, the country that reported the first case of COVID-19 globally, is the most-mentioned country in the articles.  Social issues related to pandemics are also widely discussed, in addition to health issues.
\begin{figure*}[ht]
  \includegraphics[width=0.9\textwidth]{images/figure3.jpg}
  \caption{Word cloud of the abstracts of the selected articles.}
  \label{fig:fig03}
\end{figure*}

Further, we analyzed the relationship and the co-occurrence among keywords. Semantic network analysis was applied to the keywords dataset for this purpose. Figure \ref{fig:fig03} presents the investigation results of applying semantic network analysis on the keywords dataset. It reveals that artificial intelligence, machine learning, and deep learning are the most used Big Data analytics methods mentioned in the keywords. Surveillance, infoveillance, and infodemics are key terms that appear with high dominance. The terms describe continuous activities comprising of systematic data collection, data analysis, and data interpretation towards an event related to health. These activities engage with public health measures in reducing morbidity and mortality and health improvement.

\begin{figure*}[ht]
  \includegraphics[width=0.95\textwidth]{images/figure4.png}
  \caption{ Keywords relationship of the selected articles.}
  \label{fig:fig04}
\end{figure*}

\section*{Research Contribution Area}
The articles were reviewed based on the objectives achieved. We classified the articles into health care, social life, business and management, government policy, and the environment. Out of the 92 research articles reviewed, some covered more than one area. The health care area includes 37 articles, 30 articles on social life, 20 articles on business and management, 12 articles on government policies, and 9 articles on the environment. Descriptions of contribution area and the number of related articles are presented in Table \ref{tbl:tbl01}.

\begin{table}[ht!]
\caption{Description of the contribution area and the number of related articles}
\label{tbl:tbl01}
\begin{tabular}{p{3.5cm} p{7cm}} 
\hline
Contribution Area  & Description\\
\hline
Health care (37 articles) & Covering medical science, medicine and pharmacology, epidemiology and health care services.\\
Social life (30 articles) & Covering behavioral sciences, psychology, and social change\\
Business and management (20 articles) & Covering hospitality and tourism, transportation, and finance.\\
Government policy (12 articles) & Covering policy and strategy, monitoring, and controlling.\\
Environment (9 articles) & Discussing how big data research assists the environmental sector, including the influence of the global climate on the spread of Covid-19, the impact of the pandemic on the environment (air quality and river pollution).\\
\hline
\end{tabular}
\end{table}

\subsubsection*{Healthcare}
The COVID-19 pandemic has caused many deaths and spread so fast. This virus spreads from person to person, and in a few months, the virus has been extended to various countries in the world with severe losses. Research and development have leveraged the advances in data science and big data technology for tracking and obtaining a fast and accurate understanding of pandemic spread trends to take more precise prevention and control measures.

Various studies related to virus transmission and its influence factors have been carried out to predict (a) the spread of the virus \cite{Wang25, ngie36, prakash45,rivai61,mavragani74}; (b) the person suspected of being infected {\cite{benrequia54}}; (c) new infection areas \cite{hussien30}; (d) the likelihood of the second and third waves of the epidemic \cite{tosi67}; (e) COVID-19 contamination scenario based on people movement \cite{nascimento24}; and (f) the increased number of cases \cite{alwaeli09}. COVID-19 task-force stakeholders can use these epidemiological predictions to prepare the necessary measures and policies.

Pandemic control is essential to prevent the spread of this outbreak from getting worse more widely. The literature suggests some official data sources issued by the government or agencies to be used to capture the evolutionary trajectory of COVID-19 \cite{liu72}, analyze infodemiology data for surveillance \cite{higgins75}, formulate case patterns \cite{dsouza47}, and arranging appropriate quarantines activities \cite{zhao05}. Apart from official COVID-19 data, health insurance data can also be used to analyze the risk of being exposed to COVID-19. For example, by tracking those hospitalized for pneumonia and not reporting to health authorities based on insurance claims data \cite{chen73}.

Monitoring in public facilities prone to the transmission of disease through person-to-person contact must also be considered. Disease transmission in multi-modal transportation networks can be estimated using traffic flow data and cases of COVID-19 incidence {\cite{zheng53}}. Therefore the density of transport passengers must be monitored and controlled {\cite{liu49}}.

Artificial intelligence and big data technologies are supporting medical science in fighting the pandemic. Previous studies attempted to improve the speed and accuracy of the medical diagnostics and find the best treatment methods for COVID-19 disease {\cite{jamshidi37}}. A diagnostic tool was developed for the early detection of COVID-19 pneumonia infection based on radiological images (pneumonic and non-pneumonic x-rays) \cite{mishra44}. Optical Char-
acter Recognition (OCR) technology supported by deep learning can extract image data into text data to classify and itemize medical images \cite{kim08}. A combination of some clinical variables can predict whether COVID-19 patients require ICU admission \cite{izquierdo68}. Research to find effective treatments without harmful side effects is still ongoing in pharmacology and medicine. An analysis of chloroquine derivatives proved that these derivative medicines effectively improve clinical outcomes and reduce mortality for COVID-19 patients \cite{million65}. Furthermore, data from the Korea National Health Insurance Service show that patients who claim antihypertensive medicine tend to have a lower risk of exposure to COVID-19 \cite{kim70}. 

Smart medical technology can be applied to develop IoT applications in the health area. An application that utilizes mobile devices was designed to access information on people’s health conditions dynamically.  This application supports health care professionals to monitor public health remotely. Clinical symptoms of COVID-19 infected people can be detected by smart wearable gadgets \cite{ashraf41, sambhav}. A smartwatch can monitor the movement of COVID-19 patients \cite{kallel04}, and their health parameters (such as heart rate, blood pressure, blood oxygen), providing  COVID-19 signals to paramedics sent through mobile applications \cite{nascimento24}. Past studies showed that many infected people are asymptomatic. The technology detects the extent of this viral infection of asymptomatic people \cite{bo28}.

\subsubsection*{Social Life}
The corona pandemic has affected the economic sector and caused many social problems \cite{liu34, varotsos42}. Public opinion and concerns towards pandemics are interesting to be investigated in this health emergency condition. A massive amount of data available on social media can be used to study this issue \cite{sari10,kim22,zhang35,fiadhi58,xue66, nguyen88,jung91, katapally}. Big data analytics can reveal the public reaction to some government policies and recommendations about the lockdown policy, work from home, and social distancing guidelines \cite{suratnoaji78,wrycza14,haupt03}. User-generated content (UGC) in social media can be extracted to detect critical events and public response to government measures in tackling the pandemic \cite{alomari82}. Social media conversation can also be utilized to expose COVID-19-related symptoms and experiences on disease recovery \cite{mackey76}. 

Moreover, the adherence to home confinement during the pandemic to maintain physical distancing and avoid crowds can be monitored through an activity tracker device analyzing the effect of physical interaction limitation policy on people's activity \cite{pepin71}. The adherence to health protocol can be inspected from the video data obtained from the camera device \cite{melenli11,li27}. Analysis of people's geolocation can scrutinize the human mobility changes, and contact tracking \cite{Hu01,jiang38, beria39, arimura40}.

COVID-19 has increased the anxiety of many people and can cause mental health problems. Studies on COVID-19 discussed social psychology, examining people's behavior in social situations and their capability to adapt to certain conditions' social environments {\cite{shang43}}. Research topics in social psychology covered in the past studies include the relationship between trust and the presence of infectious disease \cite{ye77}; psychological needs and their satisfaction level during the pandemic \cite{long31};  the effect of fear and collectivism on the public prevention against COVID-19 \cite{huang62}; peoples' preferences to protect the environment they live in {\cite{gang59}}. Some of the harmful effects of the pandemic have been studied, including family violence {\cite{albadawi60}}, increasing racial sentiment toward Asian people {\cite{nguyen88}}, the emergence of incivility and fake news on social media \cite{gonzales02,Wang25}, and emotional tendency and emotional symptoms of mental disorder facing the outbreak \cite{zhang80,sun15}.

\subsubsection*{Government Policy}
COVID-19 attacks almost all sectors of life and is a burden on the government. Various policies and scenarios must be implemented to control the COVID-19 increasing case rate overcoming this outbreak. Big data analytics can be useful for the government in making decisions and policies. Some of the government's policies to limit community activities are working from home \cite{wrycza14}, lockdown, and confinement at home.  The decision to lock people at home is necessary to disinfect areas with high contamination levels {\cite{benrequia54}}. The lockdown policy during a pandemic has made people more restrict themselves for treatment or continuing their routine treatment. This is indicated by a drastic decrease in total health care expenditures based on bank transaction data \cite{zhang64}.

COVID-19 attacks almost all sectors of life and is a burden on the government. Various policies and scenarios must be implemented to control the COVID-19 increasing case rate overcoming this outbreak. Big data analytics can be useful for the government in making decisions and policies. Some of the government's policies to limit community activities are working from home \cite{wrycza14}, lockdown, and confinement at home.  The decision to lock in people at home is necessary to disinfect areas with high contamination levels {\cite{benrequia54}}. The lockdown policy during a pandemic has made people more restricting themselves for their health treatment or continuing their routine treatment. The previous research has shown that the total health expenditures decrease indicated by the reduction of the bank transaction related to health services \cite{zhang64}.

Scenario policies can differ in each region depending on the COVID-19 conditions and environmental and climatic factors \cite{eksinchol17}. Population-based strategies based on ecological predictors can be used to reduce the risk of spread \cite{quynh89}.  And in the tourism sector, the government has initiated an intelligent contact tracking system to limit tourist visits to avoid contact from potentially infected tourists \cite{chen73,Liu}. Moreover, that rational decision-making needs to be done to find new tourism potentials \cite{wang50} and improve conditions of hotel industry  \cite{wu90}.

The implementation of public policies still has to be analyzed to see how the policies affect the spread of disease \cite{sirinaovakul18}. The government's key actions were evaluated to make the next policy-making becomes more appropriate\cite{hua33}. Government directs the regulations and policies to monitor and control the number of infections. Optimization of monitoring techniques in infection areas is necessary to support the goal \cite{kumar19}.


\subsubsection*{Business and Management}
The business sector has faced many obstacles during the pandemic. It is identified that many factors influence strongly on the survivability of the business.  Chaves et. al \cite{chaves84} develop a prediction model to measure the probability of entrepreneurial survival and business success based on environmental variables and public support programs by applying artificial neural networks. Entrepreneurs must be agile to anticipate the changes in customer behavior. Pandemic has shifted the customers' behavior and buying pattern in this uncertain business environment \cite{gonzales02}. Zhang Y et al. \cite{zhang64} developed a model for figuring out healthcare products and utilization to get insight into customers' shifting of healthcare needs.

Entrepreneurs had to adjust the strategy and practices to survive and stay competitive. Digital transformation is one solution and has been accelerated during the pandemic. Both parties of customers and entrepreneurs moved to benefit online channel. Online platforms that facilitate the providers and customers have enhanced intelligent services like a product recommendation to improve online customer experience \cite{shahbazi21,almaslamani48}. Continuous observation of product or service quality regarding user engagement is essential to keep business going in this hard situation \cite{cano85,he87,fedushko06}. Increasing health product needs has led to fraud in supplying products.  Health product providers need to protect their customers from illicit products by applying intelligent fraud detection\cite{mackey69}. 

The outbreak has not only impacted all businesses but has also weakened the pace of investment. Investment portfolio is volatile due to the effect of panic investors \cite{liammukda16}. Some investors hold their stake, and some others take advantage of this situation. Sentiment analysis and time series regression are applied to predict the future condition of the stock market {\cite{lee92}}. Zhang B. et.al \cite{zhang83} investigated the effect of investors' attention to stock market movements.

The tourism and hospitality sector Pandemic have been impacted significantly by the outbreak. Big Data analytics can reveal the impact of the pandemic on this market from various tourism data. Getting valuable insight from data-driven analysis, entrepreneurs and Government can make rational decisions to formulate the right tourism strategy and policy {\cite{wang57, wu90}}. Tourism behaviors have changed in response to the new Government tourism policy {\cite{sung81}}. Rejuvenation of the tourism area needs to be done by exploring the potential of the existing tourism for COVID-19-appliance tourism development \cite{wang50}. Aside from tourism behavior, passenger, and traffic behaviors have also changed \cite{nimpattanavong12}. The changes are essential to be scrutinized to control the contamination risk at the airport and on the plane as well \cite{liu49,zheng53}.


\subsubsection*{Environment}
People have changed their lives during the pandemic.. Their activities must adapt to the situation to inhibit the coronavirus's spread. Apart from impacting people's way of life, the pandemic also has an impact on the environment. Previous research has explored to what extent the pandemic changes the earth's condition. Lin et al. \cite{lin79}, and Ibrahim et al. \cite{ibrahim29} figured out the meteorological factors that influence coronavirus transmission. Environmental predictors that influence the COVID-19  can be determined by surveillance of the infected area's street view image {\cite{quynh89}}. Spatiotemporal data can reveal the distribution pattern of PM2.5 air pollution during the pandemic \cite{li26}. Using Big Data, the exposure of PM2.5 and its evolution can be identified and further used to assess the potential health risk {\cite{he55}}. Besides air pollution, researchers have paid attention to water pollution. Yan \cite{yan32} proposed a reference model to prevent and control river pollution by applying microbial treatment technology. The technology can be utilized to reduce river pollutants after the outbreak.

More about the environment during the pandemic, lockdown policy, and mobility restrictions have reduced road traffic globally. The reduction implies decreasing world gas emissions. Research on Big Data has quantified the impact of this traffic reduction on air quality based on meteorological and road mobility observations \cite{gualtieri86}. The data of road traffic reduction can be used to predict energy consumption during the pandemic \cite{wang52}. The outbreak has changed people's behavior to choose healthier transportation. Shang et al. \cite{shang43} exposed that the use of bikes increases environmental benefits regarding emission reduction and energy conservation.


\begin{figure*}[ht]
  \includegraphics[width=0.9\textwidth]{images/Figure5.png}
  \caption{Knowledge mapping of the methods and application of the COVID-19 research}
  \label{fig:fig05}
\end{figure*}

\subsection*{Analytical Techniques}
This research emphasizes exploring the advancing Big Data technology solutions conducted by previous research in fighting the COVID-19 pandemic. This section highlights computational methods that can facilitate Government and other social organizations in figuring out the current state of the COVID-19, forecasting the spread, and predicting the socio-economic impact on people and society. All the research is intended to respond to the coronavirus threats and reduce the pandemic's risks. Available methods adopted from previous experiences are exposed to cover many aspects to recover the condition. Our survey identified that various machine learning algorithms are the most used method in COVID-19 research in addition to statistics and mathematics. Deep learning, a branch of machine learning (ML) that uses deep neural networks to solve problems, is also a sophisticated solution to tackle more significant problems with greater ease and efficiency due to its advanced feature engineering. Figure \ref{fig:fig05} presents the methods used in the past studies concerning applications underpinned.

Machine learning is an artificial intelligence (AI) approach with the ability to learn and improve automatically from the experiences in processing data. Machine learning accesses data and uses it for self-learning in building a computational model. Machine learning methods can be distinguished into supervised learning, unsupervised learning, and reinforcement learning. A supervised learning approach is a learning method that uses the information to get the proper insight from a set of data by learning the mapping between outputs and related inputs. This method retains the input/output and process to build a mathematical relationship model that can make predictions or classification based on an existing dataset, called training data.
Besides, the unsupervised learning approach does not use training data to find any observable dataset pattern. Based on the mathematical model, this algorithm does not have any target variable. One of this algorithm's goals is to group objects in the same area based on their proximity. Unlike the two-stated learning approach, reinforcement learning is intended to enable computers to learn on their way from the environment through an agent. This agent, a single authority, can understand the behavioral changes of the domain. Thus, the system will do self-discovery by interacting with the environment and responding to the changes \cite{hussain}. 
Exploring the methods and techniques of the COVID-19 research, the study identified that the past research generally exposed the data mining approaches of regression, classification, clustering, association, and social network analytics. Statistical analysis is also used widely, and we discern them in the discussion into the descriptive and inferential analysis. Special issue of SEIR (Susceptible, Exposed, Infected, and Recovered) mathematical model of disease spread will be discussed in the next paragraph, followed by IoT and other Big Data applications.


\subsubsection*{Classification}
Classification is a supervised learning approach that produces a model for determining an individual belongs to a particular class. For this purpose, many categories or classes are defined in the early stages of model building. The classes are usually mutually exclusive. Regarding COVID-19 research, deep learning using techniques of RNN (Recurrent Neural Network) and LSTM (Long Short Term Memory) are used to classify the Pulmonary Function Test (PFT) image data. Optical Character Recognition technology was applied to extract text data from PFT data for classification purposes \cite{kim08}. To determine suspected cases and areas, cell-phone Spatio-temporal data can be processed using a decision tree algorithm for classification \cite{hussien30, rivai61}. An application of artificial intelligence was developed to determine the diagnosis and treatment of the COVID-19 disease for high-risk groups. The application adopted several algorithms, including  Extreme Learning Machine (ELM), Generative Adversarial Networks (GANs), deep learning techniques RNN and LSTM using clinical data and medical images {\cite{jamshidi37}}. 
 
Sun et al. \cite{sun15} developed a psychological computing model to identify the continuous emotional symptoms of mental disorders caused by the epidemic. This mental health recognition application performs visual analysis and considers speech and facial expression images as multimodal data. The application explores a relationship between short-term basic emotions and long-term complex emotions. This emotion-sensing model used Bi-directional LSTM and Three-Dimensional CNN in building the model. Further, people's psychological needs during the pandemic in a particular area can be observed from user-generated content posted on Twitter. Long et al. \cite{long31} applied Natural Language Processing (NLP) and Support Vector Machine (SVM) algorithm to research this subject. A similar technique was utilized to investigate the shifts in anti-Asian racial sentiment regarding the emergence of COVID-19 \cite{nguyen88}. Mackey et al.\cite{mackey76} conducted infoveillance research on Twitter and Instagram to expose counterfeit COVID-19 health products and characterize in terms of product types, selling claims, and sellers types by combining Fine-tuned-pre trained LSTM and Bi-Term Topic Modeling.  
 
A computer vision application that detects objects and distances among things was developed using the Kubeflow machine learning platform and OpenCV library to analyze crowd conditions from the video streaming data \cite{melenli11}. Still, in attempting to monitor and enforce the health protocol adherence, an application of face recognition was developed by adopting CNN (Convolution Neural Network) deep learning to determine if someone is wearing the mask or not \cite{li27}. 
 
A classification learning technique of MLP (Multi-Layer Perceptron) could be applied to predict the resilience of entrepreneurs facing the pandemic. Five clusters of entrepreneurs were categorized into three classes: success, survive and fail. The study used SOM (Self-Organizing Map) for the clustering \cite{chaves84}. CNN was also applied to determine the industry category based on the economic indicators using a single and hybrid database {\cite{wang57}}. Sentiment analysis complemented with regression was used in several studies to predict the stock market movements during the pandemic \cite{jing51,lee92}.


\subsubsection*{Clustering and Topic Modeling}
As an unsupervised learning approach, clustering group entities in a population-based on their similarity. K-means algorithms integrated with correlation techniques can be employed to cluster the countries based on their stages in facing COVID-19 and then examine the relationship between their public policy and the spread of diseases \cite{sirinaovakul18}. Hussien et al. \cite{hussien30} used K- Means clustering to allocate positive case areas and classify the risk status using decision trees algorithms. The K-modes clustering algorithm, the extended version of K-means, was used to help physicians group the patients to get insight into their health and the treatments that might be needed. Then, chronic disease distribution amongst clusters can be scrutinized \cite{batarseh07}. K-means clustering can be employed to allocate infected areas, classify a person's risk in an area using decision tree algorithm \cite{liu49}, and identify the coronavirus spreading \cite{rivai61}. Hierarchical clustering was applied to identify the actual groups of infected COVID-19 \cite{kumar19} and the effects of chloroquine derivatives in patients, based on medical articles \cite{million65}. 
 
Bi-Term Topic Model (BTM), a topic clustering model, was applied to analyze Twitter microblogging (tweets) to identify the Government's social distancing guidelines' public pros and cons. Combined with social network analysis, the study investigated the networked structure of the Twitter communities' communication dynamics \cite{haupt03}. A survey on public opinions on remote work during the pandemic used the K-means algorithm to cluster tweets to identify shared concerns. Naive Bayes sentiment analysis to get the tendency of the clusters \cite{wrycza14}. Some studies revealed hidden themes from the Twitter dataset to explore the public concern to some pandemic issues using the topic cluster model of LDA (Latent Dirichlet Analysis) \cite{alomari82, xue66}.

 
\subsubsection*{Association and Semantic Network Analysis}
Association is unsupervised learning that aims to find the relationship between entities from a large dataset. An application of association for COVID-19 was used Frequent-Pattern growth (FP-growth) algorithm to analyze the relationship among various diseases and the complications, cover other possible complications, and explore the relationship between complications and causality. Almaslamani et al. \cite{ almaslamani48} developed an association rule algorithm based on cosine similarity to identify customers' shopping behavior by examining associations between items purchased on their shopping cart. 
 
Semantic Network Analysis (SNA) is generally used in text mining to analyze social media data. To explore the public opinion represented by Twitter users about COVID-19, SNA was used. A study on figuring out the incivility factors on social media was conducted using mixed SNA with binary logistic regression classification \cite{kim22}. SNA technique can be utilized to explore social behavior and social changes. Sung et al. \cite{sung81}employed SNA to explore travelers' perceptions and interests after the extensive spread of COVID-19. Centrality analysis and convergent correlation analysis were equipped for this semantic network analysis in this study.

 
\subsubsection*{Regression and Time Series Forecasting}
Regression is used to estimate value based on several variables and determine the causal relationship between one variable and other variables. In comparison, time series forecasting is a technique for predicting events concerning the time sequence. This technique predicts future events by analyzing past trends, assuming that future trends will be similar to historical trends. A study on COVID-19 has applied a regression model to predict infected cases and compared the model with another prediction model of ANN \cite{prakash45}. ANN is also used to indicate the spread and the peak number of COVID-19 cases. Differential Private ANN was developed to make predictions with the feature to protect individual data privacy. This extended model has proven that by introducing laplacian noise at the activation function level. The model gives results that are similar to the base ANN \cite{sinha20}. A study on the spread prediction model was performed by creating an ensemble model from the Decision Tree and Logistic Regression models to develop a tree-based regressor model to gain higher accuracy \cite{ngie36}. Ye and Lyu \cite{ye77} studied the impact of trust and risk perception on the infection rate. Multilevel regression was applied in the study to determine the city and province-level analysis.  Multiple regression was adopted to observe the preventive intention based on social media data. The study proved that fear and collectivism positively impact community prevention intentions but reduce each other's positive influence on community prevention intentions \cite{huang62}.

Lee {\cite{lee92}} exploited the impact of COVID-19 sentiment on the US stock market differentiated by industries. The study developed time series regression models and used the data from Google Trends on coronavirus-related searches and daily news sentiment index for the analysis. Another study on the stock market has also employed a regression model to reveal the impact of investor attention and the number of media reports about masks on the rate of return of 40 mask concept stocks \cite{zhang83}. Further, we identified several studies on time series prediction, including energy consumption \cite{wang52} and electricity consumption prediction \cite{alvarez23}.

 
\subsubsection*{Descriptive and Inferential Statistics}
The study of human mobility during the pandemic has been conducted by taking into account three fundamental metrics of trip per person,  person-miles traveled, and proportion of staying home. Based on these metrics, the effect of policies across regions under diversified socio-demographics was observed. In this study, a Generalized Additive Mixed Model (GAMM) was generated for inferential analysis, and the results were compared with ones from other models \cite{Hu01}. Still, about human mobility, the flight traffic behavior was monitored for countries worldwide to examine the relationship between the number of flights and the COVID-19 infection. For this purpose, descriptive statistics were used in this study \cite{nimpattanavong12}. Descriptive analysis expanded with repeated-measures analysis of variance (ANOVA), and correlation analysis was implemented to study the hotel industry's turbulence impacted by COVID-19 {\cite{wu90}}. 
 
More about the use of descriptive statistics, a past study has discovered the correlation between the incidence of COVID-19 and search data provided by Google Trends. Afterward, the regression lines can be derived to predict the evolution of the COVID-19 pandemic \cite{tosi67}. A similar study was conducted using Pearson correlation and ARIMA (Auto-Regressive Integrated Moving Average) to reveal the relation between Google Trends data and COVID-19 cases \cite{mavragani74, higgins75}.  Descriptive statistics were further employed to exploit the effect of lockdown on people's activities represented by the number of steps per day regarding the adherence of staying at home policy \cite{pepin71}. The lockdown policy has led to lowering the road traffic trend. Gualtieri {\cite{gualtieri86}} observed the impact of road traffic on air quality in several urban areas. The analysis has taken into account the time series of traffic mobility to reveal the association among meteorological parameters, road traffic, and pollutant concentrations. Some other research on the air quality, the pollution risk, and health city conditions during the outbreak was conducted using various statistical descriptive techniques \cite{cano85,he87,li26,liu34}. 
 
Study on the evaluation of eco-tourism resources employed PCA's statistical technique (Principal Component Analysis) to diminish the indicators to develop the tourism index system. The method was integrated into the AHP (Analytical Hierarchy  Process) in generating an evaluation index system of urban tourism competitiveness intended for tourism development facing the pandemic \cite{wang50}. PCA was also applied for evaluation of online service-learning that is distinctively raised during the outbreak. It is used to develop a user-engagement score system and then discovered the association of the score with the number of subscribers and their reviews by applying the Pearson correlation technique \cite{fedushko06}. Another statistical analysis performed by past studies was DID (Difference-In Difference) techniques. DID technique was employed to identify the effect of the medicine on the risk groups of COVID-19 \cite{kim70} and the individual changes in health care utilization from different risk groups \cite{zhang64}.



\subsubsection*{SIR Mathematical Model}
The prediction and control of disease spread can be analyzed using mathematical models for infectious diseases. The SIR model (Susceptible, Infected, and Recovered) is a mathematical model which is one of the core epidemiological models. It is a basic statistical tool for analyzing infectious disease outbreaks with more specificity in modeling population subsets for accurate forecasting {\cite{shorten}}. This model can be extended to an SEIR model by including various sizes of the Exposed (E) population and more detailed data.
 
The prediction of the epidemic situation based on COVID-19 data was carried out by Wang R et al. \cite{Wang25} by comparing several models. The optimized SIR model uses the least square and particle swarm optimization method and the classical logistic regression model. Prediction of the number of patients was performed after obtaining a trained model or parameter estimation. The logistic regression model provides results that are more in line with actual conditions than the SIR model based on particle swarm.

\begin{figure*}[ht]
  \includegraphics[width=0.6\textwidth]{images/SIR1.png}
  \caption{SIR and SEIR model}
\end{figure*}
Liu M et al. \cite{liu72} developed the SEIR model for capturing the trajectory of COVID-19 evolution in Wuhan using various assumptions. The assumption is that Susceptible (S) people who 'move in' to Wuhan are Susceptible (Sin), people who 'move out' from Wuhan are Susceptible (Sout) and Exposed (Eout). And people who are exposed without symptoms are Infectious (I). The two components for infected people are hospitalized and quarantined (Ih) cannot infect people outside the hospital. Those who remain in the community and are not hospitalized (Io) will spread the disease. Individuals of these two components were included as recoverable (Rr) and death case (Rd). This model considers influencing factors such as city closures, shelters, and the addition of new hospitals, resulting in an adequate forecast of the peak, size, and duration of the epidemic.
\begin{figure*}[ht]
  \includegraphics[width=0.6\textwidth]{images/SIR2.png}
  \caption{Modification of the SEIR model\cite{liu72} }
\end{figure*}

The SEIR model was developed by Isarapong \cite{eksinchol17} to estimate pandemic conditions by adapting the actual COVID-19 data for each province in Thailand. The SEIR model is modified by dividing the Exposed (E) Phase into E1 for exposed, non-infectious, no symptoms, and E2 for exposed, pre-symptomatic, infectious. The infectious (I) phase is divided into Is for infectious, symptomatic, and Ia for infectious, asymptomatic. This model starts from Susceptible (S) to E1. There are two possible routes to the Recovery (R) phase from E1: E2-Is-R and Ia-R. Apart from considering the different recovery rates and transmission for each province, this model also considers the mobility factor between areas that can contribute to the spread of disease to other places.
\begin{figure*}[ht]
  \includegraphics[width=0.75\textwidth]{images/SIR3.png}
  \caption{Modification of the SEIR model \cite{eksinchol17} }
\end{figure*}

\subsubsection*{IoT and Other Big Data Application}
The IoT system integrates several components, consisting of sensors/devices that send data to the cloud through several connectivity types. The application software will process the cloud data to produce output such as alerts or adjusting sensors/devices automatically. IoT technology provides the solution for various problems, especially concerning activities that need to be monitored and controlled remotely. IoT technology related to COVID-19 big data research is mainly carried out in health area. The use of smart devices connected to the patients so that their conditions can be monitored remotely by medical officers/doctors in real-time via a mobile application.
 
The digital transformation for the public health care system is carried out by Nascimento et al. \cite{nascimento24} by adopting a fog environment that connects several local devices and connects to the cloud infrastructure via a communication network. It is stated that this environment will improve the quality of the data to be uploaded to the cloud. Meanwhile, a new IoT-fog-cloud-based architecture is proposed by [4] for the monitoring system for autism and COVID-19. It is stated that the proposed architecture has several advantages: it can handle IoT data flow processes in real-time, data integrity in a multi-tenant environment, and applies business processes to the cloud and the appropriate cloud resources.
 
Ashraf et al. \cite{ashraf41} introduced a strategy of layered edge computing mechanisms to identify medical health status and track people suspected of being infected with COVID-19. This layered mechanism helps reduce the system delay factor and get a quick response.  Data computing and rule-based analysis at the cloud layer will compare sensor data from several edge layers with predetermined conditions. According to the appropriate action trigger module, notifications, awareness, recommendations, and assistance will appear on the application layer.
 
Efficient control of the pandemic spread by isolating and disinfecting rapid suspicious sites is offered by \cite{benrequia54}. Proposes a big data architecture that automatically and continuously collects geolocation data from people's outdoor activities via IoT devices. The infected person is the main target for finding all the individuals who that person may have infected. In anticipation of data loss due to internet connection problems, data is collected locally on the device and sent to the system when it is reconnected to the network.
 
Another application that uses a big data approach is smart power grids. Government policies to stay at home or lock themselves in have affected electrical energy consumption in areas affected by COVID-19 and in industrial cities. A more resilient smart grid analysis through a big data-based approach using a smart grid semantic platform was carried out by Bionda E et al. {\cite{bionda56}}. The research shows the smart grid can manage sudden anomalies by updating the load profile based on forecast data in the medium short term.

\subsection*{Data source and dataset}
Big data is classically characterized by "4Vs", representing: (1) Velocity refers to the speed of data transfer and processing; (2) Volume point out the fact that a huge amount of data are now produced and available every time; (3) Variety represents the number of data sources that provide data in various types and formats; (4) Veracity concerns with the accuracy and the validity of data \cite{jia, bragazzi}. Big data analytics is advanced analytic techniques to extract knowledge from a huge volume of various data. The skyrocketing amount of data and the advance of computing technology have accelerated big data applications in processing large data stored in a distributed file system. 

Several applications developed related to COVID-19 examine massive amounts of data on several distributed servers, requiring a supporting storage system.The existence of a cloud network will provide higher performance for a large dataset \cite{zheng}. Distributed NoSQL database technology has scalability, flexibility, and high performance, which is considered most suitable for processing big data. This database system is non-relational, so it can manage databases with a flexible schema and does not require complex queries. Some of the NoSQL database technologies used in the reviewed articles include Cassandra, MongoDB, Hbase, and Neo4j as shown in Table \ref{tab:tab02}. Obtaining information insights requires multiple perspectives from a large amount of data from various sources.

MySQL and PostgreSQL are relational database management systems, where the data search process is linear with the amount of data held. The greater the volume of data requires certainly more execution time for the search process. Regarding the management of big volumes of data that the server may become overload and possibly cause bottlenecks; the data needs to be integrated into a big data library framework (Apache Hadoop software library) as Sirinaovakul et al. \cite{sirinaovakul18}, Bo Y \cite{bo28} and Nimpattanavong et al. \cite{nimpattanavong12} have done in their study. Partitions are one of the framework's main features. The feature can distribute data to predefined partition nodes adjusted to business requirements. Hence, the query process on massive data remains reliable. 

Hadoop is a big data framework managing distributed storage systems that enable access and processing of an immense volume of data. The principle is a cluster of nodes, where one cluster coordinates many nodes, and each node has its own data storage and processing. This technology provides a solution for relational databases to manage large volumes of data. The data are transferred from relational databases to Hadoop and vice versa through the Apache Sqoop (SQL(Structured Query Language) to Hadoop) tool {\cite{grover}}. Apache Kafka is an open-source streaming platform in the middle layer that separates data streams and transmits them in real-time to Hadoop big data lakes, applications, and systems analysis. Kafka is usually used for data streaming, website activity tracking, and real-time analytics, as was done by Albadawi \cite{albadawi60} and Zhang X et al. \cite{zhang35} in their study on analyzing Twitter data.

\begin{table}[ht!]
\centering
\caption{Databases technology with their applications used by previous research}
\label{tab:tab02}
\begin{tabular}{p{2cm} p{5cm} p{4cm}} 
\hline
\multicolumn{1}{c}{Technology} & \multicolumn{1}{c}{Description} & \multicolumn{1}{c}{Applications}\\ 
\hline
Cassandra & Distributed NoSQL databases designed to handle large amounts of data spread across multiple servers & Smartwatch for monitoring system \cite{kallel04}. \\
Databricks & Cloud-based data engineering tool used for processing and transforming massive quantities of data under Apache-Spark based platform. & Analysis of the needs of the distribution system operator for the electricity grid \cite{bionda56}.\\
Hbase & An open source non-relational distributed database system, column-oriented capable of processing large-scale data and is built on top of the Hadoop Distributed File System (HDFS). & Video streaming data analysis \cite{melenli11}.\\
Kafka & An open-source distributed event streaming platform used for high-performance data developed by the Apache Software Foundation. & Twitter sentiment prediction  \cite{zhang35,albadawi60}.\\
Neo4j & An open source graph database management system developed by Neo4j, Inc. & Insight-driven learning (IDL) for healthcare \cite{fiadhi58}.\\
MongoDB & Document oriented, NoSQL, cross-platform distributed database to store data in JSON-like documents & Healthcare monitoring remote system \cite{nascimento24}. Classification of residents’ psychological needs \cite{long31}. Detection of public concern \cite{alomari82}\\
MySQL & An open-source relational database management system to store structured data under the licence of GPLv2 or proprietary & Analysis of flight traffic behavior \cite{nimpattanavong12}. Analysis of health data \cite{bo28}.\\ 
PostgreSQL & An open-source relational database management system to store structured data under the licence of PostGreSQL for free and open-source of permissive. & Cluster analysis to identify data patterns of the public policy implementation \cite{sirinaovakul18}\\
\hline
\end{tabular}
\end{table}

The literature review shows that research on big data related to COVID-19 uses a wide variety of data sources available publicly or privately. We categorized the data sources into six classes: government official data, institutional service data, IoT generated data, online media data, public/open data, and others \cite{jia}. Table \ref{tab:tab03} presents the dataset used in the previous research. It is shown that Government official data on COVID-19 cases and social media data are the most widely used in the research. 

Johns Hopkins Coronavirus Research Center data and statistics are the main references for the COVID-19 pandemic, in addition to WHO official data. The data expose the situation of viruses spread by country, territory, or area. This data is beneficial for policymakers and researchers to monitor and carry out various activities to control the spread of this deadly virus \cite{lin79,sirinaovakul18,ngie36, higgins75}. The government of each country is updating their COVID-19 case data daily to officially inform the public about the official data of infected, recovered, and died, regional risk zones, and distribution of cases and transmission \cite{prakash45, tosi67}. This information is useful for people to understand the conditions of the pandemic and to take preventive actions. In addition to medical data, which is certainly widely used in past research, the data of transportation, tourism, and industries have also become the concerns in the research \cite{wang50,zheng53,lin79,gonzales02}. 

IoT technology makes it possible to dynamically access big data generated from sensing devices available in real-time. IoT data is collected from a variety of devices and sensors, such as GPS, CCTV, cameras, smart/mobile devices, and monitoring devices, to be processed. The advanced technology of IoT and smart devices has driven the development of intelligent systems that can monitor human mobility, geolocation of suspected and infected people, health condition, and health protocol compliance, see Table \ref{tab:tab03}. The use of smart wearable devices, which have now become daily necessities, makes personal physical health data easier to be obtained and monitored \cite{ashraf41,nascimento24, pepin71}. Not only public health conditions, IoT research also pays attention to the changes in environmental quality and energy consumption due to the human behavior shifts during the pandemic \cite{he55,li26,yan32}. Applying IoT and artificial intelligence, we can analyze the natural conditions in real-time \cite{peipeng}.

Social media contributes significantly to the development of big data. Pandemic has forced people to limit their mobility and direct human contact. This situation makes social media the preferred means of communication for human interaction. Social media have connected and supported awareness and pandemic updates. User-generated content in social media can be explored to obtain public information regarding the pandemic. Microblogging platforms such as Twitter and Facebook are the most social media network platform that supports the research on COVID-19in revealing public opinion \cite{suratnoaji78,alomari82, mackey76}, public concern {\cite{hua33, huang62}}, and psychological condition towards the pandemic \cite{nguyen88,long31, xue66}. Users' comments on social media can be analyzed to scrutinize people's behavioral changes due to the outbreak from many perspectives \cite{sung81}.

Public or open datasets are used to handle various research on COVID-19. The stock market, weather, and climate data are identified in the previous study. Misra M et al. {\cite{mishra44}} developed a model for early detection of COVID-19 using a pulmonary X-ray image dataset available for public use. Image data of people wearing masks are used to detect the compliance of personal prevention \cite{li27}. Sun et al. \cite{sun15} develop a model for exposing people's mental health using audio and video data.

\section*{Conclusion}
We have discussed how big data technology contributes to tackling the COVID-19 outbreak. COVID-19 has induced many problems in various sectors of life for humanity around the world. To capture the landscape of the COVID-19 research, reviewed articles were categorized into contribution research areas that appeared substantial in previous big data research. Methods and techniques were discussed to show the role of big data analytics in solving the problem and their contribution to the body of knowledge. The analytical techniques refer to computational domains, including machine learning, deep learning, statistical analysis, and mathematical analysis. Artificial intelligence fields of computer vision, remote sensing, the internet of things, and natural language processing have testified to solve COVID-19 problems. We address data sources with different data types used in the past study to guide future research in developing the data-driven application for COVID-19.

Big Data technology has demonstrated its significant role in tackling COVID-19. We identified that the previous studies had contributed mainly to research areas of healthcare, social life, government policy, business and management, and the environment. Many analytical techniques have been applied for handling many issues, including epidemic surveillance, medical treatment, social changes, consumer and market behavior, and the effects of the pandemic on earth systems. There are still many challenges ahead in dealing with COVID-19. The emerging new variants, vaccine effectiveness and side effects, relaxation of health protocols, and new normal challenges are issues to be resolved in the future. We hope that this survey will give insights into current states of knowledge on Big Data technology for COVID-19 and references for further development or starting new research.

\begin{landscape}
\begin{table}[ht!]
\caption{Data source and dataset}
\label{tab:tab03}
\begin{tabular}{p{5cm} p{7cm} p{6cm}} 
\hline
\multicolumn{1}{c}{Data Source} & \multicolumn{1}{c}{Dataset} & \multicolumn{1}{c}{References} \\
\hline
\multicolumn{3}{c}{Government Official Data} \\
\hline
\multirow{2}{*}{Public Health} & COVID-19 cases and events & \cite{zhao05,batarseh07,eksinchol17,kumar19, prakash45,dsouza47,jimenez63,zhang64, tosi67, liu72, ye77}\\
& Clinical, demographical, and laboratory data & \cite{jamshidi37,lin79, batarseh07, kim08} \\
Insurance Services & Medical prescription and Health insurance claim & \cite{kim70, chen73} \\
Energy Consumption & Electricity and total energy consumption & \cite{alvarez23,bionda56,wang52} \\
\multirow{3}{*}{Transportation} & Daily health condition of passenger & \cite{liu49}  \\
& Traffic flow and Traffic density & \cite{zheng53,lin79,shang43,gualtieri86} \\
& Residential car park & \cite{jiang38} \\
Tourism & Urban tourism data & \cite{wang50} \\
\hline
\multicolumn{3}{c}{Institution Official Data} \\
\hline
WHO, John Hopkins, and ECDC & COVID-19 global cases & \cite{lin79,ngie36,sirinaovakul18,ibrahim29, higgins75} \\
Bank cards transactions & Healthcare expenditure & \cite{zhang64} \\
Andalucia Emprende Foundation & Entrepreneur data & \cite{chaves84} \\
Yale Industrial Economic & Industry statistical data & \cite{wang57,he87} \\
Mob-Tech Research Institute & Internet usage data & \cite{hua33} \\
International hotel chain & CRM  public data & \cite{gonzales02} \\
European Society of Radiology & Covid-19 negative X-ray images & \cite{gonzales02} \\
\hline
\multicolumn{3}{c}{IoT Data} \\
\hline                                                     
GPS data & Position location & \cite{chen73} \\
Camera Video & Video streaming & \cite{melenli11} \\
\multirow{3}{*}{Smart/Mobile Devices} & Human mobility and human steps records & \cite{arimura40,Hu01,kallel04, pepin71} \\
& Physical health records & \cite{ashraf41,bo28,nascimento24}\\
& Geolocation of suspected/infected & \cite{benrequia54,hussien30, chen73} \\
Monitoring devices & Water quality and PM2.5 concentration & \cite{he55,yan32,li26} \\
\hline
\multicolumn{3}{c}{Online Media Data}\\
\hline                                                                                 Social networking service & Twitter, Weibo, Instagram, Facebook, and WeChat data & \cite{beria39,fiadhi58,gang59,albadawi60,huang62, xue66,haupt03,sari10,wrycza14,kim22,Wang25,long31,hua33,zhang35} \\
Navigation & Google data, Baidu data & \cite{liu34, jimenez63, million65, liu72,mavragani74, higgins75, ye77, zhang83, quynh89, lee92} \\
Online news & Fox news, Koren and China news and magazine & \cite{chen46,gang59,jung91}\\
E-commerce & Online shopping data, Tripadvisor data & \cite{shahbazi21,wu90}\\
\hline
\multicolumn{3}{c}{Public/Open Data}\\
\hline
Stock Exchange & Stock market data & \cite{jing51,liammukda16} \\
Kaggle & Covid-19 cases & \cite{rivai61,sinha20,ngie36} \\
Scientific data & Weather and climate data & \cite{ibrahim29} \\
\multirow{2}{*}{Other dataset} & Masked face head pose image data & \cite{li27}\\
& Facial expression video and speech audio data & \cite{sun15}\\
\hline                    
\end{tabular}
\end{table}
\end{landscape}

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\begin{backmatter}
% \renewcommand{\nomname}{List of abbreviations}
% \nomenclature{AHP}{Analytical Hierarchy Process}
% \nomenclature{AI}{Artificial Intelligence}
% \nomenclature{ARIMA}{Auto-Regressive Integrated Moving Average}
% \nomenclature{BTM}{Generative Adversarial Networks}
% \nomenclature{CNN}{Convolution Neural Network}
% \nomenclature{DID}{Difference-In Difference}
% \nomenclature{ELM}{Extreme Learning Machine}
% \nomenclature{FP-growth}{Frequent-Pattern growth}
% \nomenclature{GAMM}{Generalized Additive Mixed Model}
% \nomenclature{GANs}{Generative Adversarial Networks}
% \nomenclature{HDFS}{Hadoop Distributed File System}
% \nomenclature{LDA}{Latent Dirichlet Analysis}
% \nomenclature{LSTM}{Long Short Term Memory}
% \nomenclature{ML}{Machine Learning}
% \nomenclature{MLP}{Multi-Layer Perceptron}
% \nomenclature{NLP}{Natural Language Processing}
% \nomenclature{OCR}{Optical Character Recognition}
% \nomenclature{PCA}{Principal Component Analysis}
% \nomenclature{PFT}{Pulmonary Function Test}
% \nomenclature{RNN}{Recurrent Neural Network}
% \nomenclature{SEIR}{Susceptible, Exposed, Infected, and Recovered}
% \nomenclature{SIR}{Susceptible, Infected, and Recovered}
% \nomenclature{SNA}{Semantic Network Analysis}
% \nomenclature{SOM}{Self-Organizing Map}
% \nomenclature{SQL}{Structured Query Language}
% \nomenclature{SVM}{Support Vector Machine}
% \nomenclature{UGC}{User-Generated Content}

% \printnomenclature

\section*{Abbreviations}
AHP: Analytical Hierarchy Process; AI: Artificial Intelligence; ARIMA: Auto-Regressive Integrated Moving Average; BTM: Generative Adversarial Networks; CNN: Convolution Neural Network; COVID-19: Coronavirus Disease 2019; DID: Difference-In Difference; ELM: Extreme Learning Machine; FP-growth: Frequent-Pattern growth; GAMM: Generalized Additive Mixed Model; GANs: Generative Adversarial Networks; HDFS: Hadoop Distributed File System; LDA: Latent Dirichlet Analysis; LSTM: Long Short Term Memory; ML: Machine Learning; MLP: Multi-Layer Perceptron; NLP: Natural Language Processing; OCR: Optical Character Recognition; PCA: Principal Component Analysis; PFT: Pulmonary Function Test; RNN: Recurrent Neural Network; SEIR: Susceptible, Exposed, Infected, and Recovered; SIR: Susceptible, Infected, and Recovered; SNA: Semantic Network Analysis; SOM: Self-Organizing Map; SQL: Structured Query Language; SVM: Support Vector Machine; UGC: User-Generated Content; WHO: World Health Organization.

\section*{Acknowledgements}%% if any
The authors wish to thank the other members of the information retrieval research group at Research Center for Informatics, Indonesian Institute for Sciences, for their help and supportive discussions throughout this work. 

\section*{Authors' contributions}
DR and DM introduced conceptualization. Literature review, drafted, and writing the manuscript were performed by DR and EN. Literature collection, review, and editing of manuscripts were carried out by DR and EN. Visualization and data processing were prepared by AA and EN. WS supervised all the work of the literature review. All authors have reviewed and agreed to the published version of the manuscript.

\section*{Funding}%% if any
Not applicable (This work received no specifc grant from any funding agency, commercial or not-for-proft sectors).

\section*{Availability of data and materials}%% if any
Not applicable

\section*{\normalsize Declarations}
\vspace{2mm}

\section*{Ethics approval and consent to participate}%% if any
Not applicable

\section*{Consent for publication}%% if any
Not applicable

\section*{Competing interests}
The authors declare that they have no competing interests.




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