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 Table of Contents  
Year : 2021  |  Volume : 11  |  Issue : 2  |  Page : 105-106

Data analytics applications for COVID-19 pandemic

1 Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
2 School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India
3 Department of Orthopaedics, Indraprastha Apollo Hospital, New Delhi, India
4 Department of Industrial and Production Engineering, Dr B R Ambedkar National Institute of Technology, Jalandhar, Punjab, India

Date of Submission22-Dec-2020
Date of Acceptance15-Mar-2021
Date of Web Publication18-Apr-2021

Correspondence Address:
Mohd Javaid
Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/cmrp.cmrp_82_20

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How to cite this article:
Javaid M, Khan IH, Vaishya R, Singh RP, Vaish A. Data analytics applications for COVID-19 pandemic. Curr Med Res Pract 2021;11:105-6

How to cite this URL:
Javaid M, Khan IH, Vaishya R, Singh RP, Vaish A. Data analytics applications for COVID-19 pandemic. Curr Med Res Pract [serial online] 2021 [cited 2022 May 19];11:105-6. Available from: http://www.cmrpjournal.org/text.asp?2021/11/2/105/314031

Dear Editor,

The COVID-19 pandemic has taken us over with an unexpected storm. In this pandemic, a massive amount of related patients' data is being created, and there is an urgent requirement to develop a rapid understanding of the potential impact of COVID-19 disease. We are in desperate need of deriving useful information from this vast pool of data. Data analytics (DA) can do through its ability to inspect, cleansing, transforming and modelling this data. Furthermore, it is used to take action on incomplete, duplicate and error data. The information derived from DA should help the health-care administrators to reach accurate conclusions and support them in decision-making. By the implementation of this technology, physicians can quickly identify the patients at most risk of COVID-19.[1],[2]

DA can collect raw data and convert it into useful information on the decision-making process. Various tools are used to analyse the data such as PowerBI, Tableau, Python, R, Sci-Kit Learn, Excel/Sheets, Grow, Klipfolio and ReportPlus. These tools are capable of analysing a very complex yet very intricate project of data presented to them. Tools such as Power-BI and Tableau are ubiquitous in many industries. Still, programming languages always have a dominating hand over these tools such as python, R and their subtools. The Sci-kit and Numpy are the key pointers capable of advanced analytics over the top.

The DA can easily collect and analyse the virus's behaviour and thus help to fight the COVID-19 war and fulfil the challenges posed by the ongoing crisis.[3],[4] It helps to provide information regarding the patients' medical records and accurately analyse the individual patient's risk profile affected by this virus.[5],[6] It provides practical and reliable information of the patients and quickly measures the response during the spread of this infection.

DA provides a successful public health response by understanding the natural history of infection, transmissibility and risk population. The COVID-19 information can be analysed effectively using this technology through an algorithm. It rapidly processes the positive patients of COVID-19 and analyses the signal and physiological signature of the virus. It provides valuable information about the ongoing situation.[7],[8] [Table 1] describes the significant applications of DA for COVID-19.
Table 1: Significant applications of data analytics for COVID-19

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The primary purpose of this technology is to collect information from a variety of sources and further analyse it deeply and in a meaningful fashion. The data are collected from different sources such as documentation, online sources and recording and obtained through interview. Data analysis converts raw information into valuable output with actionable intelligence. It monitors the detailed information and analysis on its own according to the ongoing situation.[5],[9] Researchers have offered this technology to develop a rapid solution to COVID-19 pandemic.

DA can collect the information related to the COVID-19 pandemic and analyse it meaningfully to reach useful conclusions. The algorithms developed by the DA help understand this virus and develop better treatment options and further strengthen health-care professionals' efforts. It analyses the chances of spreading this virus and can quickly identify the patient infected by this virus and assess each individual's risk using the collected information. This information can easily be shared online and becomes an essential communication tool. It provides a better understanding and impact of this virus on human being and society in general.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

  References Top

Haleem A, Javaid M, Vaishya R. Effects of COVID-19 pandemic in daily life. Curr Med Res Pract 2020;10:78-9.  Back to cited text no. 1
Wang CJ, Ng CY, Brook RH. Response to COVID-19 in Taiwan: Big data analytics, new technology, and proactive testing. JAMA 2020;323:1341-2.  Back to cited text no. 2
Pietz J, McCoy S, Wilck JH. Chasing John Snow: Data analytics in the COVID-19 era. Eur J Inf Syst 2020;29:388-404.  Back to cited text no. 3
Agbehadji IE, Awuzie BO, Ngowi AB, Millham RC. Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing. Int J Environ Res Public Health 2020;17:5330.  Back to cited text no. 4
Jia Q, Guo Y, Wang G, Barnes SJ. Big data analytics in the fight against major public health incidents (Including COVID-19): A conceptual framework. Int J Environ Res Public Health 2020;17:6161.  Back to cited text no. 5
Javaid M, Haleem A, Vaishya R, Bahl S, Suman R, Vaish A. Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2020;14:419-22.  Back to cited text no. 6
Eltoukhy AE, Shaban IA, Chan FT, Abdel-Aal MA. Data analytics for predicting covid-19 cases in top affected countries: Observations and recommendations. Int J Environ Res Public Health 2020;17:7080.  Back to cited text no. 7
Chen CM, Jyan HW, Chien SC, Jen HH, Hsu CY, Lee PC, et al. Containing COVID-19 among 627,386 persons in contact with the diamond princess cruise ship passengers who disembarked in Taiwan: Big data analytics. J Med Internet Res 2020;22:e19540.  Back to cited text no. 8
Mondal MRH, Bharati S, Podder P, Podder P. Data analytics for novel coronavirus disease. Inform Med Unlocked 2020;20:100374.  Back to cited text no. 9


  [Table 1]

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