This document discusses how big data and analytics can help address the COVID-19 pandemic. It begins by defining big data and describing its key characteristics of volume, velocity, and variety. It then discusses how the pandemic has led to a large volume of health data and different data types. The document proposes a framework for collecting, analyzing and applying this data through descriptive, diagnostic, predictive and prescriptive analytics. This framework could help with tasks like epidemic monitoring, early warning, tracing virus sources, and recommending best courses of action. In closing, the document lists several references on big data applications for public health surveillance, resource allocation, and investigating COVID-19 symptoms.

1. Big Data and its Impact on Health Care
Presented By,
Dr. S. LILLY SHEEBA,
Associate Professor,
Department of Computer Science & Engineering,
SRMIST, Ramapuram,
Chennai - 600089.
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2. Contents
• How much time will it take to query this?
• In a minute...
• Big Data
• Characteristics of Big Data – 3 V’s
• Volume
• Velocity
• Variety
• Applications of
• Big Data and Covid -19
• Proposed Framework
• References
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3. How much time will it take to query this?
• Excel: A table of 500 MB
• Access: A table of 10 GB
• SQL: A table of 50 GB
• Running on 20, 980, 000 GB Google file. It deals with
more than 20 PB/ day.(1TB-1000GB; 1PB-1000PB)
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4. In a minute .......
• 204 million Emails received
• 2 million search queries/day
• 48 hours of video on YouTube uploaded
• 217 new users on Mobile Web
• 34000 likes on Facebook
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5. BIG DATA
• Collection of large and complex datasets
• Difficult to process using traditional DBMS tools or
traditional data processing applications
• Requires new architecture, techniques, algorithms and
analytics to manage and extract hidden knowledge
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6. CHARACTERISTICS OF BIGDATA – 3V’s
• Volume
– Quality of Data
• Velocity
– Speed of Data
• Variety
– Types of Data
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7. VOLUME
• Face book ingests 500 TB of new data per day
• Boeing 737 generates 240 TB of flight data during a single
flight.
• Large amount of data in case of pandemic outbreak
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8. VELOCITY
• Online gaming supports millions of concurrent users, each
producing multiple inputs/second
• Stock trading algorithms reflect market changes within
microseconds
• Click – likes streams and ad impressions capture user
behavior of millions of events/second
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9. VARIETY
• Big Data supports simple types like
– Numbers
– Dates
– String
• It also supports complex types like
– Geospatial Data
– 3D Data
– Audio & Video Data
– Unstructured Text
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10. COVID-19 and a pandemic outbreak.....
– Total people infected stands at 11 crore globally
– Total fatality stands at 25 lakh globally
– Lead to
• Burst of cases
• Larger amount of health data
• Different data storage technologies to store this
– Data here acts as a vital source
• of information and knowledge
• for research activities about the virus or pandemic
• to device measures to fight the virus and its after-
effects
• to tackle the pandemic
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11. Big Data and COVID-19
– Benefits of using Big Data
• can be used to digitally store a large amount of data
of COVID patients(infected, recovered and expired)
• helps to analyze the patterns, associations and the
differences
• helps to analyze the effectiveness of the control
measures used in controlling the advances of the
pandemic
• checks the spreading of the virus
• assists in developing future preventive methods
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12. Big Data and COVID-19 [contd..]
– Several data digitized include
• Patient location
• Proximity
• Patient-Reported Travel
• Co-Morbidity
• Patient Physiology
• Current Symptoms
– Big Data Analytics renders itself useful to
• Scientists
• Health Workers
• Epidemiologists
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13. 13

14. PROPOSED FRAMEWORK [2]
[2]
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1. Telecommunication Operations
2. Internet
3. Electronic Medical Record
4. Hospital Information System
5. Government Information System
6. Epidemic Prevention System
7. Smart Device
8. Questionnaire
Collection Layer

15. PROPOSED FRAMEWORK [3]
[2]
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1. Location Data
2. Travel Data
3. Medical Data
4. Health Status Data
5. News Media Data
6. Government Data
7. Online Consumption of Data
Data Layer

16. PROPOSED FRAMEWORK [4]
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1. Descriptive Analysis
2. Diagnostic Analysis
3. Predictive Analysis
4. Prescriptive Analysis
Analytics Layer

17. PROPOSED FRAMEWORK [5]
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1. Personnel Tracking
2. Epidemic Surveillance
3. Early Warning
4. Tracing of virus sources
5. Drug Screening
6. Medical Treatment
7. Resource Allocation
8. Production & Delivery
Epidemic Monitoring
And
Surveillance Layer
[Application]

18. DESCRIPTIVE ANALYSIS
• Provides basic information about variables in a dataset.
• Highlights potential relationships between variables.
• “What has happened?”.
• Displayed either graphically or pictorially.
• Example:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264658/table/jmv25906-
tbl-0001/?report=objectonly
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19. DIAGNOSTIC ANALYSIS
• Explains reasons and factors behind occurrence of an event.
• Explores factors that affect the occurrence of an event.
• “Why did it happen?”
• Treatment options for a patient based on clustering or decision trees.
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20. PREDICTIVE ANALYSIS
• Helps in predicting and understanding the future.
• “What could happen in the future?”.
• Analyzes past data patterns and trends by looking at historical data and
customer insights.
• Helps to identify future trends in patient care both at an individual level and
at a cohort level.
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21. PRESCRIPTIVE ANALYSIS
• Helps in recommending the best possible courses of action.
• “What should be done?”
• Enables health care decision–makers to optimize recommendation for the
best course of action for patients and health workers.
• “What if ? “ scenarios are used to access the impact of choosing one action
over the other.
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22. REFERENCES
1. S. Sharma and V. Mangat, "Technology and Trends to Handle Big Data:
Survey," 2015 Fifth International Conference on Advanced Computing &
Communication Technologies,Haryana,2015,266-271.[DOI: 10.1109/
ACCT.2015.121]
2. Burghard C: Big Data and Analytics Key to Accountable Care Success.
2012, IDC Health Insights.[URL:https://www.waysquare.com/big-data-
and-analytics-key-to-accountable-care-success/]
3. Feldman B, Martin EM, Skotnes T: “Big Data in Healthcare Hype and
Hope.” October 2012.Dr.Bonnie 360.
4. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise
and potential. Health Inf Sci Syst.2014.
5. Ginsberg J, Mohebbi M H, Patel R, et al. Detecting influenza epidemics
using search engine query data.Nature,2009, 457(7232):1012-1014
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23. REFERENCES-contd.
6. Hui-Jun Qiu, Lian-Xiong Yuan, Qing-Wu Wu, Yu-Qi Zhou, Rui
Zheng, Xue-Kun Huang, Qin-Tai Yang, Using the internet search
data to investigate symptom characteristics of COVID-19: A big data
study, World Journal of Otorhinolaryngology - Head and Neck
Surgery,2020.
7. Salman Y. Guraya,Transforming laparoendoscopic surgical protocols
during the COVID-19 pandemic; big data analytics, resource
allocation and operational considerations, International Journal of
Surgery 2020 August;80:21-25.
8. Ayyoubzadeh SM, Ayyoubzadeh SM, Zahedi H, Ahmadi M, R
Niakan Kalhori S. Predicting COVID-19 Incidence Through Analysis
of Google Trends Data in Iran: Data Mining and Deep Learning Pilot
Study. JMIR Public Health Surveill.2020;6(2):e18828.
9. Haleem A, Javaid M, Khan IH, Vaishya R. Significant Applications
of Big Data in COVID-19 Pandemic. Indian J Orthop.2020;54(4):1-
3. 23

24. REFERENCES-contd.
10. Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M, Wu J.
How Big Data and Artificial Intelligence Can Help Better Manage the
COVID-19 Pandemic. Int J Environ Res Public
Health.2020;17(9):3176.
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25. Thank you
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