A Study on “The Impact of Data Analytics in COVID-19 Health Care System”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021

1. A STUDY ON THE IMPACT OF
DATA ANALYTICS IN COVID-19
HEALTH CARE SYSTEM
[1] Sandya Jalesh Kumar, Student, Dept. of Information Technology,
Hindustan Institute of Technology and Science
[2] Dr. C.V. Suresh Babu, Professor, Dept. of Information Technology,
Hindustan Institute of Technology and Science

2. “
Once we know something, we find
it hard to imagine what it was like
not to know it.
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3. INTRODUCTION
◉ Through the disperse of novel coronavirus illness globally, existence became
considerably contrived.
◉ Data analytics have experienced powerful development over the past few
years.
◉ As it happens, it’s exceptionally considerable to take advantage of data
analytics to assist mankind in a prompt as well as factually precise method to
forestall additionally restrain the advancement of the widespread, sustain
gregarious balance and evaluate the influence of the widespread.
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4. INTRODUCTION
◉ The unforeseen significant number of coronavirus disease instances has
disturbed medical care system in many economies furthermore eventuated in
an insufficiency of dormitory in the hospices.
◉ For this reason, predicting quantity of coronavirus infection instances is
indispensable for administrations to adopt the necessary measures.
◉ The count of coronavirus disease instances could be correctly anticipated by
taking into account historical records of announced instances side by side few
extraneous components that impact the disseminate of the COVID-19 .
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5. Work starts by reviewing many of the different modelling approaches used to
provide both descriptive analytics on the current cases and deaths, but also
those used to predict the impact of the pandemic. The descriptive analytics
models share current statistics on cases, deaths, recoveries, etc. The
predictive models share current statistics and provide forecasts for deaths
and cases in the future.
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6. METHODS
To design the predictive model, all the official data sets available were
exploited. The Wuhan official data set was analyzed and as for the Italian
perspective, the official data set that is daily published was adopted and
updated by the Department of the Italian Civil Protection. Additional statistics
have been imported from the World Health Organization (WHO) website.
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7. ASSUMPTIONS
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We assumed that the Italian Government would act
promptly with restrictions and lockdowns on the Italian
population and that the Italian citizens would follow these
restrictions with a sense of responsibility.

8. ASSUMPTIONS
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We based our initial estimation on the number of swab
tests analyzed in the initial days of the pandemic period,
where an average of 8000 swab tests were performed
daily (in May, the number of swab tests was increased
significantly to an average of 50,000 tests/day).

9. 9
Graphical comparison between the Italian curve and Wuhan curve

10. SIRD Model
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11. CONCLUSION
In a crisis, governments often make difficult decisions under uncertainty and time constraints.
These decisions must be both culturally appropriate and sensitive to the population. Through
early recognition of the crisis, daily briefings to the public, and simple health messaging, the
government was able to reassure the public by delivering timely, accurate, and transparent
information regarding the evolving epidemic.
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12. Thank you!
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