As the world battles the COVID-19 pandemic, it is imperative that we take decisions and choices grounded in scientific basis and data. Insights into timely detection of the infection, the efficacy of the lockdown, the rate of virus spread, triaging and assessment, preventive actions, the spread of the disease, and the drivers of resilience of people and systems to cope with the virus, can help health practitioners, policy makers and communities respond more effectively. Let's explore how AI and ML-led approaches can help make some of these decisions better.

1. Fighting the
Pandemic with
Data
Intelligence. Delivered.

2. Who are we?
Srinivas Atreya (Chief Data Scientist at RoundSqr) Srinivas is the
quintessential AI/ML geek, with an innate ability to solve
business problems using insights from data. He has over 23
years as an Analytics / Information Management practitioner
across the US, UK, and India, in the diverse roles of an Engineer,
Architect, Data Analyst & Data Scientist.
Sirisha Peyyeti (Head of Consulting, RoundSqr) Sirisha is a
Business Leader, with more than 18 years of experience in the
IT services industry, focused in the area of Data and Analytics.
Experience in solution consulting, with a strong focus on digital
thinking. Believes that common sense is the right ingredient for
solving digital transformation problems!

3. Who are we?
Confidential3
Leading-edge AI and digital technologies to supercharge an organization.
TRANSFORMATION CONSULTING
Start with the “what” (problem) and expand to the “how” (solution) in
an agile manner.
DIGITAL SERVICES
Digital Delivery using skilled resources and cutting-edge technologies.
COGNITIVE PLATFORM
Infusing world class IP & capabilities into your business to prepare you
for an AI-first future.
RoundSqr is akin to a DIGITAL PITSTOP, providing organizations with
the right guidance and support, at the right time, quickly, to let them
drive their business better and faster
Leveraging decades of experience in data strategy, platform design
and digital transformation to help organizations reap the benefits of
digital, data science and analytics to DRIVE REAL VALUE
,

4. Our COVID-19 Initiatives
- Classification
- Disease Progression
- CORD-19 / Leveraging NLP to
better understand the disease
Understanding the
Disease
Making the
transition
- Efficacies of Non pharma
intervention / Real Time Rt
prediction
- Triage / New Perspectives
Gearing up for the
new world
- Workspaces of tomorrow
- Heat Maps
- Facial Recognition
- Track & Trace

5. #1: Distinguishing Covid-19 using Chest X-rays
UMAP Technique to classify
COVID-19 X-rays
The How?
▪ Created high contrast images , normalized the X-rays by subtracting mean to
make it relatively easy to classify
▪ First approach was to try SVM classifier– Resulted in low AUC
▪ Next approach was to use SVD to identify important features – No distinct
separation of clusters
▪ Third approach was to use neighbourhood graph technique of UMAP
▪ Tried both unsupervised and supervised UMAP
Using supervised UMAP and metric learning, we are able to separate out the
Normal, Pneumonia and COVID-19 chest X-Ray images
The Why?
▪ X-rays have more prevalent usage as a primary diagnostic
test when compared to CT scans
▪ Explore the possible preliminary classification of COVID-
19 pneumonia using chest X-rays
▪ Covid chest X-ray, non Covid pneumonia images as
datasets were considered
▪ Intent is to classify the X-Rays into normal lung, Non
Covid Pneumonia and COVID-19 Pnuemonia
5

6. #2: Disease Progression prediction based on X-ray data
Using DMD, a data-driven
approach to understand the
progression of COVID-19
The How?
▪ Dynamic Mode Decomposition – Teasing out the dominant spatio-temporal
modes.
▪ Completely data driven – no assumptions whatsoever on the function.
▪ Best Linear fit using Least Squares Regression.
▪ Eigen Value decomposition of the best linear fit gives us the dominant modes.
Using Dynamic Mode Decomposition we are able to predict the X-Rays ahead one
time step.
The Why?
▪ Mobile X-rays are a lot more prevalent than any other
testing device for chest X-rays
▪ Lungs get affected and the pattern is clear in the
Longitudinal data of X-rays taken for a patient
▪ Intent is to predict the progress of the disease by looking
at previous X-Rays of the patient
▪ That information might help frontline workers predict /
prioritize risky patients
6
Original time steps that we took for DMD Reconstructed a step ahead using DMD modes

7. #3: Leveraging NLP to better understand the disease
LDA, an unsupervised
learning method to
understand the pattern of
words within a corpus and
create meaningful clusters
The How?
▪ Started with the most used algorithm in this space - Latent Dirichlet Allocation
(LDA)
▪ Used ktrain package on CORD-19 research documents and research documents
from ncbi.nlm.nih.gov
▪ Set the optimal threshold value to filter out irrelevant documents and build
topic-world distribution
▪ Filtered out the clusters and created visualization just for relevant ones including
Transmission, Incubation, Environment, Risk for children and pregnant woman,
Virus protection
Implemented a latent probabilistic model as LDA and looking forward to build a
Variational Autoencoder
The Why?
▪ Publications for COVID-19 are growing exponentially
making it difficult to distill information that is wide
spread
▪ 59,000 scholarly articles, including over 47,000 with full
text, about COVID-19, SARS-CoV-2, and related
coronaviruses
▪ Intent is to start with segmenting this corpus into
“topics” and answer questions like “Can we try to search
the relevant content from the corpus to match the
queries?”
7
85 cases occurred Saudi Arabia, one of the key factors of transmission is
understood to be proximity to camels
Similar severe acute respiratory syndrome sars include fever myalgia dry
cough dyspnoea fatigue radiological evidence ground glass lung opacities
compatible atypical pneumonia

8. #4: Efficacies of Non pharma intervention / Rt prediction
Rt tracker, an indicator for
spread and the ideal KPI to
measure
The How?
▪ Scrape the content / numbers for new COVID-19 patients on a daily basis
▪ Use Bayes Rule to update our belief about Rt, based on the new infection data
▪ Used Poisson Distribution as the likelihood function, a preferred model for
understanding the “number of arrivals” in a given time period
▪ Update Rt values based on daily new cases
▪ The downside to this is lack of enough data or irregular data that we are seeing a
few states / districts report
There is a general decline in Rt among the states and there is relatively a higher
confidence level. But we are still not at that level where it is less than 1
The Why?
▪ When should this lockdown be relaxed and how do we
know that we are making progress? That’s the call to
take right now as the world start re-opening
▪ Rt known as Effective Reproduction Number is a good
measure to track
▪ Knowing the current Rt is essential for policy-based
decision making. When Rt>1, the pandemic will spread
through the entire population. The lower Rt, the more
manageable the situation
8

9. #5: Triage / New Perspectives
Cov-Safe – A digital tool that
frontline workers can use for
triage
The How?
▪ Mobile (and Web App) to collect cough and other voice samples
▪ Spectral analysis of the data to predict anomalies
▪ Profile information of the employee along with vitals data – Temperature, SPO2
at a minimum
▪ Questionnaire to asses physical and mental health
▪ Supervisor / Admin to review all triage records and take necessary action
▪ Ability to extend and integrate to health kiosks, low range Bluetooth devices for
vitals collection without manual entry
Comprehensive and efficient triage mechanism for risk analysis
The Why?
▪ The economy is opening up and everyone needs to be a
lot more “risk aware”
▪ An app / platform that puts the workers right at the
center and helps with easy, efficient and effective triage
is the need of the hour
▪ Can cough or sounds from the diaphragm or SPO2 be a
good indicator for COVID-19?
▪ An end – end digital platform that can triage, recommend
actions and track physical and mental health of
employees
9
Covid-19
Triage
App
3.
Dashboards
2. Determine
“Call for Action”
1. Profile
Information

10. 01
Track & Trace
▪ Entry and Exit with cameras and facial
recognition capability – No touch authorization
and authentication of employees
▪ Ability to trace the path of an individual –
beacons, camera feeds, device pings
▪ Geo-fencing and warnings / notifications as one
enters the “red zones”
Workplaces of the future
03
02
Safety Solutions
▪ Right gear detection – masks, gloves etc
▪ Restrict unauthorized access
▪ People “hotspots” detection – social distancing
norms
▪ Remote monitoring and alerting
AI Infused facilities management
▪ Frequent path analysis of foot falls and hence prioritized areas to
clean and disinfect
▪ Space / Capacity planning recommendations and What-If analysis
based on the new norms – reduced workforce at office – which
areas to be occupied, optimal temperature of aircon, elevators
operated based on % employees in the building

11. ` 2m
HOW TO PREVENT
COVID-19
Guidelines
Sirishapeyyeti
@SirishaPeyyeti
SrinivasAtreya
@SrinivasAtreya
Please follow our page on LinkedIn page for interesting updates
https://www.linkedin.com/showcase/cov-aid-20
Thank you!
www.roundsqr.com