Builder.ai's CEO and head of AI have developed a framework that looks at the pandemic and formulates an approach to spread detection, digital passports and vaccine delivery. It's released under creative commons for everyone to leverage.

1. Signal System Overview [WIP]
Contagion Spread Detection and Containment
Mapping v1.2
Authors: @Sachin Dev Duggal, @Dr Siddhartha Ghosh,
Contributors: @Rohan Patel @Saurabh Dhoot @Dr Sharon Jheeta
@Rushen Patel @Chandan Kumar
builder.ai
© April 2020 Engineer.ai Global Limited.
Except as otherwise noted, this work is licensed under under a
Creative Commons Attribution-NonCommercial-ShareAlike 4.0
International License.

2. © engineer.ai 2020
Problem - Preamble
Viruses / contagions spread an order of magnitude faster than the rate of testing. The contagion follows the path of a
social network whereas testing patterns are quasi linear in their approach.
Key is to reduce the potential of reaching terminal spread velocity and reducing the affected population.

3. © engineer.ai 2020
Problem - Preamble
- Contagions, like Covid-19 can spread incredibly quickly as they survive on surfaces or transmit over airborne human
contact. The rate at which they spread makes testing almost impossible and containment incredibly expensive.
- Until a vaccination is ready; isolation (containment) and digital passports are only ways to reduce terminal spread velocity
and economic fall out.
- Most affected in any such pandemics are the elderly and the impoverished; they do not have access to smartphones; but
by in large all have access to a feature phone. Therefore the contagion has disproportionately more impact to these people.
- The contagion has a number of spread vectors
- A diagnosed person has an incubation period that gets longer closer to R0 (first instance).
- A diagnosed person may have touched a surface that will stay “live” for upto X Days.
- Multiple diagnosed persons may have been in the same area thereby causing a compounding impact.
- A diagnosed person who travels will likely have spread to a larger person via the air conditioning systems of a flight.
- Alternative: a diagnosed person who has travelled previously and was mobile is likely to have infected significant
numbers in confined spaces

4. © engineer.ai 2020
Problem
- Build an effective and dynamic contact tracing solution that continuously shows a real-time view of most at risk and
provides a navigational direction to health authorities on how to break the spread down such that you have contained
communities.
- The solution has to be low tech as the most impact is usually on those without internet connected smartphones; ie elderly
or impoverished who have features phone.
- Development Market Smartphone penetration hovers at 60-70% for the elderly and 45-55% for the underprivileged.
- Emerging Market Smartphone penetration hover at 25-30% overall. **
- For the solution to work you need >90/95% coverage of a population at a given time; and a stratified testing model that
continuously re-evaluates the network and ensures that a real-time view of risk is shown.
- When you have a vaccine, you need to be able to the shortest path to stopping spreading and optimizing for the least
deaths which is not the same as a linear roll out of a vaccination especially when at the start the vaccination quantities
will be less.
** Source: https://www.finder.com/uk/mobile-internet-statistics

5. © engineer.ai 2020
Smartphones and Apps only won’t work - must be hybrid
- Because of their very low coverage they don’t have the ability to model analogous to population. In very specific forward
cities they can have a strong impact only if >90% download - but holistically on a global basis, a smartphone only strategy
won’t work.
- Without a model that can dynamically adjust the radius to create a “catchment” area they will create large false positives
or false negatives.

6. © engineer.ai 2020
Signal - Highlights Overview
1. Use the triangulation data available from all cell phone towers to be
able to pinpoint all devices (ie people).
a. The phone numbers would all be one way hashed so there is no
way to track a single individual.
2. Create an accurate backtrace using prior locations across cell towers -
this will be created on a spatiotemporal signal network graph
3. Superimpose this with health condition tags (ie did the person have a
lung condition.
a. Set a dynamic radius (s0) at each point of the backtrace to create
a catchment zone.
b. This will show who could be infected and who is most at risk.
i. i.e using the phone hash and medical tags (and their
severity).
4. The graph is a “living” structure that is effectively mapped in real time
as people move about and get diagnosed (true positives and true
negatives).
a. Effective reduction of area is key whether it's via testing or via
decay system (where if someone and their community have
shown no positive test results for a given period the whole
branch becomes clean).
5. The system can also identify which communities to test and the result
is then used to clear out entire branches of the graph until they have a
new node (person) entering them.
6. Vaccine Delivery needs to be done in a way to reduce the amount of
vaccine needed splinter the graph thereby making this a non spreading
localised problem which can then fall into a second wave of treatment.

7. © engineer.ai 2020
Signal - Three phases of the pandemic
1. Isolation
a. Reduce the amplitude of the spread (ie reduce the peak).
2. Allow limited back to work - Prevent economic free-fall or further damage
a. Create a dynamic digital passport for antibody testing and pseudo green light
b. Create a path to dynamically test (stratified testing).
3. Vaccination Deployment
a. With limited resources what’s the most optimized way to deploy to break universe of population into smaller and
smaller communities and track when communities fuse together.

8. © engineer.ai 2020
Signal - Highlights Overview
Path of an individual using
cell tower data
People they interact with
Dynamic Radius is the
catchment zone
When and where this
person got infected
Tertiary effected (not the
person diagnosed, not the
person they met but others
who crossed over)
Intensity of other contagion
carriers increases the radius
T=0 is when the person is
diagnosed

9. © engineer.ai 2020
Signal - 4D Spatial - Time Box
Contagions have a 4D infection spread
The 3d is the spatial plane and the 4d dimension is a time duration since an infected person visited the site.
The horizontal
radius is the
normal
geo-plane (ie the
spatial distance)
In this time box if
anyone crosses the
“spread radius”
they get tagged as
potentially infected.
Any given location needs
to have a t+4 days time
horizon as C19 has a time
box of 4 days on some
materials.

10. © engineer.ai 2020
Signal - Approach Overview
1. Use cell phone tower data.
2. Triangulate all devices using signal strength and
fixed nature of towers.
3. Superimpose this with account data for phone
numbers to infer mandatory spread.
4. Link the master universe of nodes based on
historical interaction to create a propagation
network (PN). This PN is dynamic and evolves
backward in time from the point a node has been
diagnosed to be infected
5. Adjust interactions on the network based on
intensity and other carriers (to establish who all
could be infected)
6. Fuse hashed node identifier with medical tags
basis EMR (electronic medical records) (again
only tagged not identifiable) (pre existing tags
that are medically designated with a weight)
1. Ie Node Hash has 3 Sev 1 conditions, 2 Sev
2 Conditions etc
7. Identify high risk nodes who have attributable
medical conditions on the PN and test
8. Stratified testing of those most likely; use
sensitivity and specificity to re-calibrate master
propagation network
9. Establish a vaccination strategy based on
understanding the characteristics of the PN with
an aim to reduce the size and the connectedness
of the network

11. © engineer.ai 2020
Signal uses a 5 point process
1. Person Triangulation & historical movement
a. Data from cell towers will tell you the movement off all people in a given geography (spatial plane).
b. You backtrace from when a person is positively diagnosed to see their reverse journey for a given period of time (the
contagion incubation period).
i. You do this for all people.
c. This will create a spatiotemporal signal network graph (SSNG)
d. One way hash of all numbers.
2. Propagation Network - with a dynamic radius (“spread) across the backtrace
e. The spread (s0) of a contagion (at a time t and location a) over the network should take into account the following: r
= radius of interaction of a node; p = potency of contagion; c = intensity of carriers; d = max time to dissipate on any
surface and i = saturation of nodes at a given t + d.
i. Signal DR will take into account the forward looking surface life time as well ie if a known contained person was
in a particular location then anyone coming in that location for 4 days would run the risk of being infected.
f. The dynamic radius tell you who additionally to add from (1) to the SSNG (you are effectively adding more nodes to
the network basis their inclusion in the radius).

12. © engineer.ai 2020
Signal uses a 5 point process cont’d
3. High Risk Identification
a. Use medical tags and severity to add attributes to hashed phone numbers so that you can map out those most
critically at risk. The most at risk get flagged quicker for testing.
4. Pruning and Spread Velocity Decreasing ---> Smarter testing to optimize lower test availability
a. Use stratified testing algorithm to define which are the parts of the graph to test.
b. Use implicit decay in contagions behavior to eliminate branches from testing. These become green branches that
unless they have a new person coming into them or a positive test remain green.
c. Create a vaccination path that aims to break the SSNG into smaller communities dividing the spread rather than
trying to do entire population at once. Optimize for the fastest path to vaccinate where ultimate death toll is minimal.
(This of this as a traffic navigation problem).
5. Interim Digital Passport
a. Whilst there is no fully rolled our vaccine or vaccine is still being rolled out digital certify people back into the
economy by seeing how they move across the spatial plane of the SSNG.
b. Use self test and clinical testing to create reinforced feedback into the network (including back trace - i.e if someone
is clear then the back trace for 14 days for that person is also clear).

13. © engineer.ai 2020
Holistic Visualization
Create a time vector (y-axis) and use a network structure for representation.
a. Moving vertically shows time backwards (ie days from the diagnosis)
b. Color coded interactions for who they met.
c. Show X plane as the map of the region so you can also see the locales visited.

14. © engineer.ai 2020
Holistic Visualization
Create a time vector (y-axis) and use a network
structure for representation.
a. Moving vertically shows time backwards (ie
days from the diagnosis)
b. Color coded interactions for who they met.
c. Show X plane as the map of the region so you
can also see the locales visited.

15. © engineer.ai 2020
Holistic Visualization
Create a flat heat map using icons on the
node diagram to show time from diagnosis
(t=0=date of being positively diagnosed).
Color code regions of high positive diagnosis and
their paths (ie if 5 people 5 days ago were in a
particular place)
Show the network interaction
(person a met person b)

16. © engineer.ai 2020
Holistic Visualization
Creating communities that become bigger/smaller (engulf or get divided)
basis the movement of infected people in between them.
Each “bubble” is a community that is continuously expanding or contracting.

17. © engineer.ai 2020
Detailed Framework Paper & References
Please see the detailed paper here - as we are working on this in real time we have asked for
verified email addresses to access this so that we can get updated content notified.
Visit this site: https://docsend.com/view/862npsj
1. Spatiotemporal Signal Visualizations
1. https://www.mdpi.com/2220-9964/7/9/371/htm
2. Triangulation/trilateration:
1. https://www.101computing.net/cell-phone-trilateration-algorithm/
2. https://searchengineland.com/cell-phone-triangulation-accuracy-is-all-over-the-map-14790
3. Epidemiology / Epidemiology contact tracing / social network spread
1. http://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=2155&context=qhs_pp
2. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0095133
3. https://academic.oup.com/aje/article/162/10/1024/65086
4. https://arxiv.org/pdf/1403.1011.pdf
5. http://biocircuits.ucsd.edu/lev/papers/contact_tracing1.pdf
4. Examples of visualisations:
1. https://cdv.dei.uc.pt/visualizing-urban-mobility/
2. https://www.fastcompany.com/3023416/outbreak-watch-how-quickly-an-epidemic-would-spread-across-the-world
3. https://www.wired.com/2015/04/see-diseases-spread-mesmerizing-graphics/