Covics-19 is a web app that matches countries' required medical resources to their healthcare capacity and coordinates sharing of resources among countries. It predicts COVID-19 cases and hospitalizations over 3 weeks using infection growth curves. It compares predictions to healthcare capacity to identify overwhelmed systems. It then finds the nearest region with excess capacity to redistribute supplies so more patients can receive treatment and the burden can be shared globally as the virus spreads. The web app is developed using an agile approach by a team with expertise in physics, data science, software engineering, and molecular informatics.

1. COVICS-19
INTERNATIONAL HEALTH-CARE LOGISTICS

2. COVICS-19
What does it offer?
SARS-CoV-2 is spreading rapidly through communities. Covics-19 matches
countries' required medical resources to their actual healthcare capacity and
coordinates medical resource sharing among them.

3. OUR
MOTIVATION
#HAVEANIMPACT

4. Covid-19
SARS-CoV-2 is spreading rapidly through
communities, and healthcare systems are
struggling to keep up. Though most people
infected experience mild disease, some,
especially older people and those with
underlying health conditions, require
hospitalization. Many of those infected with
SARS-CoV-2 will require hospitalization, and
the mortality rate is significant and still
uncertain. However, hospitals are struggling
to cope with the influx of patients and are
reaching a point of saturation. Without
medical assistance and cooperation between
countries, far more COVID-19 patients die.
FIGHTING
COVID-19
Challenges
Disinformation
Lack of political
agreement
Uncertainty
No
communication
channels
Shortage of
medical
resources
Logistics

5. OUR
PROPOSAL
#ORGANIZE
#COMMUNICATE
#COORDINATE
#SOLVE

6. A LOGISTICS WEB APP SOLUTION
To help tackle this problem, covics-19 offers through its web app:
■ Predicts the number of COVID-19 cases in a given region that will require
hospitalization over the next 3 weeks. Infection rate growth curve over Hopkins
data will be used for the task.
■ Compares required medical resources to the actual healthcare capacity in the
region to determine whether the health system will be overwhelmed or not.
■ Finds the nearest region with excess capacity in the coming weeks, so that medical
supplies can be redistributed.
This means that:
■ The burden can be shared and more patients can receive life-saving treatment.
■ As the infection moves globally, countries that bring their cases under control will
be able to provide aid to countries where cases are rising rapidly.

7. HOW IT WORKS
THE MATHS
THE
IMPLEMENTATION
THE WEB APP
1 2 3

8. 1. THE MATHS
■ PREDICTION OF PATIENTS IN NEED OF IN-HOSPITAL MEDICAL CARE IN 3
WEEKS
Per-country:
1. Fit cumulative confirmed COVID-19 cases over time to logistic and
exponential curves (data courtesy of John Hopkins University CSSE)
2. Assess curve fit with r2
statistic
3. Use best-fitting curve parameters to make predictions of COVID-19 cases
for the next 3 weeks
4. Use country-specific proportion of deaths + recovery to cases to infer
COVID-19 deaths and recovery numbers for the next 3 weeks
Each day we can pull the latest JHU CSSE data, update the curve parameters
and make new predictions based on the latest data

9. 1. THE MATHS
ITALY:
R2
: 0.9996
Infections today: 97,689
Predicted infections: 127,724
US:
R2
: 0.995
Infections today: 140,886
Predicted infections: 10,916,370!
SWITZERLAND:
R2
: 0.9999
Infections today: 14,829
Predicted infections: 17,895

10. 1. THE MATHS
- Make sure that there is enough supply available while minimizing amount of
supplies exchanged. This requires solving an optimization problem.
- For example if Si
is the supply available at country i, we wish to find the
policy p which maximize their sum:
𝚺 Si
(p,t) = 𝚺 (Ii
-Casesi
(t)-Gp
(i,t)+Rp
(i,t)).
- In the above: Ii
is the initial supply, Casesi
is the predicted number of cases
and Gp
and Rp
represent the amount of supplies respectively given away and
received under the policy p.
- This problem shares some similarities with the famous knapsack problem in
that we are trying to fit a fixed amount of objects into an optimal
configuration.

11. 1. THE MATHS
We also need to take the following aspects into consideration :
- Exchange of supplies takes time and this depends on the distance between
countries as well as many other factors (in general several days).
- We want to keep a certain amount of supplies as a buffer for any
unexpected spike in the demand.
- We also want to penalize each exchange to minimize the associated costs
and complexity.

12. 2. THE IMPLEMENTATION
■ Casually met on Devpost and married same cause
■ Agile approach
■ Use case analysis
■ Business requirements for UI
■ Data source retrieval
■ Prediction model selection
■ Division of Tasks
– Python prediction model
– Logistics optimization problem
– Web App, UI and DB
– Roadmap
– Pitch
■ Communications, planning, tracking
– Our Slack, Trello Kanban Board, GitHub

13. 3. THE WEB APP

14. HAVE A
LOOK
#INTUITIVE
#USERFRIENDLY
https://covics-19.herokuapp.com/

15. DEMO

16. DEMO

17. DEMO

18. THE TEAM #YOURCONTACT

19. INSERT PICTURE INSERT PICTURE INSERT PICTURE
Othmane Rifki
Physicist, DESY/CERN
Juliet Bowater
Data Scientist
Mohamed Zaim Wadghiri
Software Engineer, PhD
candidate - AI

20. Layla Hosseini-Gerami
PhD Candidate, Cambridge
Molecular Informatics
Piergiacomo, De Marchi
Software Engineer,
MyID - Credit Suisse
Alexandre DeZotti
London