Using the GNU Health Federation to help governments in the fight against the COVID-19 pandemic Pan-European Hackaton EU vs Virus: April 24-26, 2020

1. Tackling the Beast:
Using the GNU Health Federation to help governments in the fight
against the COVID-19 pandemic
Pan-European Hackaton EU vs Virus: April 24-26, 2020
Challenge​: Health & Life communication and prevention
Permanent observatory to collect data on the behavior
and habits of the population in order place preventive measures to
avoid a new emergency

2. Background and justification
Europe and the whole world are living through an unprecedented crisis due to the
COVID-19 pandemic.
Since its detection in December 2019 in the central Chinese city of Wuhan, the virus
infected more than 2.8 million people (confirmed cases) and is involved in the death
of more than 197.000 people in the world.
The most affected countries are Italy, Iran, Spain, Germany, South Korea, France,
the United Kingdom, and the United States.
A global alliance has been established to fight this pandemic. As part of its effort, the
GNU Health team has developed an additional module for COVID-19 surveillance.
The initial tests have been successfully conducted. The module allows the
processing of a full COVID-19 case tracking workflow from data collection to
reporting, as well as epidemiological surveillance report on COVID-19 and other
health conditions.
Given this background, we are honoured to join the EUvsVirus challenge.
Project objectives
The objectives of the GNU Health Team in their contribution to the European and
global effort to fight the COVID-19 pandemic are:
adding additional functionalities to GNU Health to allow the real time
surveillance of COVID-19 and other epidemics at four levels (local, regional,
national, global).
supporting health professionals and policy makers in their effort to contain the
disease by providing a health information system for:
◦ providing real time COVID-19 epidemiological statistics at the GNU Health
Federation portal.
◦ monitoring the habits and behaviours of the population in the context of
COVID-19
◦ Finding the causality and correlation in the susceptibility and disease
progression based on the socioeconomic status, lifestyle, ethnicity, and
other biological and environmental contexts.
◦ sharing ideas and resources with international teams to enhance

3. collaborative efforts
◦ promoting the philosophy of Free and Open Source software and social
medicine to tackle the various health challenges that Europe and the world
are facing today and will continue facing in the near future.
Project scope
Real time data collection and management of:
● population demographics and patient information
● population and patient demographics, domiciliary units, socio-economics,
Lifestyle information
● in the case of COVID-19, extension of the lifestyle and socio-economics
parameters to include parameters of physical distancing, isolation, contact
tracing and quarantine
Physical infrastructure management:
● procurement, inventory and stock management of medicaments, medical
devices, protective masks and other personal protective equipment (PPE)
● management of the availability of resuscitation beds
● management of intensive care unit (ICU) bed capacity
● monitoring of bed availability for COVID-19 patients
● integration with hospital pharmacy, to include the supply, delivery, storage,
inventory, billing and dispensing of medicines.
● batch management, including monitoring of expiry dates of medicines, and
further details required to prevent improper dispensing of medications.
● tools for management of ambulatory care centre at low costs via mobile
devices

4. Case management
GNU Health is already compliant with most requirements of the WHO
epidemiological case management, including:
● Support for COVID-19 case management workflow as defined by the WHO
● Recording for each COVID-19 patient the symptoms, medical history,
medication history, contact history, travel history, associated laboratory and
imaging testing, diagnostic activities
● Support of ICD-10, ICPM, ICD9-CM, Patient Functionality and Disability, WHO
essential list of medicines, vaccination calendars, Millennium Development
Goals (now SDG), Injury Surveillance System, among many other World
Health Organization standards and guidelines.
Reporting:
● patient evaluations at health facility, care at home, or via tele-consultation or
at any other location
● real time identification of patients with severe symptoms, who are fragile or at
high risk
● Person contact tracing
Workforce management
GNU Health includes operational human resource management functionality. The
system allows for the registration of all employees and the management functions for
health professionals working at a health institution.
The appointment management module allows the optimal management of the
hospital agenda planning by managing health professional calendars and
appointments
Remote medical assistance:
● Remote recording and access of patient medical records via various devices
(smartphone, tablet, Laptop, PC, Raspberry Pi, etc.)
● Tele-consultation of patients

5. Geographical information system
Capture of geographical information for domiciliary units and presentation of spatial and
visualisation information via OpenStreetMap.
Data security and privacy:
Data anonymization, patient privacy, data security, digital signature and encryption
can be enforced and adjusted to country laws and policies. GnuPG as established
standard is used for encryption.
Real time knowledge transfer and management:
● real time knowledge transfer to and from all the nodes based on user access
and permissions (Federation model)
● All medical information and patient records can recorded, updated and
transferred electronically to all nodes, as permitted
● Medical records can be searched (even in text form) and easily shared among
health professionals. Information loss is reduced to a minimum
Real time reporting of COVID-19 epidemiological statistics:
● Epidemic curve - Number of reported cases over time
● Demographics distribution of cases - Case distribution by age group, gender
socioeconomic status, work
● Symptoms and severity - Number of reported cases by symptoms and
severity
● Number of cases by geographical area - Number of deaths based on various
criteria
● Number of persons tested

6. Epidemiological Surveillance
We have chosen to provide support: locally, regionally, nationally and internationally.
We therefore need 4 levels of overview. This document will describe the
development task for the dashboards that will be provided by the GNU Health
Federation in the task of the hackathon. We will be using OpenStreetMap for our
mapping and matplotlib for construction of data dashboards.
We will adopt the Cross- Industry Standard Protocol for Data-Mining (CRISP-DM)
approach to specification of the requirements and the data that will be used in the
visualisations.
Where the GNU Health Federation approach can add something will be in allowing
care centres and centres of respite to be recognised quickly, so that for example, if
hotels are used for infected patients, their bedding capacity and support features
could be entered quickly. The numbers of beds available could then be aggregated
and displayed as well.
GNU Health Federation Health Information System: Data collection
The GNU Health Federation (GH Federation) is a distributed network composed of multiple
heterogeneous nodes. A node is the basic unit of the GH Federation, and it can be a citizen
Personal Health Record, a health professional, a health institution or a research center, to
name some examples.
In the case of the European Union, the EU GNU Health Federation would be composed of
millions of citizens and thousands of institutions from all over the Union.
The data generated in those nodes can be transferred to the ​GH Federation Health
Information System (HIS)​ via GNU Health ​Thalamus​, the message server and
authentication authority.
Visuals for Epidemiological Data for Different Levels of Locale
If we are going to provide these screens we need to know what professionals need them and
what they really need to see on them. In CRISP-DM, this is described as part of the

7. Business Understanding. And we also need to know what data needs to be accessed to
build the needed screens. This is referred to as Data Understanding in CRISP-DM.
Business Understanding
Medical Objectives: to service the needs of key professionals in understanding the situation
regarding the Virus.
Impactful visualisations are needed by everybody who has to argue and convince patients
and people who are asking for advice regarding the medical situation. Clear data availability
allows for up-to-the minute evidence-based advice. Who needs those screens most: public
health officers and epidemiologists.
These professionals need:
- - population data (by gender, age, sex, ethnicity, domiciliary units, socioeconomic
status, work, families, lifestyle,...);;
- - resources data: number of hospital beds and ICU beds, occupied and free,
number of ventilators and respirators, ECMO. Healthy health personnel versus
quarantined, sick and dead personnel;;
- - patient data regarding: infected, symptomatic, died, recovered, inhospital, in ICU,
with ventilator, with ECMO (stratified with risk factors, age, gender, comorbidity, living
conditions eg retirement homes, slums, ...);;
- - details of number of tests (PCR, antibodies) taken;;
- - details of number of quarantined contact persons;;
- - mapping of effectiveness of measures: do people stay at home, do they reduce
close contacts with family members, friends and at the workplace, do they apply
hygienic measures, etc.
Our aim is to show that these professional needs can be serviced by GNU Health.

8. Data Understanding
The data understanding stage of CRISP-DM process requires access to the data to be
understood. These data comprise several tiers of data collected at a local, regional, national
and worldwide level that must be safely accessed. For this we will use the GNU Health
Federation model. The use of GNU Health solves a major issue that is the integration of data
required when gathering data from multiple sources.
A first examination of the data collected will provide information about the amount of data
collected, the type (e.g., categorical: nominal or ordinal; numerical: continuous or discrete),
descriptive statistics (e.g., range, average, standard deviation, median, skewness, kurtosis,
mode) of each variable.
In the subsequent step, the distribution of key variables, relationships between pairs or small
numbers of variables, properties of sub-populations and simple statistical analysis will be
performed to gain detailed insight about possible interesting attributes. Findings will be
reported with summary tables and various types of graphs.
A good model for an overview dashboard might be that as provided by Johns Hopkins:
Figure 1: A Base Display Format
If we aim to provide four visualisations:
1. one that would be suitable for a local understanding of the situation (L)
2. one for the regional situation (R)
3. one for the national situation (N)
4. one for the worldwide situation (W)

9. New GNU Health Epidemiological Visualisations
In order to trace the Epidemic Curve, its Geographical Spread and the Capacity to deal with
it, we could adapt the dashboard shown in figure 1, as shown in figure 2, with data provided
live to the blue spaces.
Figure 2: Epidemic Curve, Geographical Spread and Resource Capacity
Showing: Epidemic curve - Number of reported cases over time, Geographic Spread, and
Service Capacity.
Our first attempt at a multi-level dashboard is shown in figure 3.
Figure 3: Four Levels of Reportage

10. This misses some of the bottom views like news and total infected plot vs. time and daily
cases. However with editing and layout change we can address these issues.
Regarding an International Regional view, our first work suggests:
For the National and Local views, it is here that country-specific monitoring of internal
resources becomes important. Regarding the national and local screens of a real-time
observatory based on our key proposals we may also use the panels on the right hand side
of the screen to show possibly as an option:
- - Demographics distribution of cases - Case distribution by age group, gender,
socio-economic group, living conditions e.g. retirement homes, type of home, slums,
etc. and other criteria;;
- - Symptoms and severity - Number of reported cases by region by symptoms and
severity, infected, symptomatic, died, recovered, inhospital, in ICU, with ventilator,
with ECMO (stratified with risk factors, age, gender, comorbidity););
- - Number of persons tested, numbers with virus, means of testing, features of test,
place of test, (PCR, antibodies), number quarantined, contact persons;;
- R- Resources data: number of hospital beds and ICU beds, occupied and free,
number of ventilators and respirators, ECMO. Healthy health personnel versus
quarantanized, sick and died personnel;;
- E- Effectiveness of measures: do people stay at home, do they reduce close
contacts with family members, friends and at the workplace, do they apply hygienic
measures, etc..

11. Supplementary Panels
Regarding these visualisations, they may be shown graphically in a number of ways, using
data visualisation techniques, such as:
(1) (2)
(3)
Figure 3: (1) covariance matrix between variables A, B, C, D and E showing the joint
variability of each pair of variables as a heatmap. (2) joint plot as a kernel density estimate
(kde) displaying relation between variables X and Y with respective histograms/kde on top
for variable X and on the right for variable Y. (3) pairwise bivariate distributions for
sub-populations.

12. The final step of data understanding, will involve the assessment of the quality of the data by
determining if data is complete, if there are variables that may contain errors or missing
values, how frequent is the occurrence of such errors or missing values, how are they
represented and how should be dealt with based on the information gathered on the
business understanding. A report will be produced to summarize this information.
Conclusions
The GNU Health Federation architecture has shown itself to be a powerful tool for recording
parameters that shows the impact of the socioeconomic determinants in the health of the
population. This is a powerful issue that is at the heart of social medicine, where GNU Health
has placed itself. It is precisely this aspect of GNU Health that makes it suitable as a tool for
monitoring the treatment of a pandemic at local, regional, national and international levels.
The GNU Health Federation would provide the solution to the challenge of creating a
Permanent observatory to collect data on the behavior and habits of the population in order
to place preventive measures to avoid a new emergency.
Now, any citizen of the European Union, as well as primary care centers, hospitals,
laboratories and research institution, can immediately be included as a participating node in
the GNU Health Federation, contributing to knowledge about the sources and vectors of the
pandemic, and receiving information from aggregated data elements. As health services
adapt to cope with whole-population monitoring, the GNU Health Federation with its new
real-time observatory functions can be a valuable partner system.

13. Many people have worked with us at this weekend hackaton. The European Commision, to
the organizers, volunteers, the coordinator and mentors!. On befalf of our team, thank you!
Name Country Role
Armand Mpassy Germany Team member
Joao Santinha Portugal Team member
Edgar Hagenbichler Austria Team member
Tom Kane Scotland Team member
Leonardo D’acchille Argentina Team member
Thomas Karopka Germany Team member
Ingrid Spessotti Argentina Team member
Axel Braun Germany Team member
Fernando Sassetti Argentina Team member
Abdrahman Elkafil Morocco Team member
Luis Falcon Spain Team member
Gus Hinestrosa Spain Team coordinator
George Kakoulidis Greece Volunteer mentor