Data sharing, distributed factories: open source against Covid-19

The pandemic is bringing about a wave of global cooperation among researchers, scientists, and doctors. Open source tools and methods are amplifying and supporting this momentum.

“Faced with the Covid-19 pandemic, individuals and organisations are drawing on global resources in order to meet local needs.”

Since the start of the Covid-19 crisis, thousands of open source projects have been emerging across the world to model the pandemic and develop solutions to mitigate the shortage of equipment. On GitHub for example there are over 25,000 repositories associated with Covid-19. In parallel, scientific research is opening up like never before. Institutions such as Johns Hopkins University in the United States are freely sharing their data, whilst prestigious journals including “The Lancet” or “Science” are sharing their publications, and laboratories their research results.

Modelling Covid-19

Modelling pandemic-related data helps to understand the extent, progression and impacts of the virus, and helps with decision-making. It is the reason why many countries and institutions are allowing the general public to freely view and use their data. The dataset that is the most used by health authorities, researchers, data scientists, and journalists today is that of the Johns Hopkins University Center for Systems Science and Engineering (CSSE). At the end of January 2020, it published an interactive dashboard based on several sources, and in particular on the Chinese community platform DXY. This dashboard makes it possible to follow in virtually real time, the number of confirmed, recovered, and deceased cases of Covid-19 in the world. The data that is gathered, updated daily, and available in a GitHub repository, has already been reused in many visualisations and algorithmic models.

For its part, the Penn Medicine Predictive Healthcare team, of the renowned academic medical centre in Pennsylvania, has developed an open source tool to assist hospitals in planning their capacities. CHIME provides them with estimates of the number of hospitalisations, admissions to intensive care, and patients requiring respiratory assistances, based on the SIR epidemic model.

Mitigating shortages of equipment

With national shortages of protective devices and medical equipment arising, communities of makers rapidly got into action to meet the needs of hospitals, nursing homes, and of professions in the front line facing the coronavirus by prototyping masks, protective screens, detection kits, accessories for breathing assistance apparatuses, and even ventilators.

 

YouTuber Monsieur Bidouille (French for “Mr. Tinker”) thus explains how French makers set up a “distributed factory” for protective face-shields. To coordinate the production and distribution of the thousands of face-shields by people throughout the country, the makers set up into local groups, who then contacted care workers in their area to find out their needs and organise a logistics chain. The tinkerers and FabLabs receive orders via Facebook groups. They produce the face-shields in their homes. These face-shields are then collected, disinfected, put together, and delivered along with an instruction manual. Being open source, the plans could quickly go through several iterations and be adapted for different uses. Approved by several hospitals, these models are now inspiring manufacturers.


In France, but also in Spain, Poland and several countries across Africa and the Middle East (Cameroon, Côte d’Ivoire, Senegal, Tunisia, etc.), around fifty Solidarity FabLabs, supported by the Orange Foundation, have set about the production of over 200,000 visors in collaboration with hospitals.

Some makers have gone as far as designing prototypes of ventilators that are more or less complete. However, many ongoing projects are highlighting the limits with which these initiatives are faced. Complex and putting patients’ lives at stake, these devices must be subjected to stringent tests and be awarded certification before they can be used in hospitals.

Production also meets several requirements. The design and manufacture of open source ventilators are not aimed at simple tinkerers, which is emphasized by a volunteer team trained within the MIT in Boston that is developing a simple and low-cost emergency ventilator.

In France, a citizen group of doctors, scientists, and association managers, at the origin of the MUR (Minimal Universal Respirator) ventilator project developed with the support of intensive care doctors, has set about the first step of the certification process. The MakAir project, a life support machine specifically for Covid-19 patients designed by the Makers for Life group, is also undergoing tests.

The opening up of science

Open science consists in opening up access to research data and scientific publications. This is the mantra of the OpenCovid19 collaborative programme, hosted on the Just One Giant Lab (JOGL) platform, a research and innovation laboratory based in Paris that brings together scientists, engineers, healthcare professionals, etc., around the resolution of problems of general interest thanks to the use of open source tools and methods. OpenCovid19 aims to develop a series of low-cost open source solutions to fight the pandemic. Voluntary contributors from all over the world are carrying out projects dedicated to the detection, prevention, and treatment of the disease. The first result is a diagnostic test.

Many researchers and political decision-makers underline the role of open science in getting Covid-19 research to make progress. According to them, knowledge sharing is essential in order to accelerate understanding of the virus and the development of a treatment and vaccine. The UNESCO Director-General has called on governments to “integrate open science in their research programmes”. As for the European Commission, it has launched an online coronavirus data platform, providing “an open, trusted, and scalable European and global environment where researchers can store and share datasets, such as DNA sequences, protein structures, data from pre-clinical research and clinical trials, as well as epidemiological data”.

The strengths of the open source model

These implementations of collective intelligence using an open source approach make it possible to improvise simple, efficient, and low-cost solutions in emergency situations. According to Monsieur Bidouille, volunteers thus make up a true research and development department. Enthusiastic contributors actively take part in the development and continuous improvement of the projects. Innovators, researchers, and developers work together, sometimes in cooperation with citizens, sharing the resources and knowledge produced so as to solve a common problem. This community dimension is the main force of the open source model, as opposed to the proprietary model, which puts people into competition with one another.

This global community knows how to structure itself on a local scale, as shown by the example of the protective face-shield production. The source codes, prototypes, and data being open, anybody can use them, modify them, and adapt them to specific constraints. Faced with the Covid-19 pandemic, a virus that knows no boundaries but that affects countries at different times and in different ways, individuals and organisations are drawing on global resources, made available by mass collaboration and a robust and resilient technological infrastructure, in order to meet local needs.

Read also on Hello Future

Smart vehicles: new technology beams 3D images of obstacles into drivers’ eyes

Discover

NORIA: Network anomaly detection using knowledge graphs

Discover

FoodProX uses machine learning to detect ultra-processed food

Discover

Biofuel cells break new ground in the field of flexible batteries

Discover

In-Vehicle Commerce, a white paper co-authored by Orange and Worldline

Discover

The challenge: Brain-computer interfaces that work for everyone

Discover

Money laundering: a novel approach with new algorithms to combat smurfing

Discover

AI in Medicine: Uses and Consequences on Radiology Work

Discover