Paper by Satchit Balsari, Mathew V. Kiang, and Caroline O. Buckee: “…In recent years, large-scale streams of digital data on medical needs, population vulnerabilities, physical and medical infrastructure, human mobility, and environmental conditions have become available in near-real time. Sophisticated analytic methods for combining them meaningfully are being developed and are rapidly evolving. However, the translation of these data and methods into improved disaster response faces substantial challenges. The data exist but are not readily accessible to hospitals and response agencies. The analytic pipelines to rapidly translate them into policy-relevant insights are lacking, and there is no clear designation of responsibility or mandate to integrate them into disaster-mitigation or disaster-response strategies. Building these integrated translational pipelines that use data rapidly and effectively to address the health effects of natural disasters will require substantial investments, and these investments will, in turn, rely on clear evidence of which approaches actually improve outcomes. Public health institutions face some ongoing barriers to achieving this goal, but promising solutions are available….(More)”
WHO, Germany open Hub for Pandemic and Epidemic Intelligence in Berlin
Press Release: “To better prepare and protect the world from global disease threats, H.E. German Federal Chancellor Dr Angela Merkel and Dr Tedros Adhanom Ghebreyesus, World Health Organization Director-General, will today inaugurate the new WHO Hub for Pandemic and Epidemic Intelligence, based in Berlin.
“The world needs to be able to detect new events with pandemic potential and to monitor disease control measures on a real-time basis to create effective pandemic and epidemic risk management,” said Dr Tedros. “This Hub will be key to that effort, leveraging innovations in data science for public health surveillance and response, and creating systems whereby we can share and expand expertise in this area globally.”
The WHO Hub, which is receiving an initial investment of US$ 100 million from the Federal Republic of Germany, will harness broad and diverse partnerships across many professional disciplines, and the latest technology, to link the data, tools and communities of practice so that actionable data and intelligence are shared for the common good.
The WHO Hub is part of WHO’s Health Emergencies Programme and will be a new collaboration of countries and partners worldwide, driving innovations to increase availability of key data; develop state of the art analytic tools and predictive models for risk analysis; and link communities of practice around the world. Critically, the WHO Hub will support the work of public health experts and policy-makers in all countries with the tools needed to forecast, detect and assess epidemic and pandemic risks so they can take rapid decisions to prevent and respond to future public health emergencies.
“Despite decades of investment, COVID-19 has revealed the great gaps that exist in the world’s ability to forecast, detect, assess and respond to outbreaks that threaten people worldwide,” said Dr Michael Ryan, Executive Director of WHO’s Health Emergency Programme. “The WHO Hub for Pandemic and Epidemic Intelligence is designed to develop the data access, analytic tools and communities of practice to fill these very gaps, promote collaboration and sharing, and protect the world from such crises in the future.”
The Hub will work to:
- Enhance methods for access to multiple data sources vital to generating signals and insights on disease emergence, evolution and impact;
- Develop state of the art tools to process, analyze and model data for detection, assessment and response;
- Provide WHO, our Member States, and partners with these tools to underpin better, faster decisions on how to address outbreak signals and events; and
- Connect and catalyze institutions and networks developing disease outbreak solutions for the present and future.
Dr Chikwe Ihekweazu, currently Director-General of the Nigeria Centre for Disease Control, has been appointed to lead the WHO Hub….(More)”
The Open-Source Movement Comes to Medical Datasets
Blog by Edmund L. Andrews: “In a move to democratize research on artificial intelligence and medicine, Stanford’s Center for Artificial Intelligence in Medicine and Imaging (AIMI) is dramatically expanding what is already the world’s largest free repository of AI-ready annotated medical imaging datasets.
Artificial intelligence has become an increasingly pervasive tool for interpreting medical images, from detecting tumors in mammograms and brain scans to analyzing ultrasound videos of a person’s pumping heart.
Many AI-powered devices now rival the accuracy of human doctors. Beyond simply spotting a likely tumor or bone fracture, some systems predict the course of a patient’s illness and make recommendations.
But AI tools have to be trained on expensive datasets of images that have been meticulously annotated by human experts. Because those datasets can cost millions of dollars to acquire or create, much of the research is being funded by big corporations that don’t necessarily share their data with the public.
“What drives this technology, whether you’re a surgeon or an obstetrician, is data,” says Matthew Lungren, co-director of AIMI and an assistant professor of radiology at Stanford. “We want to double down on the idea that medical data is a public good, and that it should be open to the talents of researchers anywhere in the world.”
Launched two years ago, AIMI has already acquired annotated datasets for more than 1 million images, many of them from the Stanford University Medical Center. Researchers can download those datasets at no cost and use them to train AI models that recommend certain kinds of action.
Now, AIMI has teamed up with Microsoft’s AI for Health program to launch a new platform that will be more automated, accessible, and visible. It will be capable of hosting and organizing scores of additional images from institutions around the world. Part of the idea is to create an open and global repository. The platform will also provide a hub for sharing research, making it easier to refine different models and identify differences between population groups. The platform can even offer cloud-based computing power so researchers don’t have to worry about building local resource intensive clinical machine-learning infrastructure….(More)”.
The “Onion Model”: A Layered Approach to Documenting How the Third Wave of Open Data Can Provide Societal Value
Blog post by Andrew Zahuranec, Andrew Young and Stefaan Verhulst: “There’s a lot that goes into data-driven decision-making. Behind the datasets, platforms, and analysts is a complex series of processes that inform what kinds of insight data can produce and what kinds of ends it can achieve. These individual processes can be hard to understand when viewed together but, by separating the stages out, we can not only track how data leads to decisions but promote better and more impactful data management.
Earlier this year, The Open Data Policy Lab published the Third Wave of Open Data Toolkit to explore the elements of data re-use. At the center of this toolkit was an abstraction that we call the Open Data Framework. Divided into individual, onion-like layers, the framework shows all the processes that go into capitalizing on data in the third wave, starting with the creation of a dataset through data collaboration, creating insights, and using those insights to produce value.
This blog tries to re-iterate what’s included in each layer of this data “onion model” and demonstrate how organizations can create societal value by making their data available for re-use by other parties….(More)”.
Innovative Data for Urban Planning: The Opportunities and Challenges of Public-Private Data Partnerships
GSMA Report: “Rapid urbanisation will be one of the most pressing and complex challenges in low-and-middle income countries (LMICs) for the next several decades. With cities in Africa and Asia expected to add more than one billion people, urban populations will represent two-thirds of the world population by 2050. This presents LMICs with an interesting opportunity and challenge, where rapid urbanisation can both contribute to economic or poverty growth.
The rapid pace and unequal character of urbanisation in LMICs has meant that not enough data has been generated to support urban planning solutions and the effective provision of urban utility services. Data-sharing partnerships between the public and private sector can bridge this data gap and open up an opportunity for governments to address urbanisation challenges with data-driven decisions. Innovative data sources such as mobile network operator data, remote sensing data, utility services data and other digital services data, can be applied to a range of critical urban planning and service provision use cases.
This report identifies challenges and enablers for public-private data-sharing partnerships (PPPs) that relate to the partnership engagement model, data and technology, regulation and ethics frameworks and evaluation and sustainability….(More)”
Remove obstacles to sharing health data with researchers outside of the European Union
Heidi Beate Bentzen et al in Nature: “International sharing of pseudonymized personal data among researchers is key to the advancement of health research and is an essential prerequisite for studies of rare diseases or subgroups of common diseases to obtain adequate statistical power.
Pseudonymized personal data are data on which identifiers such as names are replaced by codes. Research institutions keep the ‘code key’ that can link an individual person to the data securely and separately from the research data and thereby protect privacy while preserving the usefulness of data for research. Pseudonymized data are still considered personal data under the General Data Protection Regulation (GDPR) 2016/679 of the European Union (EU) and, therefore, international transfers of such data need to comply with GDPR requirements. Although the GDPR does not apply to transfers of anonymized data, the threshold for anonymity under the GDPR is very high; hence, rendering data anonymous to the level required for exemption from the GDPR can diminish the usefulness of the data for research and is often not even possible.
The GDPR requires that transfers of personal data to international organizations or countries outside the European Economic Area (EEA)—which comprises the EU Member States plus Iceland, Liechtenstein and Norway—be adequately protected. Over the past two years, it has become apparent that challenges emerge for the sharing of data with public-sector researchers in a majority of countries outside of the EEA, as only a few decisions stating that a country offers an adequate level of data protection have so far been issued by the European Commission. This is a problem, for example, with researchers at federal research institutions in the United States. Transfers to international organizations such as the World Health Organization are similarly affected. Because these obstacles ultimately affect patients as beneficiaries of research, solutions are urgently needed. The European scientific academies have recently published a report explaining the consequences of stalled data transfers and pushing for responsible solutions…(More)”.
Designing data collaboratives to better understand human mobility and migration in West Africa
“The Big Data for Migration Alliance (BD4M) is released the report, “Designing Data Collaboratives to Better Understand Human Mobility and Migration in West Africa,” providing findings from a first-of-its-kind rapid co-design and prototyping workshop, or “Studio.” The first BD4M Studio convened over 40 stakeholders in government, international organizations, research, civil society, and the public sector to develop concrete strategies for developing and implementing cross- sectoral data partnerships, or “data collaboratives,” to improve ethical and secure access to data for migration-related policymaking and research in West Africa.
BD4M is an effort spearheaded by the International Organization for Migration’s Global Migration Data Analysis Centre (IOM GMDAC), European Commission’s Joint Research Centre (JRC), and The GovLab to accelerate the responsible and ethical use of novel data sources and methodologies—such as social media, mobile phone data, satellite imagery, artificial intelligence—to support migration-related programming and policy on the global, national, and local levels.
The BD4M Studio was informed by The Migration Domain of The 100 Questions Initiative — a global agenda-setting exercise to define the most impactful questions related to migration that could be answered through data collaboration. Inspired by the outputs of The 100 Questions, Studio participants designed data collaboratives that could produce answers to three key questions:
- How can data be used to estimate current cross-border migration and mobility by sex and age in West Africa?
- How can data be used to assess the current state of diaspora communities and their migration behavior in the region?
- How can we use data to better understand the drivers of migration in West Africa?…(More)”
Developing a Responsible and Well-designed Governance Structure for Data Marketplaces
WEF Briefing Paper: “… extracts insights from the discussions with thought leaders and experts to serve as a point of departure for governments and other members of the global community to discuss governance structures and regulatory frameworks for Data Marketplace Service Providers (DMSPs), the primary operators and managers of data exchanges as trusted third parties, in data marketplaces and exchanges in a wide range of jurisdictions. As decision-makers globally develop data marketplace solutions specific to their unique cultural nuances and needs, this paper provides insights into key governance issues to get right and do so with global interoperability and adaptability in mind….(More)”.
Who will benefit from big data? Farmers’ perspective on willingness to share farm data
Paper by Airong Zhang et al : “Agricultural industries are facing a dual challenge of increasing production to meet the growing population with a disruptive changing climate and, at the same time, reducing its environmental impacts. Digital agriculture supported by big data technology has been regarded as a solution to address such challenges. However, realising the potential value promised by big data technology depends upon farm-level data generated by digital agriculture being aggregated at scale. Yet, there is limited understanding of farmers’ willingness to contribute agricultural data for analysis and how that willingness could be affected by their perceived beneficiary of the aggregated data.
The present study aimed to investigate farmers’ perspective on who would benefit the most from the aggregated agricultural data, and their willingness to share their input and output farm data with a range of agricultural sector stakeholders (i.e. other farmers, industry and government statistical organisations, technology businesses, and research institutions). To do this, we conducted a computer-assisted telephone interview with 880 Australian farmers from broadacre agricultural sectors. The results show that only 34 % of participants regarded farmers as the primary beneficiary of aggregated agricultural data, followed by agribusiness (35 %) and government (21 %) as the main beneficiary. The participants’ willingness to share data was mostly positive. However, the level of willingness fluctuated depending on who was perceived as the primary beneficiary and with which stakeholder the data would be shared. While participants reported concerns over aggregated farm data being misused and privacy of own farm data, perception of farmers being the primary beneficiary led to the lowest levels of concerns. The findings highlight that, to seize the opportunities of sustainable agriculture through applying big data technologies, significant value propositions for farmers need to be created to provide a reason for farmers to share data, and a higher level of trust between farmers and stakeholders, especially technology and service providers, needs to be established….(More)”.
Mapping Africa’s Buildings with Satellite Imagery
Google AI Blog: “An accurate record of building footprints is important for a range of applications, from population estimation and urban planning to humanitarian response and environmental science. After a disaster, such as a flood or an earthquake, authorities need to estimate how many households have been affected. Ideally there would be up-to-date census information for this, but in practice such records may be out of date or unavailable. Instead, data on the locations and density of buildings can be a valuable alternative source of information.
A good way to collect such data is through satellite imagery, which can map the distribution of buildings across the world, particularly in areas that are isolated or difficult to access. However, detecting buildings with computer vision methods in some environments can be a challenging task. Because satellite imaging involves photographing the earth from several hundred kilometres above the ground, even at high resolution (30–50 cm per pixel), a small building or tent shelter occupies only a few pixels. The task is even more difficult for informal settlements, or rural areas where buildings constructed with natural materials can visually blend into the surroundings. There are also many types of natural and artificial features that can be easily confused with buildings in overhead imagery.
In “Continental-Scale Building Detection from High-Resolution Satellite Imagery”, we address these challenges, using new methods for detecting buildings that work in rural and urban settings across different terrains, such as savannah, desert, and forest, as well as informal settlements and refugee facilities. We use this building detection model to create the Open Buildings dataset, a new open-access data resource containing the locations and footprints of 516 million buildings with coverage across most of the African continent. The dataset will support several practical, scientific and humanitarian applications, ranging from disaster response or population mapping to planning services such as new medical facilities or studying human impact on the natural environment….(More)”.