Global Pandemic Data Alliance Landscape Report

Initial findings on international efforts to bolster pandemic preparedness from


As the G7 Presidency transfers from the UK to Germany, The UK Cabinet Office has asked to do a high-level mapping of significant global efforts in designing and deploying pandemic surveillance data infrastructure currently in flight. Since COVID-19 was declared a global pandemic, a huge number of pandemic preparedness efforts have arisen. The goal of this landscaping project is to identify what outcomes various initiatives are focused on and which major players are working toward those outcomes. The hope is that this landscape can help inform efforts moving forward on preventing pandemics using international coordination and digital infrastructure.

In this paper you will find a categorization of some of the key players in international pandemic preparedness, profile cards describing individual organizations more deeply, and a link to a full dataset we prepared. Combined with our interviews, we found great need for consistent data science tooling, more collaborative funding models, and more voices from the Global South in readying a global response to pandemics.

It should be noted that this is an exceedingly early draft and by no means comprehensive. This first version was built from information largely within our own networks. As such, there are very few voices from the Global South nor from private corporations (the authors hail from the social sectors in the US and the UK). In 2022 we intend to build on this landscape further to better represent those voices. If you have information you’d like to contribute to the landscape or feel an initiative should be represented differently, please contact us here.

Key Findings

Through our desk research and interviews with partners, four major themes have started to emerge, which will need to be tested, extended and refined as we continue to conduct more interviews in 2022:

  • The existing models of data analysis for preparedness are helpful, but lack the scalability tools could bring. In our research, we saw only a small number of groups specifically targeting generalizable tooling and analytics. Moreover, they most often took the form of one of two models; in-house analysis and consultancies. The in-house analysis (e.g., Johns Hopkins’s COVID models) took it on themselves to create results for the world to use. The consultancies provide data scientists on-loan, paid or as volunteers, to give organizations temporary data science capacity. That model can help with individual projects, but is difficult to scale to long-term capacity. Our interviewees often reported the lack of continuity and sustainability of in-house solutions as a major hindrance to data-driven pandemic preparedness. They also cited opportunities to use new, privacy-preserving analytics that had not yet been fully utilized.  COVID-19 Forecast Hub and ICODA are some of the initiatives leading on helping the scientific community create, share, and maintain open-source tools. More software tools that are sustainable, use modern technologies, and can generalize to a wide audience would fill a much-needed gap in the market, allowing more institutions to use data science for pandemic preparedness in the future.
  • Funders need to fund cooperation and collaboration. Overwhelmingly our interviewees shared that one of their biggest risks to their success was not related to data or technology at all, but to sustainable funding. Moreover, they cited spending a lot of time managing the politics of competing initiatives so as not to duplicate work or step on toes. They credited this to each funder wanting to have a personal portfolio of projects, thus often funding duplicate efforts. The funding structure incentivizes each group to be as unique as possible, when many cited wanting to work with others and not compete. There is a huge opportunity for nation states, international agencies, and foundations to collaboratively fund efforts or to specifically fund collaboration between key initiatives with similar data and aims.
  • Pandemic response is a global problem being tackled by a small set of people. The organizations we spoke with almost exclusively cited university researchers and medical professionals (doctors, virologists, heads of ministries of health) as their main users. These researchers were both the main creators and consumers of the data and analytics. In speaking with one partner, they lamented that the frontline workers in many LMICs, who could be hit hardest by pandemics, were left out of conversation. Another cited a need for more equitable access to data and data science solutions, especially concerning countries in the global south. With the exception of a Trinity Challenge entry by Living Goods, we saw very few groups from the Global North focusing on community health workers, local clinics, or the public as data creators or consumers. While it’s not clear what specific effects that would have, it’s almost certainly a guarantee that the types of problems solved and who the solutions serve will be set by the international researchers and academics that drive most of today’s pandemic response initiatives.
  • Data Platforms that allow harmonization of data will be key to the next phase of pandemic preparedness. Even more than initiatives publishing open datasets, the Data Platforms (see our categories in the Landscape section) will play a pivotal role in uniting and harmonizing the data needed for pandemic preparedness. Every action, from understanding the state of play, to scenario planning, will depend on cross-boundary data from many parties. Initiatives like the WHO’s Hub for Pandemic Intelligence and the CDC Data Modernization Initiative International COVID-19 Data Alliance are likely to play influential roles. As many of these initiatives are new, however, it would behoove them to learn from more longstanding Data Platforms, like the Infectious Disease Data Observatory, which has overcome many legal, political, and technological hurdles to curating and harmonizing research data.


In order to learn more about the efforts in this space, we sent surveys to key contacts at the CDC, WHO, and other international organizations where they could describe their efforts and identify other partners in the space doing relevant work. We also did seven deep dive interviews with partners at the CDC Forecasting Outbreaks and Analytics, World Health Organization Global Pandemic Hub, Milken Institute, Infectious Diseases Data Observatory (IDDO), International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC), the International COVID-19 Data Alliance, and Public Health Informatics Institute. Through speaking with our contacts we identified other landscaping efforts in this space which we incorporated into our own work to minimize duplication of effort. For example, ICODA created an extensive list of ~150 initiatives using data to combat COVID-191, first created in Nov 2020 and subsequently revised in June 2021, which we have further categorized and used in our landscape. Lastly, we employed desk research to identify other organizations committed to using data to prevent pandemics, identifying 50 initiatives working to prevent pandemics that did not overlap with other partners’ landscapes.

Landscape of Pandemic Data Projects

Below you will see an image of our pandemic preparedness landscape. Each category encompasses a common set of outcomes and activities in using data for pandemic preparedness. We opted to show just a few example logos in each category so as not to crowd the image, but all organizations we researched are in the Appendix. We also note that this is an early draft of this landscape and, as such, the categories could change given more data or expert feedback. This version of the categories is our attempt to balance simplicity and completeness, as well as to emphasize differences by use of data and data science over other features.

In order to understand the landscape, it’s worth a discussion of how these categories were identified. For each initiative, we recorded information about the regions it operated in, the type of institution it is, its founding date, and other stats about its identity. In addition, we used a very coarse classification system to organize initiatives based on the following three dimensions:

The stage of the data science pipeline they work to improve: Each initiative affects one or more stages of the data pipeline in battling pandemics. Where applicable, we tagged which stages their efforts helped with. The model of the data science pipeline we are using is below:

StagePossible Activities
CollectCreate a dataset  
Create software for users to collect their own data
CurateHelp people find a dataset they need for their research

Create a platform on which people share data with one another   Create standards for data sharing   Provide data infrastructure
StaffProvide data science training   Provide data and data science consulting capacity
ToolsCreate data science tools that researchers can use to be more effective in creating their own solutions
SolutionsDirectly create a data-driven solution that people can use or understand off-the-shelf. This category includes interactive visualizations, models, and any other output that results from applying data
End UseImprove peoples’ capacity to utilize, trust, and understand the results from data science outputs.   Provide the latest research findings for users

Real world application: Some initiatives focused on one specific part of pandemic mitigation. Where applicable, we classified them as:

  • Early detection (One Health is included here)
  • Pathogen research
  • Surveillance
  • Treatment and mitigation (including vaccination)
  • Forecasting and scenario planning
  • Policymaking
  • Meta analysis
    • Automated policy analysis and comparison
    • Model efficacy testing
    • Research summarization

Implementation: If the initiative acted in any particularly unique way, e.g. convening stakeholders online or combining multiple models, that was noted as well.

With that, we ended up with the broad categories of efforts in the landscape, described below. Note that many organizations provide multiple services, e.g. IDDO is a Data Platform as well as a Tool Provider. The profile cards in the next section have more nuanced analyses of each initiative’s work for select partners we were able to interview.

Dataset Providers

These organizations are primarily focused on making different types of data available to researchers by hosting data. They differ from the next category, Data Platforms, as they do not explicitly attempt to connect researchers to one another, combine data, or add any extra value besides access to the data. These dataset providers are useful data repositories for those looking to add data to their research. Clinical trial databases showed up commonly in our research, and would be listed in this category.

Example initiatives:

  • CDC’s
  • Health Data Gateway
Data Platforms

Data Platform providers seek to create connections between datasets and researchers so that more holistic research can be done1. The initiatives in this group offer a variety of different services atop the combined data. IDDO, for example, attempts to connect researchers studying the same disease who may not know about one another. ICODA provides an actual software workbench, within which multiple researchers can work together on the same data. The CDC Data Modernization Initiative is building platforms that harmonize data across many different research facilities. Many Data Platforms we encountered are agnostic to the use of the data being served. What they are all focused on, however, are standardized ways of discovering, sharing, and combining the data, a major hurdle to full visibility of disease behavior.

Example Initiatives:

  • IDDO
  • WHO Hub for Pandemic Intelligence
  • CDC Data Modernization Initiative
Pathogen Research

A specialized form of Dataset Providers and Data Platforms, the Pathogen Research initiatives focus primarily on understanding of the disease, its behaviors, and its interaction with living beings. The initiatives in this category perform many of the same functions as their parent groups of Dataset Providers and Data Platforms, but focus specifically on pathogenic understanding separate from all other uses of the data. Note that we included vaccine development in this category to attempt to keep the landscape small and focused on differentiation by data science activities as much as possible.

Example Initiatives:

  • Africa CDC Institute of Pathogen Genomics
  • Center for Research in Emerging Infectious Diseases
Early Detection

The Early Detection initiatives are specifically focused on reducing the time to detect outbreaks of infectious diseases. These initiatives often share data and research workbenches as the general Dataset Providers and Data Platforms do, but they specialize in zoonomical data and surveillance. The One Health approaches fall into this category, attempting to identify and track novel diseases in non-human life that could infect humans.

Example Initiatives:

  • FAO/OIE/WHO Tripartite Plus
  • Rockefeller Pandemic Prevention Initiative
  • Milken Institute
  • Ending Pandemics
  • PATH

There are a number of initiatives whose main focus is applying data to understand the current state of the world, be that number of infections, number of people infected, or policies by state. They often take the form of interactive visualizations or dashboards and are primarily targeted toward decision makers and the public. Examples include the New York Times’s model of COVID-19 spread or the UK’s “Coronavirus in the UK” dashboard, which allow the public to understand how the disease is moving over time. Most of the initiatives we encountered on this front were, understandably, geared toward COVID-19 on account of its presence today. Note that almost all Surveillance initiatives are also Dataset Provider initiatives, as they almost always make their data available.

Example Initiatives:

Forecasting and Scenario Modeling

Once an outbreak has occurred, the Forecasting and Scenario Modeling initiatives seek to model the likelihood of future events. These forecasts can ideally be used by decision makers to react more quickly and effectively. Common forecasts of interest are the spread and prevalence of the disease, and predicting the outcomes of proposed policy changes. These initiatives all use fairly sophisticated data science and statistical modeling techniques to build their forecasts, requiring them to have high caliber technical talent in-house.

Example Initiatives:

  • Center for Disease Control – Center for Forecasting and Outbreak Analytics
  • The DELPHI program at Carnegie Mellon University
  • COVID-19 Forecast Hub and Scenario Modeling Hub
  • ​​IHME
Tool Providers

A relatively small set of initiatives focus on creating generalized software tools for researchers to use in their pandemic preparedness work. For example, ICODA, in addition to providing datasets for researchers to use on their platform, also provides them with common analysis tools for running their studies. The COVID-19 Forecast Hub maintains an open source software package called COVIDcast that any researcher can use to create their own forecasts for COVID-19. As more data is combined in global disease surveillance efforts, more data science and algorithmic tools will be required, and these initiatives are committed to building and maintaining those tools.

Example Initiatives:

  • COVID-19 Forecast Hub
  • Institute for Disease Modeling
Skill Providers

These initiatives do not seek to create any particular software or finding, but instead provide data science and tech capacity to those who do. They are often volunteer models, either recruited from the public or on loan from companies like Johnson & Johnson. The projects are generally short-term, ranging from a 30 minute phone call to verify an outbreak with an EpiCore volunteer, to a few months to build out a dashboard of COVID-19 data with US Digital Response.

Example Initiatives:

  • US Digital Response
  • EpiCore
  • Crowdfight
  • Center for State and Territorial Epidemiologists
Support to Health Professionals

The last group of initiatives we found provide a holistic set of information to public health officials and researchers. While they often provide data or models directly, their most valuable contribution is in providing capacity to health professionals. Example services include helping departments of health design new health data infrastructure, advising on best practices in partnering with data stewards, or consulting on new data projects. Large member networks in this category often host workshops, trainings, or run member-led initiatives on special interest areas.

Example Initiatives:

  • Public Health Informatics Institute
  • Association of State and Territorial Health Officials
  • Center for State and Territorial Epidemiologists
  • The World Health Organization

It is worth noting that we began our research looking for major multi-country partnerships focused on pandemic preparedness. A number of promising initiatives reached our desk and came up in conversations, such as the UK Center for Pandemic Preparedness and the International Pathogen Surveillance Radar. The mission statements for these initiatives sound quite promising and all have backing from major institutions. However, many of these initiatives are early enough in their journeys that we can not comment on their work at this point. We will stay abreast of these initiatives as they develop.


  • International Pathogen Surveillance Radar
  • UK Center for Pandemic Preparedness
  • Center for Epidemic Response and Innovation

Key Profiles

We hope the categories in the landscape are helpful for thinking about the types of initiatives tackling each cross section of the disease and data pipelines, and ideally identifying where there are gaps and exemplars. Below you will find a more in-depth summary of key international efforts for pandemic preparedness that we spoke with. These initiatives were either interviewed by us or came up repeatedly during our conversations, indicating a level of visibility that makes them important to our research. All are connected to large national or international efforts, and thus may have outsized influence on setting standards for pandemic preparedness. We reemphasize here that our research is not exhaustive and that these organizations skew toward the authors’ networks, specifically western HICs. While this landscape is a start, it is imperative that initiatives outside the US/EU region be included.

Infectious Diseases Data Observatory


Contacts: Laura Merson

Founding Date: 2016 (with origins in the WWARN system of 2011)

Region Served: Global

Diseases Addressed: COVID-19, Ebola, Malaria, Visceral Leishmaniasis, Schistosomiasis, Chagas Disease, Febrile Illness, Scrub Typhus

Overview: The Infectious Diseases Data Observatory (IDDO) is a scientifically independent coalition of the global infectious disease and emerging infections communities. It specializes in the curation and combination of datasets about infectious diseases across different countries, research bodies, formats, languages, and more. Researchers can share their data with IDDO, which then enables them to connect their data with other researchers in the network. Researchers can then analyze much larger, richer pooled datasets, which increases the power of their studies.

IDDO staff play an important role in the curation of their data that goes above and beyond simply ensuring data can be uploaded and accessed. When IDDO decides to support a disease, staff work to ensure they understand what data already exists and who is doing influential work in that area. The nature of this activity differs from disease to disease. For example, during the Ebola pandemic, IDDO actively worked with Ministries of Health to share and use Ebola data, as those entities were leading the response. For another rare disease, by contrast, a very active researcher helped marshall others to share their data. In all cases, IDDO also helps with the non-technical political and legal hurdles to uniting this data. IDDO’s intentional efforts to manage the human aspects of their disease network is an important aspect of unlocking the power of the data they provide.

Pain Points they Address: IDDO mainly focuses on the pain point of curating datasets that come from varied sources. In addition to solving anonymization, harmonizing, and interoperability problems that researchers may face when trying to combine data, IDDO is also critical for ensuring data longevity – protecting data from being lost when researchers change institutions, pass away, or otherwise stop maintaining the data. Moreover, many of the datasets researchers collect are too small and underpowered to questions beyond the primary outcome of the research study. By combining and harmonizing datasets, IDDO has helped researchers perform more in-depth studies of rare diseases and under-represented populations, helped Ministries of Health during pandemic response, and helped regulators access data to support the evaluation of regulatory submissions, for example data about the natural history of the disease.

CollectN/A – IDDO does not explicitly collect data
CurateThis is IDDO’s strength: researchers can upload data and have it anonymized, tagged, sorted, and standardized for use with other researchers working on that disease. IDDO also manages legal and political obstacles in harmonizing the data.
StaffIn support of IDDO’s pursuit of equitable data reuse, IDDO invests in a variety of training opportunities for researchers from LMICs to advance their analytic and data science skills.
ToolsThere are tools on IDDO’s site for researchers to use in their analysis, some developed by IDDO partners
SolutionsIDDO partners publish some of their findings on IDDO’s website. There are also some dashboards and interactive visualizations that IDDO users have built available on the website.
End UseEvidence generated from use of data on the IDDO platform is cited in regional, national and WHO treatment policies.

Phases of disease preparedness they specialize in: Any. Data and analysis pertains to everything from disease prevalence to overviews of policy across countries.

Our Tag: Data Platform

Comparative Advantage: IDDO has been around for years and, as such, has developed its abilities to navigate the challenges of the data sharing landscape, and curate and manage data. Upstart initiatives will have to spend time determining the formats and data governance standards they want when combining datasets, while IDDO already has a pipeline that anonymizes data, makes it searchable, standardizes it into CDISC format, and makes it available to researchers.

Other notes:  IDDO is interested in accelerating its curation pipeline to assist in real-time pandemic response. To do that, they would need to extend their data capabilities to include more epidemiological and genomic data than they currently have and secure funding for a larger curation team. They also cite international data privacy regulations (e.g. GDPR) as a major hindrance to acquiring and combining data in real-time to identify emerging infections.

International Severe Acute Respiratory and Emerging Infections Consortium (ISARIC)


Contacts: Laura Merson

Founding Date: 2011

Region Served: Global

Diseases Addressed: COVID-19, Ebola, Bubonic Plague, Lassa Fever, Nipah virus

Overview: ISARIC’s purpose is to “prevent illness and deaths from infectious diseases outbreaks.” They specialize in creating large scale clinical trials by joining together a global network of researchers and research institutions. Members in the network can contribute and share data with one another, or propose that ISARIC carry out specific studies that its members contribute to. Being a member of ISARIC also entitles researchers to community benefits, such as Career Development fellowships. On the data science front, it seems ISARIC’s focus is more on developing standardized research tools, connecting researchers who can share and collect data for these large studies, and on analytics, than on providing curation of the disparate data contributed to the initiative.

ISARIC partnered with IDDO during COVID-19 to help process and align the mass amount of clinical trial data coming into them, taking advantage of IDDO’s data curation talent.

Pain Points they Address:  During a pandemic, researchers need rapid access to geographically diverse and large scale clinical datasets to build the most powerful models and to understand the disease across different regions and demographics. Many research institutions collect clinical data, but the datasets they can collect are small, local, or both. ISARIC provides a research network and infrastructure that allows sharing of these individual trials so they can be analysed and understood together, and executes global studies that multiple institutions from different parts of the world can contribute to.

CollectISARIC collects data in observational and interventional clinical studies during outbreaks of  plague, Lassa, Ebola and other diseases. ISARIC aggregates, analyses and enables sharing of data from global researchers on COVID-19 and other pandemic threats.
CurateProvide guides for uploading and sharing data with one another.
StaffISARIC provide statistical resources to LMIC researchers who wish to analyse their own, and the ISARIC global collection of, clinical data.
ToolsCollaborate with WHO to create protocols and data collection standards.
SolutionsISARIC produces regular reports and an open dashboard to share the results of analysis of the global COVID-19 data it hosts.
End UseOutputs of ISARIC analyses are some of the most highly powered, globally representative evidence, used to inform public health response.

Phases of disease preparedness they specialize in: Any

Our Tag: Support of Health Professionals

Comparative Advantage: ISARIC has a member network of 52 parties across 111 countries, so it has a global reach. It partners with the WHO to quickly scale implementation of the ISARIC-WHO formatted data collection tools.

Other notes: It is interesting that ISARIC and IDDO partnered during COVID-19, as their partnership could be a useful lesson in collaboration. ISARIC’s intake of clinical trial and patient data during COVID-19 was massive and unmanageable, and IDDO’s expertise in curation eased that burden. Intentionally funding collaborations like this could help foster a global response to pandemics.

International COVID-19 Data Alliance


Contacts: Neil Postlethwaite, Anne Wozencraft

Founding Date: 2020

Region Served: Global

Diseases Addressed: COVID-19

Overview: ICODA is part research network and part online data collaboration platform. Their main objective is to help researchers answer research questions about COVID-19 by helping them bring their data together safely. ICODA takes the word “safely” seriously, and has adopted a framework called the “five safes”3 – safe projects, safe data, safe people, safe settings, and safe outputs – that is in place to ensure responsible use of the data and results. They also provide a secure workbench environment for researchers to collaborate in. This workbench provides data science and research tools in addition to the data itself.  ICODA’s main activity for testing its system presently is through Driver Projects, which are select research projects that use their workbench software to solve real world research problems.

Pain Points they Address: ICODA takes a fairly holistic approach to supporting researchers. Their cloud hosted workbench provides a trusted research environment that allows for uploading and sharing of research data, collaboration spaces to allow researchers to work together, standard tools such as R, Python Jupyter, SQL and meta and visual analysis tools contributed from other ICODA members. Their gateway allows for data discovery and access request management. As such, they deal with the pain points researchers face around finding data, sharing data, and working collaboratively on research questions.

CollectN/A – ICODA does not explicitly collect data itself nor house it unless requested
CurateICODA acts as a repository for research data, which has strong information governance processes, facilitating trustworthy data sharing for research with public benefit.
ToolsICODA provides a “workbench” via its partner Aridhia Informatics, replete with reusable open source data science tools that researchers can use and a “gateway” which provides data discovery and secure access management services.
SolutionsICODA is committed to publicly releasing all outputs, be they models, findings, or software, that result from the twelve Driver Projects.
End UseN/A

Phases of disease preparedness they specialize in: Any

Our Tag: Data Platform

Comparative Advantage: ICODA provides a fairly extensive set of capabilities to researchers. In addition to providing a place to share data and collaborate on research together, ICODA also offers a cloud hosted trusted research environment, software environment, and tools to its users. Its focus on Driver Projects means it may learn more quickly about practical digital collaboration than a more general approach.

The ICODA initiative and its driver projects have a strong focus on the Global South, and on data equity. The researchers themselves are helping shape the platform, tools, policies and process that help them accelerate research progress and overcome challenges. Another key element of ICODA is trustworthiness in data science approaches to pandemic management/ preparedness and inclusion of community, patient and public engagement in all phases.

Other notes: ICODA is very much focused on the trustworthy information governance aspects of data sharing, building on a successful approach used by HDR UK with its Innovation Gateway and extending these data discovery and access request aspects to encompass trusted research environments within which researchers can work securely. All its efforts are via partnerships with software providers with Aridhia Informatics, SAIL databank and Cytel being prime examples providing cloud hosted trusted research environments, data storage and tooling to the initiative respectively.   

ICODA whilst started to immediately assist combat the COVID-19 pandemic is missioned with leaving behind a platform and trustworthy open governance processes, to enable an efficient data response to future pandemics and other global health challenges.

Hub for Pandemic and Epidemic Intelligence (WHO)


Contacts: Philip Abdelmalik, Dusan Milovanovic

Founding Date: September 21, 2021

Region Served: Global

Diseases Addressed: Any

Overview: The Hub for Pandemic and Epidemic Intelligence is a new platform for partnerships formed by the World Health Organization as a connection point for researchers battling pandemics. The main purpose of the hub is to “inspire, accelerate, and initiate collaboration” As an international agency, the WHO serves researchers in member states by exposing them to data, knowledge, and tools that can help them with disease preparedness. This help includes connecting them to datasets they may not otherwise know about or have access to, providing open source model libraries for them to use, and sharing research and analytics that have been performed on data connected through the Hub. An important aspect of the Hub’s work is the creation of schemas that allow standardized discovery and sharing of health data. Though a young initiative, the aims of the Hub are grand, hoping to be the global source of disease preparedness collaboration.

Pain Points they Address: Researchers worldwide struggle to work together around pandemic response. Currently, they may be isolated from one another, preventing opportunities to share data, tools, and findings that could help each others’ work. The Hub seeks to address this through being a collaborative community that provides “Better Data”, “Better Analysis”, “Better Decisions”.

CollectN/A – The Hub does not seek to create new data
CurateResearchers can find and access data for their needs through the Hub. Standardization will be served by EPI-BRAIN. The Hub will provide some infrastructure to support sharing and collaboration.
StaffN/A – While there is discussion of supporting Hub users, capacity building is not an explicit aim.
ToolsN/A – At this stage, general purpose tools are not being designed, but could be in the future.
SolutionsInsights and findings will be published: “Insights for pandemic and epidemic intelligence will be based on: effective use of semantically linked data; collaborative development of analytic tools; and translation of analyses into usable insights.”
End UseThe Implementation Accelerator will be established to work with communities of practice at local, regional and global levels to scale up and accelerate the adoption of new pandemic and epidemic intelligence solutions.

Phases of disease preparedness they specialize in: All

Our Tag: Data Platform

Comparative Advantage: The trust and international partnerships that the WHO brand and network confers.

Other notes: The Hub is clear that it does not intend to be a data collection initiative, as there is no need to hoard data. Instead, they would like to create the infrastructure that all health data, one day, is shared through. If successful, the Hub would be “the web for health data”, i.e. not a single repository or directory of data, but the very substrate through which health data is discovered, shared, and used.

CDC Center for Forecasting and Outbreak Analytics Initiative


Contacts: Dylan George, Rebecca Kahn, Marc Lipsitch

Founding Date: September 2021

Region Served: USA, with global collaboration

Diseases Addressed: Initial focus is on pathogens with pandemic potential

Overview: The Center for Forecasting and Outbreak Analytics aims to support national, city and state officials in the US with data and modeling for better decision making. The Center for Forecasting and Outbreak Analytics is specifically focused on improving short-term forecasts of disease outbreaks and disease spread, as well as scenario planning to help understand the implications of various policy decisions. The center would be expert at answering common questions, such as where the disease is likely to spread to and who would it affect, what the severity of the crisis would be under different scenarios, and which scenario we’re probabilistically heading towards. To do this, they will convene public health data, expert disease modelers, public health emergency responders, and high-quality communications.  They will also have an internal data science team to answer ad hoc analytic questions that decisionmakers may have.

Pain Points they Address: The CDC CFA addresses a number of pain points related to forecasting and modeling. First, they are tackling all barriers to creating forecast models, such as identifying the right data needed for robust modeling, supporting innovations in outbreak modeling, and establishing appropriate forecast horizons. Second, they are interested in helping remove barriers to accessing and sharing data needed for modeling and forecasting. Lastly, they are tackling the pain point of communicating forecast results by connecting with key decision makers across government to help them trust, understand and use the forecast results.

CollectCDC CFA may not collect data itself, but it may set standards and recommendations for how to collect data for certain types of modeling.
CurateCDC FOA is exploring revamping infrastructure for sharing and accessing data for forecast modeling. Of the two initiatives, the CDC’s Data Modernization effort is much more focused on collection and curation than CDC FOA.
StaffThe CDC FOA intends to build an analytic response team of data scientists who can tackle on-demand forecasting and modeling needs for high priority projects
ToolsSpecific tools planned are still in planning phases, but there is an intention to build and enhance data science and modeling tools created for forecasting and outbreak analytics.
SolutionsThe outputs of forecast models and scenario planning will be made available to the public and decisionmakers.
End UseCDC FOA is working closely with decisionmakers to make sure they understand models and can use them to inform decision making.

Phases of disease preparedness they specialize in: Forecasting and Scenario Planning

Our Tag: Forecasting and Scenario Planning

Comparative Advantage: The brand of the CDC confers trust in the USA. The team leading the effort has extensive experience in forecasting and disease analysis. They are one of the few forecasting centers we encountered outside academia devoted to building a strong internal data science capacity.

Other notes: It is important to CDC FOA that their results be authoritative. One pain point they alluded to is that, currently, anyone with a computer can generate some model that says something and claim it as “truth”. FOA not only seeks to accurately forecast outbreak scenarios, but also to give a trusted, authoritative viewpoint.

Milken Institute / FasterCures


Contacts: Carly Gaspa, Esther Krofah

Founding Date: September 2020

Region Served: All

Diseases Addressed: Any

Overview: The FasterCures team at the Milken Institute is designing a rapid, global early warning system for pandemics. As the COVID-19 pandemic accelerated, policymakers and government officials realized the fragmentation in existing early warning systems, which are largely event based and localized, and called for the creation of a robust, integrated global early warning system. Milken Institute is convening over 100 health experts to envision and scope this system. Workshops with these advisors tackle topics such as gap analysis, data sharing frameworks, and ensuring integration with existing global systems. Even as the details are being specified, early discussions have suggested a hub-and-spoke model, where regional surveillance efforts coordinate at the global level. The final model will also build on existing surveillance efforts, as opposed to creating entirely new systems. The team will focus its efforts on fleshing out the blueprint for this model while simultaneously working with partners to ensure trust and adoption of the final system.

Pain Points they Address: In the midst of COVID-19, there is great interest in a global early warning system for pandemics. Where many initiatives have jumped into solving isolated aspects of such a system, The Milken Institute is stepping back to envision how such a system would work, what its use cases would be, and how it would practically work through convenings with noted researchers around the globe. They are, in effect, the scoping and implementation team for the global early warning system that many are calling for.

CollectN/A at present. The final surveillance tool may entail collection activities.
CurateRegardless of the final design of the early warning system, the team anticipates data interoperability across major organizations (e.g. Africa CDC and US CDC) to be of utmost importance to this effort.
StaffN/A at present. It is unclear if any human capacity or training will be provided.
ToolsN/A at present.
SolutionsThe early warning models and their forecasts will be shared publicly
End UseN/A at present

Phases of disease preparedness they specialize in: Outbreak Detection

Our Tag: Early Warning

Comparative Advantage: Milken Institute specializes in convening experts from around the world and enjoys a robust network they can draw from. Their partnership with major organizations like the WHO means that they have both the ability to scope such an effort as well as connect to international agencies that can support taking it forward.Other notes: Milken Institute has secured funding from the Rockefeller Pandemic Prevention initiative to support implementation of this system, so there is a path forward for the creation of any solutions they arrive at.

GPDA Profiles

The members of GPDA, while helping form this R&D roadmap for the G7, are also themselves engaged in efforts for pandemic preparedness. Their profiles are included below for completeness.

Epiverse (


Contacts: Danil Mikhailov

Founding Date: September 2021

Region Served: Global

Diseases Addressed: Any

Overview: COVID-19 exposed major gaps in our collective ability to use data to prevent, detect, and respond to a pandemic.’s “Epiverse” platform addresses these gaps by bringing together researchers, government, tech/cloud computing companies, social impact organizations, and funders in order to build, deploy, and scale innovative open-source software solutions. By unlocking insights in non-traditional data, new privacy-preserving distributed tools have potential beyond epidemiology to address broader social challenges.

Pain Points they Address: There are not many standardized data science tools available for researchers when dealing with pandemic preparedness. Even when new tools are created for the scientific community, there are very few avenues for funding and maintaining them. Finally, the tools that do exist are often developed in the Global North. Epiverse seeks to address those pain points by supporting teams to create open source and reusable epidemiological software tools, platforming them, and maintaining them as an international community-led effort, with tools designed and developed by contributors in the Global South as well as the Global North.

CurateAs Epiverse has a focus on privacy-preserving algorithms, some approaches to sharing and working that type of data are likely to emerge.
ToolsEpiverse’s main focus is supporting tool creation and maintenance for the wider scientific community to use
SolutionsNo doubt the applications of Epiverse tools will be made available to the public
End UseN/A

Phases of disease preparedness they specialize in: Any

Our Tag: Tool Providers

Comparative Advantage: plays a unique role in being part implementer and part funder. For that reason, they may be able to provide one of the missing pieces of scientific tool creation – funding and maintenance.

Other notes: Epiverse has an explicit focus on privacy-preserving technologies that could provide more opportunities than have been available in the past for gleaning insights from data,  possibly even across previously insurmountable legal borders.



Founding Date: 2019

Region Served: Global

Diseases Addressed: Any

Overview: The I-DAIR Project seeks to advance the UNSG’s High-level Panel on Digital Cooperation’s recommendations related to digital health and to help meet the targets set by the World Health Organization (WHO) on universal and quality health coverage. We work with diverse stakeholders on an international platform to promote responsible and inclusive AI research and digital technology development for health inter alia by moving towards data for health as a global public good and by addressing key governance, validation, benchmarking and collaboration challenges in research on AI and digital health.

Pain Points they Address: I-DAIR has an explicit focus on using AI in developing more effective disease response. As such, they seek to tackle the pain point of a lack of innovation and dearth of new technologies deployed to this cause.

CollectI-DAIR alludes to the use of AI for the discovery of unusual data sources and automated data collection for pandemic preparedness and response
CurateI-DAIR envisions providing a neutral infrastructure for data and analytics
StaffPart of I-DAIR’s work with the GPDA involves conceptualizing the architecture for a global pandemic scheme and training a “transdisciplinary, digitally-savvy international community of public health officers.”
ToolsI-DAIR will provide a collaborative platform for R&D of digital tools, models and analyses as global public goods. As a start, I-DAIR will develop an investment case as well as a minimum viable product (MVP) for such a platform.
SolutionsI-DAIR will develop the neutral infrastructure and a repository of modular solutions that can be deployed across different phases of the pandemic, particularly in LMIC settings
End UseI-DAIR will work closely with decision makers and the communities they serve to ensure that the tools are accessible across various stakeholders and data-driven pandemic responses can be implemented at local, regional and global levels.

Phases of disease preparedness they specialize in: Any

Our Tag: Tool Provider

Comparative Advantage: I-DAIR is one of the few groups explicitly devoted to emphasizing the use of machine learning and AI for global disease response, all across the data usage pipeline.

The Royal Society of London – S7 Science Research Agenda


Founding Date: 1660s, report for this work published March 2021

Region Served: Global

Diseases Addressed: All

Overview: As the lead of the S7 – the Science Academies of the G7 – for 2021, the Royal Society’s led the development of the S7 statement on data for international health emergencies, which the Global Pandemic Data Alliance is focused on delivering. The Royal Society also convenes RAMP – ‘Rapid Assistance in Modelling the Pandemic’, which uses modelling expertise from across sectors to provide insight on key questions arising from the pandemic. It also convened DELVE – ‘Data Evaluation and Learning for Viral Epidemics’ in the early stage of the pandemic. The Royal Society is involved in the Global Pandemic Data Alliance as S7 leads, and works closely with other science Academies including the German Academy of Sciences Leopoldina who will be S7 leads in 2022. The Society has a long-term focus on enabling well-governed access to data including for emergencies such as pandemics and environmental crises, and is carrying out a major public dialogue on attitudes to data use in emergencies.

Pain Points they Address: While there has been much discussion about the gaps in our health systems due to COVID-19, there have not many deep needs assessments carried out to ascertain what needs to exist instead. This effort by the Royal Society will spell out an implementation plan for meeting the pandemic research needs of the scientific community.

CurateEven at this early stage, the report is calling on G7 countries to “​​agree on principles and systems, technology, and infrastructure to facilitate safe and equitable sharing of data in global health emergencies”
End UseThe Royal Society is unique in that it is building a vision and implementation plan for better global coordination around pandemic research. As such, it is difficult to know yet which phases of the pipeline the system will emphasize.

Phases of disease preparedness they specialize in: Any

Our Tag: Data Platform, though the full range of recommendations this team proposes may fall into other categories as well.

Comparative Advantage: The Royal Society has a legacy of expert scientific research. They are also heading up the S7 and thus have strong influence in the scientific agenda the G7 pursues.

Open Data Institute


Founding Date: 2012

Region Served: UK, though standards recommendations apply to international bodies

Diseases Addressed: Any. ODI’s work is primarily around data governance and standards, independent of the disease in question.

Overview: The Open Data Institute (ODI) works with companies and governments to build an open, trustworthy data ecosystem. We work with a range of companies, organisations, governments, public bodies and civil society to create a world where data works for everyone. Our work includes a range of activities including conducting applied research, delivering consultancy services and training, running innovation and sector change programmes, and providing free tools and courses.

Pain Points they Address: In sharing data, open or otherwise, companies and institutions may struggle to find best practices for standardizing that data, governing it, and delivering it. The ODI provides those standards through guidelines on infrastructure and policy. They also tackle the pain point of lack of technical capacity that some pandemic preparedness organizations may face, providing consulting and training to make up for that.

CollectThe ODI provides guidelines on appropriate and ethical data collection under different circumstances
CurateThe ODI delivers standards, research on sharing and governance policy, and best practice for infrastructure
StaffThere are some training programs as well as on-demand consulting services
SolutionsThere are a few applications of data shared by the ODI, though mostly as the result of challenges they run occasionally
End UseThere is a strong consultative component between the ODI and partners in helping decisionmakers understand and effectively use the results of open data initiatives.

Phases of disease preparedness they specialize in: Any. They are agnostic to application of the data

Our Tag: Data Platform, though only because their key focus is on data curation as one of their key strengths. However, there is no specific ODI platform for pandemic preparedness.

Comparative Advantage: The ODI is expert in handling all things data governance. As such, they are in a unique position to advise on infrastructure and sharing standards for pandemic preparedness data from a multitude of sources, be they government, nonprofit, or corporate.

Trinity Challenge


Founding Date: Seems like 2021

Region Served: Global

Diseases Addressed: All

Overview: The Trinity Challenge is a coalition of partners united by the common aim of developing insights and actions to contribute to a world better protected from global health emergencies. We are working to uncover solutions that use data and analytics in novel ways, to better inform decision making and guide responses to future outbreaks. The 2021 Challenge set out three areas of interest:

  • Identify: to determine the emergence and re-emergence of diseases and the risks they pose to communities.
  • Respond: to decrease transmission and spread by identifying measures that are effective, equitable, and affordable.
  • Recover: to improve the resilience of health and economic systems, and address the disproportionate impacts of outbreaks and pandemics, particularly on vulnerable groups.

Pain Points they Address: The Trinity Challenge primarily addresses the analytics innovation gap in pandemic preparedness. Today, many researchers are equipped only with the statistical tools that they’ve used for the past few decades. Increasingly machine learning and AI could help augment their abilities by supporting common tasks, like modeling disease interactions, as well as new opportunities, like scanning social media for mentions of symptoms. The Trinity Challenge bridges that gap by funding innovation in using machine learning for pandemic preparedness.

ToolsIf any tools are developed by Challenge winners, it is likely Trinity Challenge will try to help support them
SolutionsThe outputs from the Trinity Challenge winners will be provided to the world to aid in pandemic preparedness.
End UseN/A

Phases of disease preparedness they specialize in: Any

Our Tag: Tool Providers

Comparative Advantage: The Trinity Challenge is a consortium of many different types of organizations, ranging from universities to nonprofits to big tech companies like Microsoft. Their varied experiences and funding models could support interesting and innovative work.

Conclusion and Future Work

We hope that this report serves as a useful first step in understanding the landscape of initiatives using data for pandemic preparedness. Our research and interviews highlighted the need to ensure initiatives do not duplicate efforts, either because new programs starting today do not take the time to learn the lessons of those doing similar work during prior pandemics, or due to misaligned funding incentives. It also showed an abundance of work on data harmonization and sharing, but a dearth of programs for sharing and maintaining software tools. Lastly, there is a need for more equity and involvement across the Global South. We hope that our categorization can also serve useful for those trying to navigate the plethora of activities and outcomes related to using data for pandemic preparedness.

In 2022, we will continue adding data to our dataset of initiatives through select interviews and invitations to the community to contribute information about their own efforts. All data and reporting will be open source and public-facing, and we intend for this landscape to be an ever-growing and ever-evolving resource for the pandemic preparedness community.`


We would like to thank Philip Abdelmalik, Carly Gaspa, Dylan George, Rebecca Kahn, Esther Krofa, Marc Lipsitch, Laura Merson, Dusan Milovanovic, Neil Postlethwaite, Vivian Singletary, and Anne Wozencraft for generously giving their time to speak with us and for their help in preparing this report. We also extend special thanks to the ICODA team for their diligent work landscaping and categorizing data initiatives, which provided an extensive foundation to add to.

[2] Data Platforms do not necessarily house the data itself, they may link to external data or direct others to owners of the data. The WHO’s Hub for Pandemic Intelligence, for example, explicitly states that they do not intend to host or centralize data, but instead connect people seamlessly to the data they need.

[3]  First coined by the UK Office of National Statistics (ONS)

Appendix: Initiatives researched

Appendix: COVID-19 Initiatives that ICODA Landscaped And Were Tagged In Our System

2019 Novel Coronavirus Resource (2019nCoVR)
4CE Consortium for Clinical Characterization of COVID-19 by EHR
ACT (Access to COVID-19 Tools) Accelerator
ACTIV Accelerating COVID-19 Therapeutic Interventions and Vaccines
Africa CDC PGII (Pathogen Genomics Intelligence Institute)
Africa UN Knowledge Hub for COVID-19
ALERRT African Coalition for Epidemic Research, Response and Training COVID-19 response
AMRI COVID-19 Fast Response Service
ANZCTR Australian New Zealand Clinical Trials Registry
Apple COVID-19
ASH RC COVID-19 Registry for Hematology
AWS data lake for the analysis of COVID-19 data
BBMRI-ERIC COVID-19 resources
BioExcel COVID-19
Broad Terra cloud workspace
Butterfly Network Data Library
C-19 Global South Observatory
C-TAP COVID-19 Technology Access Pool COVID-19 Data Lake
CAIAC Collective and Augmented Intelligence against COVID-19
CanCOGeN Canadian COVID Genomics Network
CAS COVID-19 Resources
CDC COVID-19 cases and testing in the U.S.
CDISC Interim User Guide
ChiCTR Chinese Clinical Trial Registry
Cochrane CENTRAL Central Register of Controlled Trials
Cochrane COVID-19 Study Register
CODATA Decadal Programme
COG-UK COVID-19 Genomics UK Consortium
COMET Initiative
CommonHealth COVID Data Map
CONCVACT Africa CDC Consortium for COVID-19 Vaccine Clinical Trial
CORD-19 COVID-19 Open Research Dataset
Coronavirus canSAR
Coronavirus Tech Handbook
Coronavirus Tweets
Cough against COVID
CoV-AbDab: The Coronavirus Antibody Database
COVID Alliance
COVID CIRCLE (COVID-19 Research Coordination and Learning)
COVID Human Genetic Effort
COVID Moonshot
COVID Near You
COVID OpenData Portal
COVID Tracking Project
COVID-19 & Cancer Consortium
COVID-19 Cell Atlas
COVID-19 Clinical Data Analytics Platform
COVID-19 Clinical Research Coalition
COVID-19 clinical trials registry
COVID-19 Colombia Project Novel Coronavirus Data Repository
COVID-19 CP Collaboration Platform
COVID-19 CT segmentation dataset
COVID-19 Data Exchange
COVID-19 Data Forum
COVID-19 Data in Harvard Dataverse
COVID-19 Data Portal
COVID-19 Data Protal Sweden
COVID-19 Data Repository for Africa
COVID-19 Data Science Consortium
COVID-19 dataset clearinghouse
COVID-19 Dermatology Registry
COVID-19 Disease Map
COVID-19 Evidence Accelerator
COVID-19 Galaxy Project
COVID-19 Genomics Research Network
COVID-19 Global  Rheumatology Alliance
COVID-19 Healthcare Coalition
COVID-19 Healthcare Coalition Resource Library
COVID-19 Host Genetics Initiative
COVID-19 HPC Consortium
COVID-19 in liver disease reporting registry
COVID-19 Interoperability Alliance
COVID-19 Mass Spectrometry Coalition
COVID-19 Mobility Data Network
COVID-19 Multi Model Comparison Collaboration (CMCC)
COVID-19 National Core Studies
COVID-19 OSS Help (Open Source HelpDesk)
COVID-19 Pop-Up Ecosystems
COVID-19 preclinical drug development database
COVID-19 Quebec Biobank
COVID-19 Research Database
COVID-19 Research Project Tracker
COVID-19 Statistics and Research – Our World in Data
COVID-19 Symptom Tracker
COVID-19: Casistica Radiologica Italiana
COVID-CT dataset
COVID-DPR Digital Pathology Repository
CRIS Clinical Research Information Service
Croatian Open Data Portal on COVID-19
Crowdfight COVID-19
CSIS Southeast Asia COVID-19 Tracker
CTD Comparative Toxicogenomics Database
CTRI Clinical Trials Registry India
Cure HIV-COVID Reporting Database
Data against COVID
Data Science Africa COVID-19 Response
DATA4COVID (Data Collaboratives in response to COVID-19)
DELVE Global COVID-19 Dataset
DETECT Health Study
DHIS2 COVID-19 Package
DIAMOND coronavirus dataset
Discovery VIRUS COVID-19 registry
DNAStack Beacon
DOLT Coronavirus Dataset
DS4C Data Science for COVID-19 in South Korea
ECDC National information resources on COVID-19
ECRIN Clinical Research Metadata Repository
EDCTP COVID-19 Emergency Funding
ELIXIR COVID-19 data resources
ELIXIR strategic resources
Emergent Alliance
En Red contra COVID-19
EndPandemic National Data Consortium
ERF-AISBL Research Infrastructures and COVID-19 Research
EU Clinical Trials Register
EU-OPENSCREEN ERIC COVID-19 data resources
EU-OPENSCREEN ERIC strategic resources
Euro-BioImaging COVID-19 data resources
European Centre for Disease Control COVID-19 data
FAIRsharing COVID-19 Collection
Figshare COVID-19 Open Research Data
Forum of International respiratory societies
GECO Global Effort on COVID-19 Health Research
GenOMICC study on COVID-19 patients
Ghana COVID-19 Monitoring Dashboard
GISAID EpiCov Database
Global Hidradenitis Suppurativa COVID-19 Registry
Google COVID-19 BigQuery Public Datasets program
Google COVID-19 Community Mobility reports
Harmony Alliance COVID-19 Data Platform
HDX COVID-19 datasets
Health Data Research Innovation Gateway
HERO Registry
IBM Dataset
IBM Functional Genomics Platform
ICPSR COVID-19 Data Repository
IDDO/ISARIC Clinical Data Sharing Platform
Indonesia COVID-19 Data Tracker
Instruct-ERIC strategic resources
Instruct-ITALIA strategic resources
IQVIA CARE (COVID-19 Active Research Experience) Project registry
IQVIA COVID-19 Trial Matching Tool
ISARIC COVID-19 Clinical Research Resources and Clinical Characterisation Protocol (CCP)
ISRCTN registry
J-IDEA (Jameel Institute for Disease and Emergency Analytics) COVID-19 resources
JEDI Grand Challenge Billion Molecules against COVID-19
JHU CSSE COVID-19 data repository
Johns Hopkins Coronavirus Resource Center
Kinsa HealthWeather
LENS COVID-19 datasets
LEOSS Lean European Open Survey for SARS-CoV-2 Infected Patients
Mbale COVID-19 Open Cohort Study
MSIF Global Data Sharing Initiative
N3C National COVID Cohort Collaborative
National observatinoal COVID-19 data portal
NCBI Virus Sars-CoV-2 Data Dashboard
nfdi4health Task Force COVID-19
NHS COVID-19 Data Store
NIPH Clinical Trials Search
NWL COVID-19 Data Repository
NY Times COVID-19 data
ODK Open Data Kit
OECD country policy tracker
ONE Africa COVID-19 Tracker
Open COVID-19 Data Working Group dataset
Open COVID-19 Initiative
OpenAIRE COVID-19 Gateway
OpenAIRE Zenodo COVID-19 community
PACTR Pan African Clinical Trials Registry
PAN-COVID (Pregnancy and Neonatal)
PANDORA Pan-African Network for Rapid Research, Response and Preparedness for Infectious Diseases Epidemics
PATH responds to COVID-19
PERC (Partnership for Evidence-based Response to COVID-19)
PHOSP-COVID study (Post Hospitalisation)
PIDTRAN USA Pediatric COVID-19 Registry
PoliMap COVID-19
PRACE versus COVID-19
PRINCIPLE trial (Platform Randomised trial of interventions against COVID-19 in older people)
PRIORITY Pregnancy Coronavirus Outcomes Registry
Public Coronavirus Twitter Dataset
Public Health Alliance for Genomic Epidemiology
R-TRC Support for COVID-19 Studies
RCSB PDB COVID-19/SARS-CoV-2 Resources
RDA COVID-19 Working Group
ReBEC Brazilian Registry of Clinical Trials
RECON R Epidemics Consortium (and COVID-19 Challenge)
RECOVER Rapid European COVID-19 Emergency Response research
RECOVERY trial (Randomised Evaluation of COVID-19 Therapy)
REPEC Peruvian Clinical Trial Registry
RESPIRE / BREATH HUB (Global Health Research Unit on Respiratory Health) COVID-19 projects
Risorse dati su COVID-19
Roche Data Science Coalition COVID-19 Database
Roche Data Science Coalition’s UNCOVER
RPCEC Cuban Public Registry of Clinical Trials
SCOR Secure Collective COVID-19 Research – MedCo
SECURE-AD registry
SECURE-Cirrhosis registry
SECURE-IBD database
SECURE-SCD registry
Sierra Leone COVID-19 Live Update
SORMAS Surveillance Outbreak Response Management and Analysis System
SPHERES SARS-CoV-2 Sequencing for Public Health Emergency Response, Epidemiology and Surveillance
Stanford University  COVDB Coronavirus Antiviral Research Database
SURF Stanford Medicine COVID-19 Tools
Systematic review of COVID-19 clinical trials
T1D Exchange: Surveillance of COVID-19 in Patients with Type 1 Diabetes
Targeting COVID-19 Portal
TIF Reporting of COVID-19 in haemoglobin disorders
Tracking COVID-19 in Latin America
TransCelerate BioPharma
TRANSVAC services to COVID-19 vaccine developers
Trial-Forge COVID-19 Resources
Trinity Challenge
TU Wien OSSDIP data infrastructure and Processes
UK Biobank COVID-19 Hub
University of Montreal COVID-19 Image Data Collection
VIVLI COVID-19 Data Portal
VODAN Africa
VODAN Virus Outbreak Data Network
WHO COVID-19 situation reports  and dashboard
WHO Global COVID-19 Clinical Data Platform
WHO ICTRP International Clinical Trials Registry Platform
Wikidata COVID_19

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