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Moderna uses the right dose of data to boost discovery

About Moderna

Cambridge, MA-based Moderna believes that messenger RNA, or mRNA, is the “software of life.” Since being founded in 2010, the company has worked to pioneer a new class of medicines based on their extensive research into mRNA.

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Moderna leverages a modern, multicloud data stack to gain a more complete view of clinical trials, increase scientific efficiency and collaboration, and optimize shipments to reduce costs and meet budget goals., google cloud results.

• Leverages a multicloud data strategy to use and integrate the best tools for the job at hand
• Integrates internal and external datasets for a more complete view of clinical trials
• Increases diversity in clinical trials to improve representation
• Reduces the time scientists spend on manual data manipulation to increase research time and collaboration
• Optimizes shipments to reduce costs and meet budget goals

In a highly regulated industry, Moderna knows the critical importance data plays in key processes for the creation, approval, and distribution of vaccines and therapeutics. When the COVID-19 pandemic started in 2020, the pressure to find a fast, effective, and safe vaccine became a global priority.

As the need for accurate, auditable, and actionable insights has become greater, Moderna has leveraged its modern, multicloud data stack to prioritize the job at hand.

Finding the best solution for the job

As a research-driven organization, data has been critical to Moderna’s strategy since the beginning. Central to the data-driven strategy is Moderna’s commitment to finding and using the best tools for the job—then integrating those tools to make sure the company is using the best possible solution.

“Moderna’s digital philosophy is around best-of-breed integratable software. If there's something that solves a problem well, that’s what we will use, if not we will develop it ourselves. We believe in finding the best tool for the job, integrating it, and then making it whole. When we think about infrastructure, if something works, we will integrate and use it,” explains Dave Johnson, VP of Informatics, Data Science, and AI at Moderna.

“Moderna’s digital philosophy is around best-of-breed integratable software.”

For years, Moderna has centralized its data in Amazon Redshift and used that to feed data into other tools. While this met their cost and compatibility requirements, teams across Moderna still needed an easier and faster way to access actionable insights. Previously, the majority of employees relied primarily on Excel for data analysis, with some researchers utilizing Spotfire Desktop.

While these tools provided some access to data, they still required significant manual work and set a high barrier to entry. This manual process led to data silos across the organization, limited opportunity to further explore data, and created issues of consistency resulting from various and conflicting versions of the same report.

To help employees access and validate data across the organization, Moderna set out to find the best fit for the job. The top criteria were to improve self-service and exploration, maintain data quality and consistency, and ensure the new tool would be cost-effective and integrate with the tools Moderna already had in place. In 2016, Moderna selected Looker ’s data application platform to increase organization-wide access to trusted, secure metrics.

With strategic guidance from Looker's Professional Services, Moderna formulated a small team of two people to build a foundation for self-service analytics. Following best practices for scalable and flexible deployments, Moderna was able to access trusted insights from Looker within a few weeks of implementation.

Today, Moderna uses Etleap for real-time ETL into Amazon Redshift. They use Looker for building models, transformation on the fly, data exploration, and self-service. And they use Google sentiment processes to pull insights from their ticketing system, ServiceNow.

“Looker fits well with our multicloud philosophy because we can choose our preferred database and leverage integrations to make our data accessible and actionable. Overall, Google is making a lot of progress in multicloud, which allows you to not have to think about the vendor and just adopt what you need to do the job well,” explains Johnson.

Moderna’s modern multicloud data strategy allows the company to centralize, access, and take action on trusted data across the organization.

“Looker fits well with our multicloud philosophy because we can choose our preferred database and leverage integrations to make our data accessible and actionable. Overall, Google is making a lot of progress in multicloud, which allows you to not have to think about the vendor and just adopt what you need to do the job well.”

Increasing diversity in clinical trials to increase efficacy

When running a clinical trial, there are numerous systems involved to collect data and manage the various clinical processes. There is also additional external information, from epidemiological data that tracks the spread of a pandemic to census and demographic data that provides a view of the populations at risk.

Historically, this information has remained siloed across different sources with clinicians manually attempting to bring the data together in Excel. In addition to being time-consuming and leaving room for human error, this process makes it impossible to create a holistic and up-to-date view of all factors that might impact the operations of the study. As Johnson adds, “It’s nearly impossible to bring everything together and see how factors can relate to or impact one another.”

This limited view into these various factors makes it difficult for those managing the clinical trials to make informed decisions about enrollment and trial execution.

“By pulling all of these disparate data sources into a single place with compelling reports and visualizations in Looker, we’ve enabled our clinical operations team to make real-time decisions about their trials to ensure the highest quality data,” explains Johnson.

Using Looker, Moderna is able to create and take action with a more holistic view of their clinical trials than has previously been possible. Today, the company can analyze within and across internal datasets (such as clinical operations, race, gender, age, risk group) and external datasets ( such as epidemiology, census, etc). This visibility provides a more complete view of the study, while making it easier to identify potential trends and outliers. Because of this, Moderna is also able to track and increase the diversity in their clinical trials in a way that hasn’t previously been possible.

“Our internal data science team developed custom metrics around racial diversity and created performance metrics to understand where we should be relative to each region. We’ve built dashboards to help us look across regions and then filter down to each site and see how they’re performing relative to their peers and where they should be. Now that we can bring in more external data sources alongside our own data, we’re making a very conscious decision to increase diversity in our clinical trials, pursuing more accurate representation. Our teams use the trial enrollment data in Looker many times a day, and they are constantly reviewing the dashboards to see where they are and where they should be. They share them internally, with leadership, and with recruitment to make sure we are producing diversity. This is dramatically changing how we work,” shares Johnson.

Moderna is analyzing population and sample data to inform decision-making across all of its trials, including the search for a COVID-19 vaccine. With Looker, Moderna is able to track, improve, and publicly share the diverse representation within its trial. As Johnson explains, “COVID is disproportionately impacting minorities, and our goal is to ensure our trial represents the population.”

Accessing a complete view of research to increase exploration and collaboration

Formulating and testing hypotheses is critical to the scientific process, and for this to work scientists need access to data they can trust. Typically, scientists need to pull data from several sources, interrogate it, identify new questions, collect and add new data, and repeat. It’s a very manual, but important, process.

“Previously, our scientists had to build their own mini data warehouse in Excel and then make graphs. If they had the technical skills, some of them would use Spotfire for additional analysis since it’s a powerful tool for the scientists who know how to use it. Then, they’d have to share it and there would be issues with copy, consistency, and permissioning,” adds Johnson.

Since implementing Looker, this process has been greatly streamlined to allow scientists to use time they previously spent on manual reporting to focus on research and discovery. This new process helps to eliminate the potential for error from manual reporting and extracts while also increasing the ease with which research data can be shared, tested, and supported.

As Johnson explains, “Now that research data is in the data warehouse, scientists can easily build reports and it’s all safeguarded and integrated. Looker improved collaboration and speed of decision-making because scientists don't need to fish around. It’s improved quality because we aren’t questioning the source of the data—whether it’s been miscopied or changed, or what version.”

By centralizing research data access with Looker, Moderna has been able to ensure security and accuracy, while improving visibility and exploration.

“Looker improved collaboration and speed of decision-making because scientists don't need to fish around. It’s improved quality because we aren’t questioning the source of the data—whether it’s been miscopied or changed, or what version.”

Streamlining shipping and logistic to reduce costs

The research process relies heavily on being able to quickly and securely ship materials across Moderna’s sites and external partners. These requirements keep the Moderna logistics team, which handles all shipping requests, in high demand. Previously, they were greatly exceeding their budgets due to the high volume of requests they were asked to fulfill. They knew they needed to start saving money, but they didn’t have the visibility into expenses that they needed in order to identify opportunities to cut costs. While they had built an internal tool to track and manage shipping, it didn’t provide a full picture of the transactions or allow them to compare spend to budgets in real time.

“In order to identify cost savings, we first needed to be able to see all shipments and then drill into which departments and types of requests were costing the most,” explains Johnson. “Now, we have all of this in Looker so the team can find opportunities to combine shipments or change the shipment type when appropriate.”

For a team that manages 60,000+ shipments per year, real-time access to shipping dashboards has allowed the logistics team to greatly improve their processes while also helping them to accurately track and meet their budget targets.

Harnessing sentiment analysis to improve the user experience

User feedback can provide invaluable insights. However, it’s often unstructured and siloed in different applications, which make it difficult to analyze and develop a comprehensive view.

Previously, Moderna wanted to harness user survey insights from its internal IT support ticketing system, ServiceNow, and other applications used for communication. As Johnson shares, “We wanted to answer basic questions around whether people were happy and to understand sentiment over time so we could identify opportunities, but it just wasn’t feasible.”

Now, teams use Google’s Natural Language Processing (NLP) API to analyze unstructured support ticket data from various sources to analyze sentiment over time. The Moderna team can also do deeper exploration with entity recognition to identify device type and spot trends that help improve the internal user experience. For example, they can quickly identify and take action if there is an issue impacting certain software applications or hardware.

Today, when a new ticket comes in, the IT support team can search by key components (for example, device types that aren't syncing properly) and then look for correlations across issues over time. This visibility helps the support team more accurately troubleshoot issues and proactively address factors that might impact the user experience. It also allows them to track and make sure they’re continually improving the user experience.

“Now that we can understand sentiment over time, the management team can see if we’re trending in the right direction. This information is relayed back to the help desk team so they can set internal targets regarding the speed and volume of tickets they complete. They can also explore trends by technician and work with them to provide guidance and improve service,” adds Johnson.

Looking deeper into the data to discover more

As Moderna continues to drive discovery in medical research, the teams are continually finding new ways to leverage and integrate the best technologies to help meet their primary goal—discovering ways to use mRNA to help people.

“We’ve found that teams become very interested in Looker when their data is added. Then we conduct basic training to help them become data consumers, but from there they start to ask more questions and we train them to explore further and build. Looker has a depth to it—it’s not just a visualization that you look at. People can go deeper as they learn more,” concludes Johnson.

Moderna’s path to vaccine innovation: A talk with CEO Stéphane Bancel

This interview is part of COVID-19 vaccines: The road to recovery and beyond , a series that includes a broad array of voices leading the historic global effort to develop, distribute, and provide equitable access to COVID-19 vaccines, including the Africa CDC ; CEPI ; Gavi, the Vaccine Alliance ; Moderna ; and Pfizer .

The COVID-19 pandemic has resulted in devastating public-health and economic outcomes. It also spurred one of the most promising scientific feats in the last century—the development of several highly effective vaccines authorized for emergency use by the US Food and Drug Administration (FDA) in less than one year.

About this series: COVID-19 vaccines: The road to recovery and beyond

This interview is part of the series COVID-19 vaccines: The road to recovery and beyond . By drawing on the experience of private- and public-sector experts across a wide range of perspectives—from vaccine innovation to manufacturing, distribution, and accessibility—we aim to help leaders navigate recovery efforts for the COVID-19 pandemic and share some of the lessons they’ve learned to combat the pandemic and support the future of vaccines. Articles in the series include interviews with the heads of top global health organizations, government agencies, and vaccine manufacturers. For example:

• Africa CDC: Africa’s plan for a continent-wide pandemic recovery
• Accelerating pandemic response efforts: An interview with CEPI’s Richard Hatchett
• Gavi, the Vaccine Alliance: Finding solidarity in an effort to vaccinate the world
• Moderna’s path to vaccine innovation: A talk with CEO Stéphane Bancel
• Pfizer’s mix of science and grit alters the course of its COVID-19 response

Along with public-health authorities, we recognize that vaccination is critical to combating infectious diseases, and we are committed to the urgent effort of bringing an end to the COVID-19 pandemic and transforming the future of immunization.

This achievement required unprecedented mobilization and the cooperation of a broad set of global stakeholders in both the public and private sectors, including governments, vaccine developers, and public-health organizations. Building on deep scientific knowledge gained from years of experience working with viruses, such as MERS, SARS, influenza, HIV, and Hepatitis C, the medical industry’s private sector has made immense progress in advancing potential treatments and vaccines to help address COVID-19. 1 “The bold mission of America’s biopharmaceutical companies is to bring an end to the burdens of disease, in all its forms,” Our commitment, America’s Biopharmaceutical Companies, 2018, innovation.org. Looking ahead, the breakthroughs stemming from COVID-19 hold vast potential for the broader vaccines industry with the emergence of ground-breaking innovation, more public engagement, and increased focus from health officials.

Making inroads in the fight against COVID-19 is Massachusetts-based biotech company Moderna, which has been looking to innovate approaches to vaccine development since the company’s start in 2010. Before COVID-19, Moderna already had a new class of vaccines in the works that use messenger ribonucleic acid, or mRNA, 2 Messenger ribonucleic acid, or mRNA, is a single-stranded molecule that carries genetic code from DNA in a cell’s nucleus to ribosomes, the cell’s protein-making machinery. “The science and fundamentals of mRNA technology,” Moderna, 2021, modernatx.com. which instructs a patient’s own cells to produce the proteins needed to activate the immune system to prevent illness. “The potential implications of using mRNA as a drug are significant and far-reaching and could meaningfully improve how medicines are discovered, developed, and manufactured,” says Moderna’s CEO Stéphane Bancel.

Bancel spoke with McKinsey’s Olivier Leclerc about why he believes his company was well positioned to respond to the COVID-19 crisis, how scientific breakthroughs with mRNA led to an effective COVID-19 vaccine, and what it takes to lead his company for long-term impact during the crisis. Below is an edited excerpt of Bancel’s remarks.

Responding to COVID-19

McKinsey: Vaccine development is a long, complex process. How did Moderna step up when the pandemic was declared?

Stéphane Bancel: By March 2020, the World Health Organization [WHO] declared COVID-19 a pandemic, and we were racing against the virus every day while we still had almost 20 non-COVID-19 programs that needed to keep moving. We raised the cadence of our executive-committee meetings from once a month to once or twice a week to have a clock speed that was adapted to the situation. We also used a decentralized model, which gave the distinct teams the independence to move quickly. The pace was unprecedented, fueled by the need to respond to a pandemic situation. To deliver on our goal of 100 million doses of COVID-19 vaccines within 12 months 3 Reuters staff, “Moderna says shipped 100 million COVID-19 vaccine doses to United States,” March 29, 2021, reuters.com. and a billion doses by the end of 2021, the team worked seven days a week and, at times, pulled all-nighters. If it weren’t for the extraordinary people I work with—who are selfless, mission-driven, and committed to building the best version of Moderna possible—we would not be where we are today.

McKinsey: Considering the intense pressure to develop an effective vaccine, how was interaction with the US government managed?

Stéphane Bancel: It came down to the collaboration between the pharmaceutical industry and the US federal government. The US government picked three different technologies to invest in for a diversified risk profile and then chose two pharmaceutical companies per technology. In the end, they were betting on six different companies. It was a brilliant move. The conditions of the contract included a base business of 100 million doses, with options to increase depending on the clinical data and the efficacy at the time of launch. This allowed us to take on a lot of business risk at a time when every single day mattered.

The other key part of this equation is that the FDA worked relentlessly to authorize the Moderna COVID-19 vaccine, and others, with an emergency use authorization [EUA]. Usually, you submit a question to the FDA, and they have a defined timeline for responding and engaging with clinical-trial sponsors. But they adapted to the crisis situation. During the pandemic, we could reach out to them any time—including weekends.

McKinsey: What were some difficult decisions you faced while developing the COVID-19 vaccine?

Stéphane Bancel: One tough spot we were in was deciding whether to slow down our Phase 3 clinical study. The study began on July 27, 2020, and the first few weeks were fantastic. However, we weren’t enrolling enough people from diverse backgrounds into the study, especially those disproportionally affected by COVID-19, such as African Americans. We felt we were failing society if we didn’t include communities most impacted by the virus. After many discussions, we decided to slow down the study, which made a big impact on our timeline. But at the end of the day, when you step back and look at the big picture, you can see that the tough decisions are well worth it.

We are now one of the first biotech companies to publish the demographic data of our clinical trials. In our Phase 3 study, we had 9.7 percent African American or Black participants, 4.7 percent Asian, 0.8 percent American Indian or Alaska Native, and 20.0 percent Hispanic participants, which reflects a greater diversity among participants than many other previous drug trials. 4 Meera Jagannathan, “Vaccine trials have underrepresented people of color for the past decade—and many failed to even report demographic data,” MarketWatch, February 19, 2021, marketwatch.com.

Delivering innovative solutions

McKinsey: What is mRNA technology? And why is it so effective against COVID-19?

Stéphane Bancel: Simply put, messenger RNA [mRNA] vaccines are a new type of medicine that trigger an immune response to protect against infectious diseases. Many other vaccines put a weakened or inactivated virus into the body to trigger that response. Instead, mRNA vaccines teach our bodies how to make a protein, or a piece of a protein, to get the same result.

Moderna’s COVID-19 mRNA vaccine tells your cells to make a harmless viral protein called a spike. This stimulates your immune system to make antibodies and immune cells that counterattack the spike when they come across it. When scientists published the genetic code for the COVID-19 virus in January of 2020, we realized that the spike protein of the Middle East Respiratory Syndrome [MERS], and SARS-CoV-2 5 The scientific name of the new strain of coronavirus is SARS-CoV-2. In people, the disease caused by the virus is called coronavirus disease 2019, also known as COVID-19. Because we are addressing the virus itself in the context of animal health, we refer to it as SARS-CoV-2 (“Basics of COVID-19,” Centers for Disease Control and Prevention, last updated May 24, 2021, cdc.gov). were very similar. In our previous work on the MERS virus with Dr. Anthony Fauci’s team at the National Institute of Allergy and Infectious Diseases [NIAID], we determined that the best vaccine using Moderna immunotechnology was a full-length spike protein. Based on that work, and all the work we had done on vaccines before, we were hopeful that we were off to a great start.

McKinsey: How can Moderna’s mRNA platform respond to new variants of coronavirus and their potential to evade the immune system?

Stéphane Bancel: Some mutations of the COVID-19 virus will likely have no impact on our vaccine’s efficacy, while others might. With several mutations, there is more and more “drift” from the original SARS-CoV-2 that was sequenced. Our mRNA platform lets us create new versions of the vaccine to attach to that variant in a matter of weeks. In January 2020, it took us just 42 days to go from a sequence of the SARS-CoV-2 virus to shipping the first product for human clinical trial to the federal government. The mRNA platform also allows for multiple variant sequences to be included in one vial, which lets us respond to new mutations faster than ever.

Our team, like most scientists around the world, has been following new variants, and we continue to have clinical trials under way. One of the benefits of mRNA is the flexibility and speed to development. We have been closely monitoring how our authorized COVID-19 vaccine protects against emerging variants, and in parallel, we are advancing our booster strategy.

McKinsey: Now that mRNA technology has been proven to work for COVID-19, what role do you expect it to play in the future of medicine and healthcare?

Stéphane Bancel: At Moderna, our mission is to deliver on the promise of mRNA science to create a new generation of transformative medicines for patients. Since mRNA is an information-based platform, it works similar to a computer’s operating system, letting researchers insert new genetic code from a virus—like adding an app—to create a new vaccine quickly. When COVID-19 struck, we already had nine vaccines in clinical trials using mRNA technology. The COVID-19 vaccine was our tenth. Because we had invested in building our mRNA platform, it was basically a copy and paste—inserting the new genetic code into our preexisting platform. It’s this type of technology that helped Moderna develop a COVID-19 vaccine in only 11 months. 6 A Phase 3 randomized, placebo-controlled, observer-blind clinical trial to evaluate the efficacy, safety, and immunogenicity of the Moderna COVID-19 Vaccine in participants 18 years of age and older is ongoing in the United States (NCT04470427). Randomization was stratified by age and health risk: 18 to under 65 years of age without comorbidities (not at risk for progression to severe COVID-19); 18 to under 65 years of age with comorbidities (at risk for progression to severe COVID-19); and 65 years of age and older with or without comorbidities. “Clinical trial data,” Moderna, 2021, modernatx.com.

Since mRNA is an information-based platform, it works similar to a computer’s operating system, letting researchers insert new genetic code from a virus—like adding an app—to create a new vaccine quickly.

We are now in a world where mRNA as a platform has been derisked for use in vaccines and can be authorized for emergency use. Moving forward, using genetic information—either human genes or the genome of a virus—gives us more opportunities for new mRNA programs. Moderna was always built to scale up, and we plan to conduct larger trials and accelerate our clinical programs. We are studying the use of our mRNA platform to develop medicines for infections of the lung and many other organ systems where we hope mRNA may help patients.

Looking ahead at the opportunities for faster product innovation and delivery—combined with a world where the general public, healthcare professionals, and governments are more conscious of the spread of viruses and willing to invest in treatment—I think there is an opportunity with mRNA to transform infectious diseases in the next five years.

Creating impact for the long haul

McKinsey: What has helped Moderna deliver impact—not just over the past year, but also over the past ten years?

Stéphane Bancel: I’m obsessed with digitalization. At past companies, I was extremely frustrated about the time we wasted as a business not serving the customer and not pushing the envelope because we didn’t have data or the data were incorrect. I remember a moment that shifted my thinking for the rest of my career. It was the year 2000, and I was fresh out of business school and working for a big pharma company. I spent a week chasing down a manufacturing lot. The internet was booming, yet I was still sending emails trying to locate a lot—information that should have been available to me from any device or computer system. Now at Moderna, we challenge ourselves daily to digitize the company better.

The process starts by being very thoughtful about technology, building our own apps, adding the right people, and then giving all employees access to that technology. The last step is very important. Everyone at the company has access to our systems from anywhere via their company mobile device—whether they are approving an invoice or designing a drug. This digital experience is completely ingrained in our DNA.

One of the biggest challenges over the next three years for both the HR team and the digital team is to incorporate more artificial intelligence as part of our process. With all of the structured data we’ve gathered—through preclinical trials, research, and experiments—we’ve been able to build better algorithms. These algorithms are enabling us to employ machine learning and make faster decisions. For instance, we can get predictions in the clinical space that humans wouldn’t be able to make in a reasonable amount of time.

Coronavirus vaccines progress: What’s next?

McKinsey: Managing for the long haul can be hard under normal circumstances. Did you have to adjust your leadership style in response to the crisis?

Stéphane Bancel: One of the hardest things to deal with in this type of crisis is being able to go the distance. Many of us end up working more hours under stress when we are in crisis mode, which tends to come at the expense of our health and well-being. It’s important to make the right lifestyle choices to avoid possible burnout. I’ve learned over the years that engaging in sports and getting good sleep is critical for me in managing my stress. Everyone needs to find what works best for them to maintain that type of pace, or else it’s just not possible to sustain.

I also learned that fighting COVID-19 has to be a team effort. When we’re juggling many decisions, it’s the team that has to keep all the pieces in the air at the same time. And subsets of the team need to be able to accomplish a task or get aligned even if I’m not in the room. To do so, the team needs to be informed and have enough pieces of the puzzle to be effective and avoid any disconnects. It also takes being even clearer than usual on our goals, articulating those goals clearly, and passing the ball when needed. Our whole team needs to be moving in the same direction.

This content is provided “as is” solely for informational purposes. It is not legal, health, or safety advice. Organizations should engage their own experts to ensure any adopted measures are compliant with applicable laws and standards in their jurisdictions. The opinions expressed by individuals or organizations are their own and do not reflect the views or opinions of McKinsey & Company. References to specific products or organizations do not constitute any endorsement or recommendation by McKinsey.

Stéphane Bancel is Moderna’s CEO and a biochemical engineer. Olivier Leclerc is a senior partner in McKinsey’s Los Angeles office.

The authors wish to thank Jennifer Heller, Adam Sabow, Jeff Smith, and Ramesh Srinivasan for their contributions to this article.

This article was edited by Astrid Sandoval, an executive editor in the London office.

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Accelerating the development of life-saving treatments

Moderna and OpenAI partner to accelerate the development of life-saving treatments.

Moderna partners with OpenAI to deploy ChatGPT Enterprise to thousands of employees across the company. Now every function is empowered with AI, creating novel use cases and GPTs that accelerate and expand the impact of every team.

Moderna has been at the intersection of science, technology, and health for more than 10 years. Moderna’s mission is to deliver the greatest possible impact to people through mRNA medicines—with the COVID-19 vaccine being their most well-known breakthrough.

The company has partnered with OpenAI since early 2023. Now, ChatGPT Enterprise is evolving how Moderna operates across each function.

Moderna is using its platform for developing mRNA medicines to bring up to 15 new products to market in the next 5 years—from a vaccine against RSV to individualized cancer treatments. In order to achieve its ambitions, Moderna has adopted a people-centric, technology-forward approach, constantly testing new technology and innovation that can increase human capacity and clinical performance.

Moderna brings AI to everyone

Moderna adopted generative AI the same way Moderna adopts other technology: with the mindset of using the power of digital to maximize its positive impact on patients. To allow AI to flourish, they knew they needed to start with the user and invest in laying a strong foundation for change.

Moderna’s objective was to achieve 100% adoption and proficiency of generative AI by all its people with access to digital solutions in six months. “We believe in collective intelligence when it comes to paradigm changes,” said Miller, “it’s everyone together, everyone with a voice and nobody left behind.” For this, Moderna assigned a team of dedicated experts to drive a bespoke transformation program. Their approach combined individual, collective and structural change management initiatives.

Individual change management initiatives included in-depth research and listening programs, as well as trainings hosted in person, online and with dedicated AI learning companions. “Using AI to teach AI was key to our success”, Miller points out. Collective change management initiatives included an AI prompt contest to identify the top 100 AI power users who were then structured as a cohort of internal Generative AI Champions. Moderna’s culture of learning led to local office hours in every business line and geography, and scaled through an internal forum on AI, which now has 2,000 active weekly participants. Lastly, structural change management initiatives included engaging Moderna’s CEO and executive committee members to foster AI culture through leadership meetings and town halls as well as incentive programs and sponsored events with internal and external experts.

This work led to an early win with the launch of an internal AI chatbot tool, mChat, at the beginning of 2023. Built on OpenAI’s API, mChat was a success, adopted by more than 80% of employees across the company, building a solid foundation for the adoption of ChatGPT Enterprise.

Building momentum with ChatGPT Enterprise

With the launch of ChatGPT Enterprise, Moderna had a decision to make: continue developing mChat as an all-purpose AI tool, or give employees access to ChatGPT Enterprise?“

As a science-based company, we research everything,” said Brice Challamel, Head of AI Products and Platforms at Moderna. Challamel’s team did extensive user testing comparing mChat, Copilot, and ChatGPT Enterprise. “We found out that the net promoter score of ChatGPT Enterprise was through the roof. This was by far the company-favorite solution, and the one we decided to double down on,” Challamel said.

Once employees had a way to create their own GPTs easily, the only limit was their imaginations. “We were never here to fill a bucket, but to light a fire,” Challamel said. “We saw the fire spread, with hundreds of use cases creating positive value across teams. We knew we were on to something revolutionary for the company.”

The company’s results are beyond expectations. Within two months of the ChatGPT Enterprise adoption:

Moderna had 750 GPTs across the company

40% of weekly active users created GPTs

Each user has 120 ChatGPT Enterprise conversations per week on average

Augmenting clinical trial development with GPTs

One of the many solutions Moderna has built and is continuing to develop and validate with ChatGPT Enterprise is a GPT pilot called Dose ID. Dose ID has the potential to review and analyze clinical data and is able to integrate and visualize large datasets. Dose ID is intended for use as a data-analysis assistant to the clinical study team, helping to augment the team’s clinical judgment and decision-making.

“Dose ID has provided supportive rationale for why we have picked a specific dose over other doses. It has allowed us to create customized data visualizations and it has also helped the study team members converse with the GPT to further analyze the data from multiple different angles,” said Meklit Workneh, Director of Clinical Development at Moderna.

Dose ID uses ChatGPT Enterprise’s advanced data analysis feature to automate the analysis and verify the optimal vaccine dose selected by the clinical study team, by applying standard dose selection criteria and principles. Dose ID provides a rationale, references its sources, and generates informative charts illustrating the key findings. This allows for a detailed review, led by humans and with AI input, prioritizing safety and optimizing the vaccine profile prior to further development in late-stage clinical trials.

“The Dose ID GPT has the potential to boost the amount of work we’re able to do as a team. We can comprehensively evaluate these extremely large amounts of data, and do it in a very efficient, safe, and accurate way, while helping to ensure security and privacy,” added Workneh.

Improving compliance and telling the company’s story

Moderna’s legal team boasts 100% adoption of ChatGPT Enterprise. “It lets us focus our time and attention on those matters that are truly driving an impact for patients,” said Shannon Klinger, Moderna’s Chief Legal Officer.

Now, with the Contract Companion GPT, any function can get a clear, readable summary of a contract. The Policy Bot GPT helps employees get quick answers about internal policies without needing to search through hundreds of documents.

Moderna’s corporate brand team has also found many ways to take advantage of ChatGPT Enterprise. They have a GPT that helps prepare slides for quarterly earnings calls, and another GPT that helps convert biotech terminology into approachable language for investor communications.

“Sometimes we’re so in our own world, and AI helps the brand think beyond that,” explained Kate Cronin, Chief Brand Officer of Moderna. “What would my mother want to know about Moderna, versus a regulator, versus a doctor? How do we tell our story in an effective way across different audiences? That’s where I think there’s a huge opportunity.”

A team of a few thousand can perform like a team of 100,000

With an ambitious plan to launch multiple products in the next few years, Moderna sees AI as a key component to their success—and their ability to stay lean as a business while setting new benchmarks in innovation.

“If we had to do it the old biopharmaceutical ways, we might need a hundred thousand people today,” said Bancel. “We really believe we can maximize our impact on patients with a few thousand people, using technology and AI to scale the company.”

Moderna has been well positioned to leverage generative AI having spent the last decade building a robust tech stack and data platform. The company fosters a culture of learning and curiosity, attracting employees that excel in adopting new technologies and building AI-first solutions.

By making business processes at Moderna more efficient and accurate, the use of AI ultimately translates to better outcomes for patients. “I’m really thankful for the entire OpenAI team, and the time and engagement they have with our team, so that together we can save more lives,” Bancel said.

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Publication Date: September 15, 2020

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Instructors should consider the timing of making videos available to students, as they may reveal key case details. In summer 2020, Stephane Bancel, CEO of biotech firm Moderna, faces several challenges as his company races to develop a vaccine for COVID-19. The case explores how a company builds a digital organization, and leverages artificial intelligence and other digital resources to speed its operations, manage its processes and ensure quality across research, testing and manufacturing. Built from the ground up as such a digital organization, Moderna was able to respond to the challenge of developing a vaccine as soon as the gene sequence for the virus was posted to the Web on January 11, 2020. As the vaccine enters Phase III clinical trials, Bancel considers several issues: How should Bancel and his team balance the demands of developing a vaccine for a virus creating a global pandemic alongside the other important vaccines and therapies in Moderna's pipeline? How should Moderna communicate its goals and vision to investors in this unprecedented time? Should Moderna be concerned it will be pegged as "a COVID-19 company?"

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The Coronavirus Crisis

Moderna's covid-19 vaccine shines in clinical trial.

Clinical data for Moderna's COVID-19 vaccine showed it was nearly 95% effective in preventing disease, according to an interim analysis described in a company release Monday. Maddie Meyer/Getty Images hide caption

Clinical data for Moderna's COVID-19 vaccine showed it was nearly 95% effective in preventing disease, according to an interim analysis described in a company release Monday.

A second COVID-19 vaccine now also appears highly effective in preventing illness following exposure to the virus that causes the disease.

The biotech company Moderna Inc. said Monday that its experimental vaccine was 94.5% effective in preventing disease, according to an analysis of its clinical trial.

The news comes a week after Pfizer and BioNTech said their vaccine was more than 90% effective .

The results for both vaccines come from interim analyses of large clinical studies. In the Moderna study there were 30,000 volunteers. Half got two doses of the vaccine 28 days apart; half got two shots of a placebo on the same schedule.

There were 95 instances of COVID-19 illness among the study participants; only five of those cases were in the vaccinated group. Ninety were in the group receiving the placebo. Of these, there were 11 cases of severe disease. The results indicate the vaccine was inducing the kind of immune response that protects people if they were exposed to the coronavirus.

"This positive interim analysis from our Phase 3 study has given us the first clinical validation that our vaccine can prevent COVID-19 disease, including severe disease," Stéphane Bancel, chief executive officer of Moderna, said in a statement.

Both the Moderna and Pfizer vaccines use the same technology to make their vaccines. It's based on a molecule known as mRNA , or messenger RNA. That molecule contains genetic instructions for making proteins inside cells.

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Pfizer says experimental covid-19 vaccine is more than 90% effective.

For the vaccine, researchers created an mRNA with the code for making the coronavirus spike protein . The protein is the key to the virus infecting cells. It's also what can trigger someone's immune system to make antibodies against the virus, but without causing infection since the rest of the virus is missing.

That two mRNA vaccines appear to be working is remarkable, since the technology is new and there hasn't been an mRNA vaccine approved by the Food and Drug Administration made to date.

The Moderna and Pfizer studies were conducted using slightly different protocols. To be counted as a COVID-19 case, participants in the Moderna study had to have at least two symptoms of disease in addition to a positive test for the virus. The Pfizer study required only one symptom. Also, Moderna waited 14 days following the second injection to begin counting cases; Pfizer's study started counting at seven days.

The vaccines also differ in their storage requirements. Moderna says its vaccine can be safely stored in freezers at about 25 degrees Fahrenheit (minus 4 degrees Celsius), a temperature easily reached by a home refrigerator freezer. Pfizer's vaccine required storage in specialized ultracold freezers capable of cooling below minus 94 degrees Fahrenheit (minus 70 degrees Celsius). Moderna also says its vaccine will remain potent for up to 30 days at normal refrigerated temperatures, which should ease distribution.

Both companies' vaccine studies managed to recruit a reasonably diverse group of people. Moderna reports 6,000 enrollees who identified as Hispanic or Latinx and more than 3,000 participants who identified as Black or African American, as well as 7,000 people older than 65, and 5,000 with high-risk chronic diseases.

Pfizer and Moderna are still gathering safety data the Food and Drug Administration has said is necessary for consideration of an emergency use authorization that would allow the companies to distribute the vaccine during the pandemic.

Fauci Voices Cautious Optimism About Moderna Vaccine, Calling Trial 'Quite Promising'

Side effects seen for the Moderna vaccine at the interim analysis included pain at the injection site, fatigue and aching muscles and joints. The data safety and monitoring board didn't identify "any significant safety concerns."

Moderna said it intends to file "in the coming weeks" with the FDA for authorization of the company's vaccine for emergency use.

The federal Operation Warp Speed project to hasten development of COVID-19 vaccines awarded Moderna a $1.5 billion contract in August to ramp up manufacturing and deliver 100 million vaccine doses, enough for 50 million people. The government has an option to buy up to 400 million more doses.

Moderna said Monday that it expects to be able to ship about 20 million vaccine doses in the U.S. by the end of 2020. Next year, the company said it expects to be able to make 500 million to 1 billion doses worldwide.

The research and development of the Moderna vaccine was aided by $955 million in federal funding from the Biomedical Advanced Research and Development Authority. Moderna has also been developing this vaccine alongside the National Institute for Allergy and Infectious Diseases, which in July told NPR it expects to spend about $410 million on the effort.

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Moderna , then pre-IPO – now a leader in mRNA technology for drug discovery – wanted to better understand the competitive landscape and patient journey of people with seven specific disease states. Moderna was referred to Sciencia because of our deep scientific knowledge acuity but more importantly, they knew our reputation of working hard and fast without sacrificing quality and they needed this project completed quickly. With no time to waste, we worked closely with the Head of Strategy at each step to ensure we were moving in the right direction. In less than 4 weeks, we needed to perform the analysis, develop a presentation, and deliver the final presentation to the C-suite.

To start, our team reviewed the internal scientific data and published materials of the disease areas to understand the pathologies in more detail. Specifically, we needed to understand the mechanism of action of these diseases and associated biomarkers, to understand their role, if any, in normal physiology versus pathologies. We continued to look into competition – including a pipeline analysis – to gain a high-level overview of the asset mechanism of action and the likelihood of approval and adoption in the clinic. Conducting desk research and physician interviews, we strived to understand the patient journey of people with the disease, while documenting the presentation and diagnosis, treatment paradigm and long-term care, and health outcomes.

After synthesizing all the research materials and patient journey map, we transformed the data into a visual story to help our audience – busy C-suite executives – quickly understand the market landscape and clinical adoption risk of each disease state. In just under 4 weeks, working closely with the internal team, we researched the landscape, performed interviews and delivered a visual and scientifically accurate presentation that allowed their team to immediately grasp the situation. From there, they made decisions to push forward internal programs, positioning themselves for success in the market.

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• Waning of vaccine...

Waning of vaccine effectiveness against moderate and severe covid-19 among adults in the US from the VISION network: test negative, case-control study

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• Peer review
• Suchitra Rao , associate professor of pediatrics 2 ,
• Brian E Dixon , director of public health informatics 3 4 ,
• Patrick K Mitchell , senior epidemiologist 5 ,
• Malini B DeSilva , internal medicine specialist 6 ,
• Stephanie A Irving , project director 7 ,
• Ned Lewis , data manager 8 ,
• Karthik Natarajan , assistant professor of biomedical informatics 9 10 ,
• Edward Stenehjem , infectious disease specialist 11 ,
• Shaun J Grannis , vice president of data analytics 3 12 ,
• Jungmi Han , research analyst 9 ,
• Charlene McEvoy , internal medicine specialist 6 ,
• Toan C Ong , research instructor 2 ,
• Allison L Naleway , senior epidemiologist 7 ,
• Sarah E Reese , senior biostatistician 5 ,
• Peter J Embi , professor of medicine ,
• Kristin Dascomb , medical director infection prevention 11 ,
• Nicola P Klein , senior research scientist 8 ,
• Eric P Griggs , epidemiologist 1 ,
• I-Chia Liao , analytics developer 13 ,
• Duck-Hye Yang , senior epidemiologist 5 ,
• William F Fadel , clinical assistant professor 3 4 ,
• Nancy Grisel , analyst 11 ,
• Kristin Goddard , senior research manager 8 ,
• Palak Patel , epidemiologist 1 ,
• Kempapura Murthy , SAS programmer 13 ,
• Rebecca Birch , senior epidemiologist 5 ,
• Nimish R Valvi , postdoctoral fellow 3 ,
• Julie Arndorfer , analyst 11 ,
• Ousseny Zerbo , research scientist 8 ,
• Monica Dickerson , epidemiologist 1 ,
• Chandni Raiyani , biostatistician 13 ,
• Jeremiah Williams , surveillance coordinator 1 ,
• Catherine H Bozio , epidemiologist 1 ,
• Lenee Blanton , research epidemiologist 1 ,
• Ruth Link-Gelles , epidemiologist 1 ,
• Michelle A Barron , senior medical director 2 ,
• Manjusha Gaglani , chief of pediatric infectious diseases 13 ,
• Mark G Thompson , epidemiologist 1 ,
• Bruce Fireman , biostatistician 8
• 1 Centers for Disease Control and Prevention COVID-19 Response Team, Atlanta, GA, USA
• 2 Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
• 3 Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN, USA
• 4 Fairbanks School of Public Health, Indiana University, Indianapolis, IN, USA
• 5 Westat, Rockville, MD, USA
• 6 HealthPartners Institute, Minneapolis, MN, USA
• 7 Center for Health Research, Kaiser Permanente Northwest, Portland, OR, USA
• 8 Kaiser Permanente Vaccine Study Center, Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
• 9 Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
• 10 New York Presbyterian Hospital, New York, NY, USA
• 11 Division of Infectious Diseases and Clinical Epidemiology, Intermountain Healthcare, Salt Lake City, UT, USA
• 12 Indiana University School of Medicine, Indianapolis, IN, USA
• 13 Baylor Scott &White Health, Temple, TX, USA
• Correspondence to: J M Ferdinands zdn5{at}cdc.gov

† Patients aged <50 years were excluded from estimates of fourth dose effectiveness; thus, column sum might not equal 100% of encounters.

• Accepted 9 September 2022

Objective To estimate the effectiveness of mRNA vaccines against moderate and severe covid-19 in adults by time since second, third, or fourth doses, and by age and immunocompromised status.

Design Test negative case-control study.

Setting Hospitals, emergency departments, and urgent care clinics in 10 US states, 17 January 2021 to 12 July 2022.

Participants 893 461 adults (≥18 years) admitted to one of 261 hospitals or to one of 272 emergency department or 119 urgent care centers for covid-like illness tested for SARS-CoV-2.

Main outcome measures The main outcome was waning of vaccine effectiveness with BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccine during the omicron and delta periods, and the period before delta was dominant using logistic regression conditioned on calendar week and geographic area while adjusting for age, race, ethnicity, local virus circulation, immunocompromised status, and likelihood of being vaccinated.

Results 45 903 people admitted to hospital with covid-19 (cases) were compared with 213 103 people with covid-like illness who tested negative for SARS-CoV-2 (controls), and 103 287 people admitted to emergency department or urgent care with covid-19 (cases) were compared with 531 168 people with covid-like illness who tested negative for SARS-CoV-2. In the omicron period, vaccine effectiveness against covid-19 requiring admission to hospital was 89% (95% confidence interval 88% to 90%) within two months after dose 3 but waned to 66% (63% to 68%) by four to five months. Vaccine effectiveness of three doses against emergency department or urgent care visits was 83% (82% to 84%) initially but waned to 46% (44% to 49%) by four to five months. Waning was evident in all subgroups, including young adults and individuals who were not immunocompromised; although waning was morein people who were immunocompromised. Vaccine effectiveness increased among most groups after a fourth dose in whom this booster was recommended.

Conclusions Effectiveness of mRNA vaccines against moderate and severe covid-19 waned with time after vaccination. The findings support recommendations for a booster dose after a primary series and consideration of additional booster doses.

Introduction

Randomized trials of BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) vaccines showed 94-95% protection against covid-19 among adults and suggested efficacy against covid-19 requiring hospital admission. 1 2 Since the introduction of these vaccines in December 2020, evidence has accumulated that their effectiveness wanes over time since vaccination, especially against milder disease, 3 4 5 6 7 8 9 they are less effective against omicron than earlier SARS-CoV-2 variants, 10 and a third (booster) dose restores high effectiveness against severe disease. 10 11 12 13 Although protection against severe omicron related disease is believed to be high for several months after a third dose, the durability of protection and how this effect can vary by age group, immunocompromised status, and vaccine product is uncertain. In March 2022, the US Centers for Disease Control and Prevention recommended a second booster dose only for specific subgroups at high risk (such as adults aged 50 and older). 14 A more complete understanding of the effectiveness and durability of third and fourth doses of the mRNA vaccines is important to inform policy about booster doses.

The CDC’s VISION network previously examined the effectiveness of mRNA vaccines against admissions to hospital or emergency visits and urgent care visits associated with covid-19, with data from eight healthcare systems. 15 In this article, we update VISION’s analyses of mRNA vaccine effectiveness, focusing on the durability of three and four dose protection against severe disease (ie, admission to hospital) during the omicron period. We assess the trajectory of vaccine effectiveness overall and in subgroups defined by age, immunocompromised status, and vaccine product.

Study design

The VISION network has been described previously. 15 We applied a test negative design to estimate vaccine effectiveness of mRNA vaccines using retrospectively collected data. We focused on mRNA vaccines because they comprise more than 95% of covid vaccines administered in the US. 16 Separate analyses were done of patients who were admitted to hospital (hospital sample) and patients who received care in an emergency department or urgent care clinic (emergency department or urgent care sample).

Study population and setting

The study population included adults (≥18 years) who received care for covid-like illness at a VISION network hospital or emergency department or urgent care center and had molecular testing for SARS-CoV-2 at least 14 days after vaccines became locally available for their age group (17 January to 3 May 2021). The last contact included in this study period occurred on 12 July 2022. We excluded individuals who received any vaccine other than the BNT162b2 or mRNA-1273 vaccine, individuals who received more than four doses of an mRNA vaccine before the index medical contact, individuals who received only one dose of an mRNA vaccine less than 14 days before the index contact or who had a third or fourth dose less than seven days before the index contact, individuals known to have a positive laboratory test result for a SARS-CoV-2 infection more than 14 days before the index contact, and individuals with a positive SARS-CoV-2 test result but no diagnoses or symptoms suggesting covid-19 illness.

Vaccination status

Vaccination status was categorized by the number of doses received and the number of months between the most recent vaccine dose and the index contact date (referred to as time since vaccination). Patients were considered partially vaccinated if they received only one dose at least 14 days prior to the index contact date or had received a second dose less than 14 days previously. Patients with no record of vaccination before the index contact date were considered unvaccinated. Patients with three doses were those who received a third dose in a primary vaccination series (eg, among immunocompromised individuals) or a booster dose after a primary series of two doses. Aligning with recommendations for receipt of a fourth dose, we examined the effectiveness of four doses among adults aged 50 years or older and among immunocompromised adults of any age. Vaccination status was ascertained from immunization registries, electronic health records, and insurance claims.

The primary outcome was a positive or negative molecular SARS-CoV-2 result for a test done within 14 days before a medical contact to less than 72 h after among patients presenting with covid-like illness, as identified from ICD-9 and ICD-10 (international classification of diseases, ninth and 10th revision, respectively) diagnostic codes (supplemental methods; supplemental table S1). The index date for each contact was the earlier of either the contact date or the date of the closest SARS-CoV-2 molecular assay. An individual could be included as a case once in the emergency department or urgent care sample and once in the hospital sample. Individuals could be included as a control multiple times.

Statistical analysis

We used a test negative case-control design in which cases were patients with covid-like illness with laboratory confirmed covid-19 and controls were patients with covid-like illness and negative SARS-CoV-2 test results (controls could have had positive test results for other respiratory viruses such as influenza). We compared cases with controls in the hospital sample, and separately compared cases with controls in the emergency or urgent care sample. Cases were not individually matched to controls.

Conditional logistic regression was used to examine case-control status in relation to vaccination status categorized as vaccinated with four, three, or two doses, or partially vaccinated; unvaccinated individuals were used as the reference group. To examine waning of vaccine effectiveness, we categorized people who were vaccinated using time specific indicators defined by two month intervals of time since vaccination; unvaccinated individuals were used as the reference group. We exponentiated the regression coefficient of each vaccination status indicator to yield an odds ratio, subtracted the odds ratio from 1 to estimate vaccine effectiveness, and multiplied by 100 to scale vaccine effectiveness as a percentage. In several analyses, a sparse bimonthly interval for which the vaccine effectiveness estimate had a confidence interval wider than 50 percentage points was combined with the previous bimonthly interval to provide a more precise estimate of vaccine effectiveness (see supplemental methods). Vaccine effectiveness estimates (and confidence limits) were scaled to a range of –100% to 100%. 17

Logistic regression models were conditioned by calendar week and geographical area such that we compared cases with controls tested during the same week in the same region (supplemental table S2). Covariates included in the models were those determined through bivariate analyses to be statistically significantly associated with both the outcome and vaccination status, as well as those specified a priori as established confounders, including age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, immunocompromised status, and local viral circulation. Cubic splines were used for age, seven day average positivity of SARS-CoV-2 test in the area of the contact, and the propensity to be vaccinated; others were indicator variables. Propensity scores (supplemental methods) predicted vaccination (any versus none) based on demographics, comorbidities (supplemental table S3), and characteristics of the facility (supplemental table S4), and were derived independently for each period of variant dominance (supplemental table S5). Patients who were immunocompromised were identified by ICD-9 and ICD-10 diagnostic codes (supplemental methods). 18 We conducted separate analyses for three periods based on when a variant accounted for 50% or more of sequenced isolates in each site: before delta was predominant, when delta was predominant, and when omicron was predominant (supplemental table S6). We assessed the magnitude of waning as the difference in vaccine effectiveness between patients who had recently been vaccinated (defined as less than two months) and patients at a specified level of time since vaccination (eg, four to five months from dose 3), and we examined waning by age (18-44 years, 45-64 years, ≥65 years), vaccine product, and immunocompromised status. Bootstrapping was used to estimate a 95% confidence interval around the difference between vaccine effectiveness at less than two months and vaccine effectiveness at four to five months.

We conducted several sensitivity analyses. First, we added to the study population patients with a known prior infection to assess the sensitivity of results to whether previously infected patients are included or excluded.. Second, we wanted to distinguish results between patients who had been admitted to hospital and patients who had been admitted to an emergency department or to urgent care. Therefore, we examined vaccine effectiveness in the emergency department or urgent care sample and omitted patients admitted to hospital within 30 days. Third, we investigated a negative control exposure 19 by examining vaccine effectiveness in patients who received their first dose less than 14 days before the index date of contact. These patients were not expected to have substantial vaccine induced protection, and a vaccine effectiveness estimate substantially more than zero would be evidence of residual confounding. 20

Analyses were conducted with SAS version 9.4 and R version 4.1.2. All confidence limits are 95% intervals. Confidence intervals excluding the null value were considered statistically significant.

Patient and public involvement

Study participants contributed in important ways to this research by supplying the underlying data on which the study is based. It was not, however, feasible to involve them in the design, conduct, reporting, or dissemination of this study because the study was conducted under the CDC’s covid-19 incident response structure and limited to analysis of retrospectively collected electronic data only, with no patient interaction.

Study population

From 17 January 2021 to 12 July 2022, 259 006 patients were admitted to 261 hospitals and 634 455 were admitted to 272 emergency departments or to 119 urgent care centers. The hospital sample included 17 446 people with covid-19 during the omicron period, 23 379 during the delta period, and 5078 before delta was dominant. The emergency department or urgent care sample included 57 174 people with covid-19 during the omicron period, 39 909 during the delta period, and 6204 before delta was dominant ( table 1 ; supplementary figs S1-S18).

Characteristics of adults with covid-19-like illness who were admitted to hospital or to an emergency department or urgent care, and percentage with laboratory confirmed SARS-CoV-2 infection. Data are number of patients (percentage of column or row) unless stated otherwise

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In the hospital sample, the median age was 69 years (interquartile range 56-79, 11.2% were black participants, 9.8% were Hispanic, and 23.3% had an immunocompromising condition. In the emergency department or urgent care sample, the median age was 51 years (interquartile range 33-69), 11.0% were black participants, 13.3% were Hispanic, and 4.5% had an immunocompromising condition ( table 1 ). Characteristics by vaccination status are given in supplemental tables S7 and S8. Median times between the last vaccination date and index contact date in the hospital sample were 173 (interquartile range 97-248) days for two doses, 105 (56-156) days for three doses, and 33 (19-50) days for four doses, and in the emergency department or urgent care sample were 179 (110-247) days for two doses, 100 (52-155) days for three doses, and 34 (20-52) days for four doses.

Vaccine effectiveness

Vaccine effectiveness estimates from the hospital and emergency department or urgent care samples are shown in figures 1 and figure 2 and detailed in supplemental tables S9-S14.

Vaccine effectiveness (%) against covid-19-associated hospital admissions by time since vaccination and period of variant predominance. Vaccine effectiveness estimates are adjusted for geographic area, calendar week, age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, immunocompromise status, local viral circulation, and propensity to be vaccinated score. CI=confidence interval; col=column. *Figure 3 presents 4 findings for 4-dose recipients in the subgroups recommended for a fourth dose

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Vaccine effectiveness (%) against covid-19-associated emergency department and urgent care visits by time since vaccination and period of variant predominance. Vaccine effectiveness estimates are adjusted for geographic area, calendar week, age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, immunocompromise status, local viral circulation, and propensity to be vaccinated score. CI=confidence interval; col=column. *Supplemental Table 14 presents findings for 4-dose recipients in the subgroups recommended for a fourth dose

Vaccine effectiveness against covid-19 requiring hospital admission was 94% (95% confidence interval 93% to 95%) in the pre-delta period and 96% (95% to 97%) in the delta period, during the initial two months after the second dose. By months four to five after the second dose, vaccine effectiveness against hospital admission decreased to 87% (77% to 93%) in the pre-delta period and 89% (88% to 90%) in the delta period. In the omicron period, two dose vaccine effectiveness against hospital admission was lower than in the earlier periods, both before and when delta was dominant, and waned more, decreasing from 73% (63% to 80%) initially to 57% (51% to 62%) by four to five, and to 40% (32% to 47%) by 12 months after the second dose.

The patterns of vaccine effectiveness estimates from the emergency department or urgent care sample were similar. Vaccine effectiveness of two doses against emergency department or urgent care visits was initially high in the pre-delta period (95%; 94% to 96%) and delta period (93%; 92% to 94%) and then waned. During the omicron period, vaccine effectiveness of two doses against emergency department or urgent care visits was lower initially (63%; 57% to 68%) than in the earlier pre-delta and delta periods and then waned more. From up to one month after the second dose to months four to five, the vaccine effectiveness of a second dose decreased by 9 percentage points (95% confidence interval 4 to 16) during the pre-delta period, by 7 percentage points (7 to 9) during the delta period, and by 26 percentage points (19 to 32) during the omicron period.

A third dose initially restored high levels of protection against both hospital admissions and emergency department or urgent care visits, then began to wane. In the hospital sample, vaccine effectiveness of three doses was initially 96% (95% to 96%) during the delta period and 89% (88% to 90%) during the omicron period. Similarly, in the emergency department or urgent care sample, the vaccine effectiveness of a third dose was initially 96% (95% to 96%) during the delta period and 83% (82% to 84%) during the omicron period. Waning was evident in both samples by four to five months after the third dose during the omicron period, when vaccine effectiveness decreased to 66% (63% to 68%) against hospital admission and to 46% (44% to 49%) against emergency department or urgent care visits.

Vaccine effectiveness against hospital admission after a fourth dose increased to 72% (51% to 83%) in the 50-64 year group and to 76% (71% to 80%) in the 65 years and older age group ( fig 3 ). Similarly, vaccine effectiveness against emergency department or urgent care visits after a fourth dose increased to 57% (47% to 65%) and 73% (69% to 76%) among the 50-64 year and 65 years and older age groups, respectively (supplemental table S14). Vaccine effectiveness of a fourth dose among immunocompromised individuals in the hospital sample was 48% (29% to 62%; fig 4 ), but we were unable to measure this precisely enough in the emergency department or urgent care sample.

Vaccine effectiveness (%) against covid-19-associated hospital admissions by time since vaccination and age group, restricted to omicron period. Vaccine effectiveness estimates are adjusted for geographic area, calendar week, age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, immunocompromise status, local viral circulation, and propensity to be vaccinated score. CI=confidence interval; col=column. *Patients aged <50 years were excluded from the estimate of fourth dose effectiveness for the subgroup aged 45-64 years.

Vaccine effectiveness (%) against covid-19-associated hospital admissions by time since vaccination and immunocompromise status, restricted to omicron period. Vaccine effectiveness estimates are adjusted for geographic area, calendar week, age, race, ethnicity, presence of respiratory and non-respiratory comorbidities, local viral circulation, and propensity to be vaccinated score. CI=confidence interval; col=column

Vaccine effectiveness in subgroups

In all subgroups examined, vaccine effectiveness waned as time elapsed after the second dose, increased markedly with a third dose, and waned as time elapsed (supplemental tables S9-14). Vaccine effectiveness also substantially improved after a fourth dose among most subgroups for whom this booster dose was recommended. Comparing the initial two months after the third dose with months four to five, vaccine effectiveness against hospital admission during the omicron period decreased by 33 percentage points (95% confidence interval 16 to 56) in the 18-44 years group, 31 (21 to 40) in the 45-64 years group, and 19 (16 to 22) in the 65 years or older group ( fig 3, table 2 ). Results were similar in post hoc analyses that were restricted to individuals without immunocompromising conditions (supplemental table S15).

Estimates of mRNA vaccine effectiveness against covid-19 related hospital admissions during omicron period by age group. Data are number of patients (percentage of column or row) unless stated otherwise.

Vaccine effectiveness was higher in recipients of the mRNA-1273 than BNT162b2 vaccine in all three variant periods in both the hospital sample and the emergency department or urgent care sample. Vaccine effectiveness waned in recipients of both vaccine products. In the hospital sample during the omicron period, vaccine effectiveness of mRNA-1273 waned from 91% (89% to 92%) to 65% (60% to 70%) by four to five months after three doses whereas vaccine effectiveness of BNT162b2 waned from 88% (86% to 90%) to 66% (63% to 70%) after three doses ( table 3 ).

Estimates of vaccine effectiveness against covid-19 related hospital admissions during omicron period by mRNA vaccine product. Data are number of patients (percentage of column or row) unless stated otherwise

Vaccine effectiveness after two and three doses was generally lower among individuals who were immunocompromised, in both the hospital and the emergency department or urgent care samples, in each period and at all times since vaccination ( fig 4 , table 4 , supplemental tables S9-S14). In the omicron period, vaccine effectiveness of three doses against hospital admission waned from 78% (73% to 82%) to 48% (40% to 55%) by months four to five in the immunocompromised subgroup compared with 91% (90% to 92%) to 71% (68% to 74%) in the subgroup without immunocompromise ( table 4 ).

Estimates of mRNA vaccine effectiveness against covid-19 related hospital admissions during omicron period by immunocompromised status. Data are number of patients (percentage of column or row) unless stated otherwise.

Sensitivity analyses

In the first sensitivity analysis, vaccine effectiveness estimates in both samples were similar but slightly lower if patients with previous SARS-CoV-2 infection were included (supplemental tables S16 and S17). In the second sensitivity analysis, vaccine effectiveness estimates were similar but lower if the emergency department or urgent care sample excluded patients who were later admitted to hospital. In the third sensitivity analysis, vaccine effectiveness ranged from –5% to 24% among patients whose index date for medical contact was less than 14 days after the first dose, consistent with the little protection induced by the vaccine during this two week period.

Principal findings

Protection against severe omicron related covid-19 was high after three doses of an mRNA vaccine but began to wane less than six months after the third dose. In the hospital sample, vaccine effectiveness after a third doses was 89% among individuals within two months but decreased to 66% among individuals at four to five months. In the emergency department or urgent care sample, vaccine effectiveness of a third dose was 83% within two months but decreased to 46% at four to five months. In all subgroups defined by age, immunocompromised status, and vaccine product, the third dose was initially associated with markedly increased protection, but vaccine effectiveness was lower by four to five months. Vaccine effectiveness increased after a fourth dose for most subgroups for whom this booster dose is recommended in the US. Although we have not yet observed events more than four months from a fourth dose, our results suggest that protection after the fourth dose begins to wane after a few months.

Comparison with other studies

Our vaccine effectiveness estimates for mRNA vaccines are broadly consistent with those in other reports: vaccine effectiveness was lower against the omicron variant than earlier variants, 10 21 22 vaccine effectiveness waned after a second dose, 3 4 5 6 7 8 9 and a third dose restored high levels of protection against severe covid-19 during the omicron and delta periods. 10 11 12 13 Our results are also consistent with other reports of waning protection after three mRNA doses. 23 24 25 As with others, we noted less waning against more severe outcomes, 3 26 lower vaccine effectiveness among individuals who were immunocompromised, 17 27 and higher vaccine effectiveness among recipients of mRNA-1273 compared with recipients of BNT162b2. 10 23 24 We also observed improvement in vaccine effectiveness after a fourth dose. 28

Strengths and limitations of this study

One strength of our study is the number and diversity of sites and inclusion of outcomes of varying severity. Additionally, our sample size was large enough to detect modest waning of vaccine protection and to allow stratification of vaccine effectiveness estimates by immunocompromise status. We rigorously controlled for calendar time and geography such that cases were compared with controls tested during the same week in the same geographical area. This comparison allowed us to distinguish differences in vaccine effectiveness attributable to the waning of vaccine induced immunity from those attributable to the change in dominance of SARS-CoV-2 variants.

Our study has limitations. First there is residual confounding if the timing of primary vaccination or booster doses was related to covid-19 risk in unmeasured ways (eg, mask use or occupation). However, we did not observe substantial vaccine protection in the two weeks after a first dose, which provides reassurance that residual confounding is limited. Second, although our test negative design is intended to avoid selection bias from healthcare seeking behavior, the design could induce selection bias arising from factors associated with a covid-like illness but not with covid-19. For example, inclusion of individuals who had influenza as controls could underestimate vaccine effectiveness due to the correlation between covid-19 vaccination and influenza vaccination. Because fewer than 5% of people in the control group in our study were positive for influenza, we expect this bias to be minimal. Also, we cannot rule out selection bias arising from reliance on clinician directed testing, although we note that almost all the patients admitted to hospital with covid-like illness were tested for SARS-CoV-2. Third, immunocompromised status was ascertained only from diagnostic codes at the time of medical contact (without data on prescriptions or laboratory tests), and we could not distinguish whether a third dose was in a primary series for people who were immunocompromised or was a booster dose. Insufficient adjustment for immunocompromised status might have biased vaccine effectiveness estimates downward, especially for those who were vaccinated and received a booster dose relatively early. However, we found waning protection in stratified analyses among both individuals who were immunocompromised and individuals who were not immunocompromised. Fourth, we did not have viral genomic sequence data. Fifth, although we excluded individuals with documented previous SARS-CoV-2 infection, our data might have missed many past infections. Sensitivity analyses that included people with known previous infections suggest that our vaccine effectiveness estimates would be higher if we could have ascertained and excluded everyone with protection induced by infection. Sixth. although we interpret our analyses of the hospital sample as pertaining to severe covid-19, some patients admitted to hospital could have tested positive for other reasons while being in hospital, especially during the omicron period. 29 To address this, patients were not eligible for inclusion if they had a positive SARS-CoV-2 test result but no diagnoses suggesting a covid-19 infection. Seventh, although our sample includes enough outcome events to yield precise estimates of vaccine effectiveness for the overall adult population, estimates of vaccine effectiveness against admissions to hospital for covid-19 were less precise for younger adults and individuals who were immunocompromised owing to smaller sample sizes. Finally, we pooled data from heterogeneous populations in 10 US states; however, our findings might not be generalizable to other populations.

Policy implications

To evaluate the clinical significance of waning vaccine effectiveness, consideration of the absolute number of people admitted to hospital that would have been prevented had no waning occurred is helpful. However, this number depends on the background rate of severe covid-19, which sometimes varied 10-fold or more over several weeks. In this context, hospital admissions that would be prevented during an anticipated surge are an appropriate alternative. For example, the rate of hospital admissions related to covid-19 reached about 1500 per million unvaccinated adults each week in January 2022 in the US 30 ; if incidence surges that high again, then for every million adults who lose 20 percentage points of vaccine protection, about 300 additional people each week (1500×0.20) will be admitted to hospital owing to covid-19 compared with no waning effect. During the omicron period, vaccine effectiveness waned within six months of the third dose by about 20 percentage points among those without immunocompromising conditions and by more than 40 percentage points among those with immunocompromising conditions. This amount of waning is enough to be relevant for clinical and policy considerations about the need for boosters or other protective measures. Combined with evidence of the safety and immunogenicity of an additional vaccine dose, 31 32 33 our findings lend support for consideration of additional doses beyond the primary series.

Conclusions

Protection conferred by mRNA vaccines against moderate (emergency department or urgent care) and severe (hospital admission) covid-19 waned during the months after primary vaccination, increased substantially after the third dose, and waned again by four to five months. A fourth dose improved vaccine effectiveness among those for whom this booster dose was recommended. Vaccine effectiveness waned less against severe disease than against moderate disease. Vaccine effectiveness of either mRNA vaccine waned among adults of all ages. Among immunocompromised individuals, vaccine effectiveness was lower and waning was more noticeable. These findings support recommendations for a third vaccine dose and consideration of additional booster doses.

What is already known on this topic

Studies of the BNT162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna) covid-19 vaccines suggest that their effectiveness decreases over time and increases with an additional dose

How this pattern has varied with the dominant variant and number of vaccine doses, or by age group, immunocompromise status, and vaccine product is, however, not known

What this study adds

Among US adults of all ages, protection provided by either mRNA vaccine against moderate and severe covid-19 waned after primary vaccination, increased markedly after a third dose, and then waned again by four to five months after a third dose

Vaccine effectiveness diminished less against severe disease than against moderate disease

A fourth dose improved vaccine effectiveness among most subgroups for whom it was recommended; overall, our findings support recommendations for broad use of booster doses

Ethics statements

Ethical approval.

This study was approved by the institutional review board of Westat.

Data availability statement

No additional data available.

Contributors: All authors contributed to the design of the study. PKM, SER, RB, and DY performed the statistical analysis. SR, BD, MBD, SAI, NL, KN, ED, SJG, JH, CM, TCO, ALN, PJE, KD, NPK, IL, WFF, NG, KG, KP, NRV, JA, OZ, CR, MB, MG, and BF were involved in data collection and study coordination at partner sites. EPG, PP, MD, JW, CHB, LB, and RL provided data collection and central study coordination at US Centers for Disease Control and Prevention, supervised by MT. JMF and BF produced the first draft of this manuscript and all authors reviewed, edited, and approved the final version. JMF is the guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This study was funded by the Centers for Disease Control and Prevention through contract 75D30120C07986 to Westat and contract 75D30120C07765 to Kaiser Foundation Hospitals.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: NPK reports institutional support from Pfizer, Merck, GlaxoSmithKline, Sanofi Pasteur, and Protein Sciences (now Sanofi Pasteur) for unrelated studies and institutional support from Pfizer for a covid-19 vaccine trial. CM received institutional support from AstraZeneca for a covid-19 vaccine trial. ALN received institutional support from Pfizer for an unrelated study of meningococcal B vaccine safety during pregnancy. SR received grant funding from GlaxoSmithKline and Biofire Diagnostics. Authors declare no financial relationships with any organizations that might have an interest in the submitted work in the previous three years, and no other relationships or activities that could appear to have influenced the submitted work.

The lead author (JMF) affirms that this manuscript is an accurate and transparent account of the study being reported and that no important aspects of the study have been omitted.

Dissemination to participants and related patient and public communities: The individual level dataset from this study is held securely in limited deidentified form at the US Centers for Disease Control and Prevention. Data sharing agreements between CDC and data providers prohibit CDC from making this dataset publicly available. CDC will share aggregate study data once study objectives are complete, consistent with data use agreements with partner institutions.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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