Why were Covid vaccine trials so fast?

Something I remember fairly vividly from the first few months of the pandemic was a sense of hopelessness that any effective drug or vaccine would become available anytime soon.Most people didn’t believe it was possible to develop vaccines in the timeframe needed for them to be useful. Some looked at past vaccine timelines, which had averaged roughly 8 to 12 years, and thought this one would be similar. Others thought that, even though it was an emergency situation, it would still take at least a year and a half, or two years, or maybe even four.“The grim truth behind this rosy forecast is that a vaccine probably won’t arrive any time soon. Clinical trials almost never succeed. We’ve never released a coronavirus vaccine for humans before. Our record for developing an entirely new vaccine is at least four years.”— Stuart Thompson, New York Times, April 2020.I had a different conclusion. In a piece I wrote in the summer of 2020, I explained why I believed that vaccines would most likely arrive within a year of the beginning of the pandemic (placing a 58% probability on enough doses for 25 million Americans being approved and available between October 2020 and March 2021, with my central estimate landing around February 2021).We now know how the timeline panned out and my forecast was, if anything, slightly too pessimistic, since vaccines first became available three months earlier, in December 2020. In this post, I want to go into more detail and take a look back at what happened. Why were Covid vaccine trials so fast?One of the most surprising things about Covid vaccines is how many of them there are: mRNA vaccines (Pfizer and Moderna), viral vector vaccines (Oxford-AstraZeneca and Johnson & Johnson), protein subunit vaccines (Novavax), inactivated whole virus vaccines (Sinovac and Sinopharm), and others. Although their protection dropped as the virus evolved into new strains, I take this bounty of options as a result of the coronavirus being relatively easy to develop vaccines for.Part of the reason is the disease itself. With some infections, like HIV, no one clears the virus naturally or develops lasting immunity to it, so it’s hard to know what a vaccine should mimic.

Covid-19 was very different: it was evident early on that most people recovered and developed antibodies that could neutralize the virus. This suggested it was possible to prime the immune system with a vaccine.Another reason was prior research into coronaviruses. Work on SARS and MERS from earlier coronavirus outbreaks meant scientists already understood some of their features. They had identified the key antigen the immune system reacted against – the spike protein – and had animal models and laboratory assays ready to go. Some vaccines against animal coronaviruses had already been developed. Candidate vaccines for the earlier SARS virus had also been developed for humans (and shelved after the 2003 SARS epidemic was contained). When SARS-CoV-2 arrived, lots of this groundwork could be picked up.There’s another feature that explains why some vaccines ended up highly effective: a stabilized spike protein. The spike protein gives the coronavirus its crown-like appearance, and is the main ingredient in most Covid vaccines. But interestingly, it changes shape during an infection. Before it fuses with a cell, it sits in a compact “pre-fusion” form (on the left, below); afterwards, it springs into an elongated “post-fusion” form (on the right). The former boosts immunity most because it’s what antibodies generally encounter, before the virus has entered cells. But when the spike protein is isolated for a vaccine, it tends to collapse into the later, post-fusion form.Conformation of the pre-fusion (left) vs post-fusion (right) spike protein, as ribbon diagrams. Adapted lightly from Yongfei Cai et al. (2020)Over the past decade, advances in structural biology, especially in cryo-electron microscopy, have helped scientists see the pre-fusion shape of coronaviruses directly. With that knowledge, they introduced a few mutations to stabilize the spike protein in its pre-fusion form, better for the immune system to recognize. This is a big reason why vaccines with the stabilized pre-fusion protein (including the mRNA vaccines and Novavax) generated much stronger neutralizing antibody responses and were likely much more effective than vaccines without the stabilized form (including the AstraZeneca and Sputnik V vaccines).All of these features meant the chances of a vaccine succeeding were somewhat high. They’re also why they could be designed very fast – of course, with a lot of work on the part of scientists.

It took two days to design the Moderna vaccine, which then spent 63 days in preclinical testing before entering clinical trials, versus a weekend and about 100 days for the Oxford/AstraZeneca vaccine. But the vaccines would still need to be tested in clinical trials and gain regulatory approval afterwards, and this process usually takes several years.The timeline for vaccine development typically goes like this. A candidate vaccine is first designed and tested in the lab and in animals, as ‘preclinical testing’, which takes around 1.5 years on average. Next, in ‘phase 1 trials’, it’s tested in a small number of participants for basic data on safety and the immune response, taking around 2.5 years. Phase 2 trials expand this to hundreds of participants to refine the dose and gather more data on immunogenicity and safety, and take around 3 years. Then phase 3 trials test efficacy and safety in hundreds or thousands of people, and take around 2.5 years. After all these stages, all the data is submitted to the regulator as a package, which reviews it, usually taking another year.Historically, successful vaccines have taken around 10.7 years to make it through the whole pipeline. Each phase happens sequentially and functions as a ‘go / no-go’ system, where candidates are dropped if they flop at a stage.Covid vaccine trials took place quite differently. Rather than running sequentially, the phases ran in parallel, as you can see in the diagram below. For example, phase 1 and 2 trials were combined, collecting the relevant data for each at once, or phase 2 and 3 were combined in the same way. Ultimately, the same amount of data was collected in a shorter timeframe, by overlapping the phases rather than doing them one by one.This was a major reason it was possible to condense the whole timeline so much. But even then, the trials still ran much faster than usual. For example, Pfizer’s combined phase 2-3 trial then took about 4.5 months, but it usually takes an average around 2.5 years for phase 3 trials alone. What made that possible?One of the biggest delays in clinical trials is the process of recruiting enough participants to run them at all. Only half of trials meet their original target for recruitment, and a fifth revise their target downward. But the Covid vaccine trials recruited huge numbers, fast.

The phase 3 trials for Moderna and Pfizer enrolled around 30,000 and 44,000 participants respectively, making them some of the largest clinical trials in history.I think there were a couple of reasons this was easier than usual. One was that people were simply more willing to volunteer. It was a pandemic after all, and everyone’s attention was on it, plus, many people were stuck at home without much to do anyway. Interest in volunteering for trials was very high. Dr Jim Kublin, who helped run a clinical trial network for Covid, recalled: “My colleagues were enrolling a phase one study for the Moderna vaccine, and they had like 10,000 people sign up on their website. They needed 40 people to enroll.”The infrastructure to recruit participants was also more streamlined than usual. The National Institute of Allergy and Infectious Diseases (NIAID) created a clinical trial network with a shared volunteer registry called COVID-19 Prevention Network (CoVPN), from four existing NIAID trial networks, to supplement recruitment into trials. Volunteers could sign up with a short screening questionnaire to the network as a whole and be routed to whichever trial fit. The registry helped support recruitment for the late-stage trials of Moderna, AstraZeneca, Janssen, and Novavax, and by autumn 2020, almost half a million Americans had signed up through it to take part in Covid vaccine trials. Meanwhile in the UK, a similar shared volunteer registry recruited participants through the NHS. Over half a million volunteers signed up in total, feeding into trials including those for Novavax and Oxford/AstraZeneca.Number of people registering on the Covid-19 Prevention Network (CoVPN) to participate in coronavirus vaccine trials. Left-axis: daily registrations (incident, in blue). Right-axis: total registrations (cumulative, in red). Over 300,000 people volunteered within the first month of the platform’s launch. Source: Neil F. Abernethy et al. (2025).It also took less time to test new drugs and vaccines because the disease was spreading rapidly. It’s challenging to run trials fast when diseases are rare or slow to develop. Taking a simple example, imagine a trial where 100 people are given a placebo and 100 people given a vaccine.

If only 1 or 2% of them (so 1 or 2 people) caught Covid during the trial as the base rate, it would be very hard to spot a reduction from that already-low number. Even if vaccines reduced infections by half, you’d hardly be able to tell in this example, not with much confidence anyway. Without a much larger sample size, a difference between the two groups could simply reflect noise.This reflects the ‘statistical power’ of the trial, and that it’s easier to figure out whether a vaccine is protective if new cases are arising quickly (i.e. the disease has a high incidence rate) or if the sample size is very large. You can see this in the model I made below. It’s also why it’s much harder to develop new vaccines for diseases we’ve reduced massively: it would be a struggle to test new polio vaccines in rich countries that have eliminated the virus, for this reason.Here is a simple model I made to show how the disease incidence rate and sample size affect the length of a vaccine trial. In reality, the trials would have taken longer than this implies given their sample size, because participants weren’t all recruited simultaneously, some dropped out, and the trials set a more demanding bar – waiting for around 164 cases to rule out an efficacy below 30% rather than 69 cases to detect 50%. They also recruited a larger and more diverse population than needed for a simple average to see if the result would apply across multiple demographic groups. You can recreate this with my code on GitHub.Fortunately for trial statisticians1 – but unfortunately for everyone else – Covid was spreading quickly. During Pfizer’s trial, the monthly incidence rate (the % of the population infected for the first time, per month) was around 0.5%. By September 2020, using seroprevalence data, the CDC estimated that up to 23% of people had already been infected in the hardest-hit areas, and just below 10% across most of the US.2 In this situation, what matters most is how fast new cases are accumulating while a trial is running: during the autumn and winter of 2020, outbreaks were developing fast, and cases were racking up quickly in the placebo group, which made it easier to see a reduction in the vaccine group.