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Sunday, September 27, 2020

The market for Covid-19 vaccines and the tipping point to herd immunity

by Ajay Shah.

Many firms are developing Covid-19 vaccines. Enormous resources have to be deployed, up front, to develop a vaccine and to build manufacturing capacity. It is likely that many vaccines will get through to approval in mature regulatory regimes. Not all vaccines will work identically for all situations, e.g. some vaccines may work better for an elderly person than others.

It is commonly assumed that the global market size for a Covid-19 vaccine is about 6 billion people. In this article, we argue that this might not be the case. Let's think about the situation in the market once one or more vaccine reaches the market.

The buyers perspective before vaccine sales have commenced

The private gain for an individual from buying a vaccine are shaped by the probability of getting sick when leading an unconstrained life. This is shaped by the extent to which Covid-19 has burned through the communities that the person plans to engage with. As an example, in the slums of Bombay or Delhi, herd immunity has set in. A person living there knows that few people in her circles are now getting sick, and she feels relatively safe. Well known factors such as age and co-morbidities will also shape the threat perception of each person. Therefore, for her, the gains from a vaccine are relatively modest, and the willingness to pay is small.

In each city of the world, there is a different numerical value for the attack rate (the fraction of people who are infectious) and the extent of immunity. The state of the epidemic in Pune is different from that in Bombay. As time passes, each city is inching towards herd immunity, and the passage of time thus diminishes interest in paying for a vaccine. Vaccine IP and manufacturing facilities are wasting assets.

It it were possible to develop a combination of tests that add up to an `immunity passport', then the price of this test and the odds of coming out positive would shape the demand function for the vaccine.

Progress on immunisation and herd immunity

Into this world, let us imagine that the sale of multiple vaccines commences. At first, there would be a rush of demand and high prices. As immunisation progresses, the attack rate would go down and the gains from buying the vaccine would further go down. In places like Bombay and Delhi, where a considerable proportion of the population has already been exposed to the disease, when a modest fraction of the population is vaccinated, this could tip the population over into herd immunity, and the disease could die down.

In such a world, vaccine makers face the prospect of a short hot market. At first, vaccine demand will be high and the factories will not be able to keep pace. Competition will come about and that will exert pressure on prices. In a city like Bombay, with about 20 million people, after (say) 5 million persons buy the vaccine, this may significantly change the threat perception in the eyes of the average individual. Vaccine demand would then decline.

Under such numerical values, the market potential in Bombay is not roughly \$50 $\times$ 20 million people or \$1 billion, but perhaps more like \$25 $\times$ 5 million people or about \$125 million.

All of this reduced revenue potential will go to the first few firms that get 5 million doses into the Bombay market. Competition would exert downward pressure on the price, demand would tail off as herd immunity sets in, and there would be a price crash. The late comers would flood the market with output but would obtain low revenues in return.

The vaccine demand collapse in a simple model and in the real world

We have always known that a vaccine is not just a private good; there is a positive externality. The novel idea of this article is about tipping points.

Consider a simple model in which herd immunity is achieved at 60%. Suppose 50% of the population is already immune and knows it. The first 10% that gets the vaccine tip the system over to $R_0<1$ and then the fires start dying out. Once the fires start dying out, the attack rate goes down, the threat perception changes, and the incentive for private people to buy the vaccine drops a lot. Under these conditions, the positive externality imposed by vaccine purchase by the early vaccine buyers, upon the overall system, is particularly large.

A key factor that drives behaviour in this model is that when a person is immune, she knows it and then has no incentive to buy a vaccine. In the real world, people don't know whether they are immune, and would be more inclined to buy a vaccine just to be safe. In the limit, the veil of ignorance is complete, nobody is able to assess the threat, and everyone wants to buy a vaccine.

In the real world, the veil of ignorance is not complete. At every place, people do have a personal judgement about the threat level based on the extent to which their friends and family are getting sick (or not) per month. Age and co-morbidities will also shape vaccine demand. As a general principle, it is always wise to think that humans are sentient optimising creatures. Individuals have a noisy estimator of the threat that they face and this will shape their willingness to pay for a vaccine.

Wall street tells Main street what to do

These problems feed into the thought process of private firms and shape the commitments of capital to the problems of vaccine development and manufacturing when faced with a novel epidemic. 

Numerous vaccines are under development. The process of vaccine approval is necessarily slow. At present, we generally think that over time, one by one, many of these vaccines will get through to the market. By the reasoning of this article, the first few will get through, within a few months the market will collapse, and all funding will be yanked for other projects. This will be a bit reminiscent of how funding for vaccines against Sars-Cov-1 was abruptly yanked when the funders realised that Sars-Cov-1 had reached $R_0<1$.

The numerical values used here (e.g. 60% for herd immunity, 5 million immunised in Bombay to tip over into herd immunity, $50, etc.) are of course purely illustrative. To translate these ideas into practical calculations requires data on the extent to which immunity has come about. In many places worldwide, there are good estimates of the persons who have antibodies, but there is more to immunity than measured antibodies. In India, the information available about the state of the disease in (say) Bombay is rather poor.

If we take this dynamics of the vaccine market seriously, vaccine makers have an incentive to create such datasets. Alongside the construction of such datasets, there is a need for derivatives trading on underlyings such as the fraction of Bombay residents who have antibodies.

The argument of this article is a special case of the long-standing problems of incentives for vaccine development. An effective pathway for state intervention, and philanthropic capital, lies in offering contracts for R&D and manufacturing which change the incentives of private persons to engage in these activities.


To the extent that this reasoning is correct, individuals will at first face a vaccine market with high prices and shortages. For many individuals, particularly for low-risk persons, there is a tradeoff between paying more to get the vaccine early versus paying less to get it late or even to not get vaccinated if the pandemic has subsided.

For firms with a vaccine under development, this article paints a winner-takes-all scenario, where the first few vendors who get output on scale will capture all the revenue. To the extent that this reasoning is correct, plodding along to the finish line late will induce low revenues.

For policy makers and philanthropic capital, it is important to avoid a `coronavirus winter', a collapse in coronavirus research of the kind which happened after the SARS epidemic achieved $R_0<1$. There is enormous knowledge, and capable teams, which has been created by the early gold rush of building vaccines against SARS-Cov-2. This knowledge should not be lost. As an example, it would be nice if research groups will publish research papers and release code before they put out the lights. We need to think of the sustainable frameworks, where we achieve a new normal of high R&D into pathogens that can trigger pandemics.


  1. I believe Tweet of Adar Poonawala about whether Govt. of India has 80000 crore for vaccine was actually his expression of fear of demand collapse. He wants to get a forward contract from govt to avoid the possible market uncertainty you have mentioned. I guess even the argument that at the start vaccine demand will have high prices and shortages depends on when and how many safe and effective vaccines arrive. Thank you for this insightful article.
    I write this Marathi blog ( I will be posting link to this blog in the blog about vaccines I am planning to write.

  2. excellent article! - gives directions/pointers for many things -
    - how much production'd be allowed to prevent over-capacity/supply-glut/drop in prices - is a classic question posing relevant industries/regulatory bodies - drawing parallels from one Dermatology Drugs market I studied - by FY22 - there are 65 molecules in the commercial-launch phase for Skin Cancer - when 85% of the Skin Cancer market is already captured by the FDA approved current molecules! - that 15% market ~$2 billion - is chased by 65 molecules - gives clear idea of miniscule ROI% potential and adverse Cost-Benefits ratio - akin to the scenario posited in the vaccines market!

    - as rightly pointed by you - the vaccines market indeed a play on risk-perception - and ~50% of Americans said won't take vaccine - in case of Indians and factors stated by you in decision-making to immunize self - does not justify simple back-of-the-envelope calculations for the market - likely closest calculation - to vaccinate people who have to/are forced to work/in contact with outsiders - age-based cohort of 15-60 years - after eliminating recovered people from the cohort!

    - then directs to formation of datasets of such age-based cohorts - huge exercise akin to the Census - but the necessary evil - although such datasets exist in bits-and-parts with many private companies/marketers and state and central govts./MoHFW and such - pointer to aggregate and form such datasets for policy makers - not for vaccine market estimation (because this is a man/lab-manufactured pandemic) - more beneficially perhaps by further segregating income-wise, profession-wise and used for manifold purposes - vote banks ;) , marketers of financial products/services,
    other such govt., for-profit, not-for-profit organizations!


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