How to size a market for any type of vaccine, anywhere in the world
Earlier this year, the BBC published a news story announcing that the World Health Organization (WHO) was planning to run pilots of the world’s first Malaria vaccine in 2018, initially targeting 750,000 young children in Ghana, Kenya and Malawi. Let’s imagine for a moment that the vaccine was being introduced, not as a pilot, but wholly within these three countries. You might want to know how many children would receive it, how many would be protected from the disease, and what the uptake of the vaccine might look like over the next 10, 20, 30 years.
How long would it take to pull all of this data and extract the relevant information? Two days? Two weeks? Two months, even? Not even close. The answer is about two minutes. I know this to be true because I just did it myself.
Here are some of my answers: Assuming a lag time of four years, by 2022, the target population (aged 0-1) across the three countries would be 6.29 million. By that time, we would expect 5.43 million children to have been vaccinated, and of those, 2.17 million would be immune to Malaria (assuming the 40% efficacy rate cited in the BBC story). I also have the corresponding numbers for each year up to 2040. If I really wanted to, I could run the same query for a different country – any country in the world, that is – or for the entire African continent.
While this might sound like a lot of sorcery, it’s actually the work of an exciting new interactive product called the Vaccines Epidemiology Dashboard. Using powerful macros to knit together population data and historical patterns of vaccine coverage, the new offering allows users to size potential patient populations over time for any country in the world.
To generate a forecast, you simply fill in the parameters. First, define your target population (gender, age range) and the region (countries). If there is no historical vaccines data for your market, the algorithm will pick a proxy country on which to base your projection (that is similar in GDP), and apply the uptake curve to the population for your chosen country. If you are forecasting for a new vaccine, you will need to nominate a comparator vaccine with similar characteristics. For the Malaria example, I used Yellow Fever. After that, you just need to enter the target coverage (%), year of introduction, expected lag time to target coverage, vaccine efficacy, and market penetration (your expected share). Then you pull the trigger.
What you get back is a line graph depicting the sizes of four sub-populations over time, up to 2040: one curve for your selected population, another for your vaccinated population (a function of coverage), a third for your immune population (a function of efficacy) and finally one more for your potential market size (a function of market penetration/share.) You also get the tabulated versions of the numbers, of course.
The data gives companies a better overall understanding of the vaccines landscape and model launch scenarios. It also allows them to gather competitive intelligence by comparing assets with comparator vaccines. Furthermore, it equips them to make assumptions about the market penetration of vaccines in development and to forecast the annualized market size for their assets in any country.
It is so easy and quick to use that it almost feels like cheating. It should certainly save manufacturers a lot of time and effort, particularly those companies with products in early stage development.
For more information on Vaccines Epidemiology Dashboard, click here.