How data analytics supported the development and distribution of COVID-19 vaccines
COVID-19 is the biggest public health crisis the world has seen since the Spanish Flu, which infected one-third of the world’s population in the aftermath of World War I, and killed more than 50 million people.
Even now, more than 18 months after the first COVID-19 patient was detected, governments, hospitals and other healthcare institutes are still struggling to contain the spread of the pandemic.
Fortunately for us, there is one distinctive difference between the Spanish Flu and COVID-19—the pace at which pharmaceutical companies have been able to develop vaccines.
Another contributing factor to the success and speed of the COVID-19 vaccine development was data analytics in healthcare.
Thanks to these advancements, which have reduced the typical vaccine development timeline of 10 years, pharmaceutical companies have been able to explore, develop, trial and receive approval for multiple vaccines within two years since the outbreak of the virus.
It has also helped government authorities in developing and implementing successful vaccine rollout plans to inoculate as many people as possible.
In this post, we take a deeper dive into the role data analytics played in the development, manufacturing and distribution of COVID-19 vaccines.
Data analytics improved the efficiency of preclinical and clinical experiments
The development of a vaccine for any disease requires pharmaceutical companies to conduct rigorous experiments and clinical trials involving a large number of participants, to prove the efficacy of a vaccine against a virus.
The typical vaccine development process takes a minimum of 10 years before the vaccine gets approved by regulatory authorities in charge of widespread manufacturing and distribution.
The need of the hour, however, forced the pharmaceutical industry to adopt novel techniques in both analysing the data at hand and developing vaccines and experiments to speed up the development process.
One such data analytic tool used in the development of the COVID-19 vaccine is Design Of Experiments (DOE), which improved the efficiency of preclinical and clinical experiments while reducing the number of experiments otherwise required.
DOE ultimately helped companies design and approach experiments systematically, allowing them to identify and determine the effects various factors had on the outcome of clinical experiments.
Predictive technology helped companies scale vaccine production up
The success of vaccine development also relies on how fast pharmaceutical companies can manufacture the doses required to meet demand. Scaling the manufacturing process up to meet the needs of billions of people in a very short timeframe, however, is entirely unprecedented.
Fortunately, data analytics tools like predictive analytics and multivariate analytics helped companies not only predict demand to scale production up but also to reduce the number of batches needed to prove the efficacy of the vaccine.
Data analytics supported the management of the fluctuations in vaccination supply
Vaccine distribution is perhaps the area where data analytics is most traditionally used. This time around, data science was used to predict and handle fluctuations in vaccine supply due to various political, economical and logistical factors.
Government authorities planned their vaccine rollout strategy to prioritise the most vulnerable locations and population segments. Many countries, for example, prioritised vaccinating everyone over the age of 65 as that segment of the population was deemed the most vulnerable to the virus.
Governments also used data analytics tools to detect virus transmission patterns to predict possible outbreaks and prevent them by vaccinating the people living around that geographical area.
COVID-19 vaccinations and beyond; data analytics will continue to shape public healthcare
COVID-19 is likely to be the most impactful healthcare crisis of our lives. Fortunately, things may return to the new normal sooner than we anticipate with the introduction of life-saving vaccines.
The development of these vaccines involved the adoption of many novel techniques including new vaccine production methods and data analytics to support this.
It also helped pharmaceutical companies and governments in developing, manufacturing and distributing these vaccines to protect the health of millions of people around the world.
As such, data analytics will continue to be a force for good as it helps us navigate the uncertainties of public health crises—today and in the future.