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How big data helps in vaccine development

Coronavirus is the biggest public health crisis the world has seen since the Spanish Flu, which infected one-third of the world population in the aftermath of World War I, and killed more than 50 million people.

Moreover, more than 18 months after the first Coronavirus patient was detected, hospitals, governments, and some other healthcare institutes are still struggling to contain the spread of the pandemic.

Moreover, for us, there is one distinctive difference between the Coronavirus and Spanish Flu – the pace at which pharmaceutical companies have been able to develop vaccines.

Another contributing factor to the speed and success of the Coronavirus vaccine development was data analytics in healthcare.

Thanks to these latest advancements, which have reduced the typical vaccine development timeline of around ten years, pharmaceutical companies have been able to explore, trial, develop and receive approval for multiple vaccines within two years since the outbreak of the Coronavirus.

It has also helped the authorities of the government in developing and implementing successful vaccine rollout plans to inoculate as many as possible.

In this article, we take a deeper dive into the role data analytics played in the role of development, manufacturing, and distribution of the Coronavirus 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 wide range of participants to prove the efficacy of a vaccine against a Coronavirus.

The typical vaccine development process takes a minimum of ten years before the vaccine gets approved by regulatory authorities in charge of widespread distributions and manufacturing.

Furthermore, the need for an hour forced the pharmaceutical industry to adopt novel techniques in both analyzing the data at hand and developing vaccines and experiments to speed up the process of development.

One such data analytic tool used in the process of development of the Coronavirus vaccine is the design of experiments (DOE), which helps to improve the efficiency of the clinical and preclinical experiments while reducing the number of experiments otherwise being required.

DOE helped the companies to design and approach experiments systematically, which allows them to identify and determine the effects several factors had on the outcome of the clinical experiments.

Predictive technology helped companies scale vaccine production up

The success of the development of vaccines also relies on how fast pharmaceutical companies can manufacture the doses which are being required to meet the demand. Expanding and scaling the manufacturing process up to meet the needs of billions of people in a very short time period thus is entirely unprecedented.

Moreover, data analytics tools like multivariate analytics and predictive analytics helped the companies to not only predict the demand to scale production up but also to reduce the number of batches that are needed to prove the efficacy of the vaccine.

Data analytics supported the management of the fluctuations in vaccination supply.

The distribution of vaccines 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 the various political, logistical, and economic factors.

Government authorities planned their vaccine rollout strategy to prioritize the most vulnerable locations and population segments. Many countries, for instance, prioritized 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 to detect the virus transmission patterns to predict the possible outbreaks and prevent them by vaccinating the people living around that geographical area.

Coronavirus vaccinations and beyond, data analytics will continue to shape up public healthcare.

Coronavirus is likely to be the most impactful healthcare crisis of our lives. Moreover, things may return to the new normal sooner than we used to anticipate with the introduction of life-saving vaccines.

The development of these vaccines involved the adoption of several techniques, which include new vaccine production methods and data analytics to support this.

It also helped the pharmaceutical government and companies in developing, distributing, and manufacturing these vaccines to protect the health of millions of people across the globe.

As such, data analytics will even continue to be a force for good as it helps us to navigate the uncertainties of the public health crisis – today and in the future.

Srikanth
Passionate Tech Blogger on Emerging Technologies, which brings revolutionary changes to the People life.., Interested to explore latest Gadgets, Saas Programs

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