Insights from the School of Science and Engineering
Prior to 2020, finding a vaccine for a new disease took years to develop. And that’s on the fast track. But all this has changed in the age of COVID-19. Data analytics and vaccine development have met their moment.
“Ushering a vaccine through rigorous testing protocols and regulatory approvals is not an easy (or quick) effort,” writes Tiffany McLeod in a Sartorius blog post, “but incorporating advanced data analytics could help accelerate the process.”
From diagnostics and disease prediction to population health management, precision medicine, and more, data analytics is revolutionizing healthcare. But as a pandemic sweeps across the globe, it is vaccine development, manufacturing, and distribution that highlights the vital influence of data analytics in the industry.
COVID-19 is a novel coronavirus, not seen previously in humans. This makes diagnosis and treatment particularly challenging. Developing an effective vaccine is especially difficult.
Researchers must essentially start from scratch. They have little to no prior data on the nature of how the disease presents in humans or what treatments will elicit an effective immune response. A novel disease introduces novel challenges. Applying data analytics to this challenge saves lives.
Let’s take a brief look.
Data Analytics and Vaccine Development
There are six stages of vaccine development. The Center for Disease Control lists them as:
- Clinical development
- Regulatory review
- Quality Control
At each stage, data analytics informs decision-making and drives progress.
The preclinical stages involve researching how the disease works, finding suitable vaccine candidates, and initially testing its potential immune response.
Throughout the exploratory and preclinical process, vast amounts of data are collected across numerous, diverse datasets. Data mining, machine learning, and artificial intelligence accelerate trend recognition by orders of magnitude.
These trends lead to avenues of investigation that once took years to develop. Even when a possible vaccine is a blind alley, data analysis allows research to iterate quickly, getting viable candidates to clinical trials much faster.
Clinical development is the linchpin, consisting of three phases.
Phase 1 – small-scale trials:
The main focus of this phase is safety. Prior to the start of clinical trials, exhaustive data analysis indicates that a potential vaccine is both safe and shows an acceptable likelihood of effectiveness. These trials generally involve between 20 to 100 participants. According to the FDA, trial volunteers are generally healthy and free of disease. Researchers look for “adverse reactions to increasing doses” and early indication of whether the drug induces an immune response.
According to Dr. Justin Lessler, associate professor of epidemiology at Johns Hopkins Bloomberg School of Public Health, if “the vaccine appears safe and the people who get it mount a detectable immune response,” trials then move to phase 2.
Phase 2 – expanded trials:
With an absence of safety concerns from phase 1, trials then expand to hundreds of people. The intent is establishing “proof of concept,” in a more diverse trial population ranging in demographics and health status. This phase is typically broken down into two “sub-phases.” The first to further assess dosing requirements, the second to determine the efficacy of the immune response at varying doses.
Phase 3 – large-scale trials:
Involving thousands of participants, this phase aims to generate critical data on the vaccine’s effectiveness and safety, including all side effects. These trials are carried out across a wide demographic. The immune response is further tested under natural disease conditions and compared with a control group receiving a placebo. If the vaccine maintains its effectiveness and safety throughout this phase, it can be submitted for regulatory approval.
Once ready for manufacture and distribution, data mining and analysis track immune response and side effects throughout the entire population. Monitoring safety and effectiveness with real-world data maintain the iterative process in response to changing conditions.
At An Inflection Point
In a recent press release, Nino Giguashvili, Senior Research Analyst at IDC Health Insights Europe, says: “The COVID-19 pandemic has signified the value of real-world data to an unprecedented extent.”
In that same press release, Dr. Mark Lambrecht says that “Clinical development is at an inflection point with accelerated development for new therapeutics and vaccines for COVID-19.” Dr. Lambrecht is Global Director of the Health and Life Science Practice at SAS Life Science Analytics Framework.
Giguashvili and Lambrecht encapsulate the hope and influence of data analytics on human health.
Grasping the full promise of data analytics and vaccine development, now and in the future, requires more trained, dedicated professionals in both healthcare and data science.
Master of Science in Healthcare Analytics
The program is designed in collaboration with an industry advisory council ensuring that graduates emerge with the analytical training the healthcare industry needs most.
The MSHA program is built around 6 core competency courses, 2 industry immersion courses, and a capstone project. The capstone includes real-world experience with students working as data consultants for local healthcare providers using live data.
Topics of study include:
- Foundations of data management
- Foundations of statistical analysis
- Data visualization
- US healthcare ecosystem
- Population healthcare analytics
- Prescriptive and predictive analytics
- Data mining
Prepare yourself to meet the challenges and match the promise of data analytics with a Master of Science in Healthcare Analytics from Merrimack College.