Pandemic, drug testing and Data Science

Funding a one-year masters

  • £812

    pledged of £11,693

    • 7


    • 0


Completion Date: Thu 30 Apr 2020

Hello! My name is Saurabh Kumar - an aspiring scientist and currently working full time as a System Engineer. We have a physical presence on Moon and Mars, but we are yet to find a cure for diseases like HIV, Alzheimer's, and Asthma. We are losing close to a million lives just because of AIDS every year. Of the total amount of medical data we have today, 4/5 is in an unstructured format. We can make the process of drug discovery faster and easier by exploring and structuring this data and save potentially millions of lives. The thought and inspiration for doing research in the field of medical data analysis lead me in the direction of pharmaceuticals development and marketing, especially vaccines.

Current scenario of drug development

Have you ever wondered?

• Why it takes so long to get medicine for a new disease?

• Why there are only a handful of companies making most of the vaccines?

• Almost 10 Million people die from cancer annually, Every sixth death in the world is due to cancer.

• There are close to 334 million people suffering from asthma.

• Why 2/3 out of approximately 37 million HIV positive people are from sub-Saharan Africa?

• Why only 1/3 of them have antiretroviral medicines the only known solution for HIV?

• Vaccines have saved more lives than any other invention.

• At least 10 million deaths were prevented between 2010 and 2015 thanks to vaccinations.

• We are 99% of the way to eradicating polio globally.

• Measles vaccination has saved an estimated 17.1 million lives since 2000.

• With the invention of the vaccine for cervical cancer, we can eliminate it in the next 20 years.

Then why we are not creating more solutions for other diseases?

• There are only 4 main pharmaceutical companies; GlaxoSmithKline, Merck, Pfizer, Sanofi. They produce most of the vaccines we have today. This includes vaccines for HPV, Polio, Measles, Shingles, hepatitis (A, B), Influenza, etc.

• The average cost of developing a new vaccine is between 1-2 billion dollars and the average time-to-market is close to 10 years.

• Anything we do to decrease the development time and investment increases competition and the potential for new vaccines.

Companies focus on markets to get a good return on their large investment. GlaxoSmithKline made more than 1 billion in 2018 by selling vaccine for Shingles, aided by the fact that it became mandatory to get vaccinated, for Shingles if you are over 50 years of age in the USA.

On the other hand, due to poor medical infrastructure in the Democratic Republic of Congo and a weak economy, by the time pharmaceutical organizations noticed the Ebola outbreak in 2014 and started working on it, more than 11000 people were dead.

There are organizations like Gavi funded and founded by Bill and Melinda Gates Foundation. Which is working on the development of cheap and experimental vaccines. They need help.

The traditional analytical and testing approaches we are using today are not good enough. Lack of modern analysis tools and isolation restrict the small level organizations and individual researchers who are on the ground where the problem is rising.

 They cannot test the product on a live specimen. They cannot carry forward their research or integrate it with others who are trying to solve the same problem elsewhere. The unavailability of the right analytics tool and model restricts them just to do the theoretical examination or wait for it to be a pandemic. Their only option is to wait and see if a large organization is ready to take the action because of a good return on investment.

There are advanced analytics tools designed by organizations like IBM, for example. IBM’s Watson is business-centered and works effectively at the organizational level. But not everyone can afford to use these products due to cost and technical abilities. There is a huge need for opensource analytical products that can be used at the academic and individual levels.

How Data Science and Artificial intelligence can help?

• Eighty percent of the total medical data we have is unstructured. With the help of artificial intelligence, we can understand this unstructured data. That will give us a completely new understanding of the diseases we have and how we can find a cure or vaccine rapidly.

• With the help of Artificial intelligence, we can understand the similarity between several patients. This will help researchers to create dynamic patient cohorts. And will be much easier to take the right path to treat the same kind of patients.

• By the end of 2020, it is expected that medical data will double after every 73 days, With the help of A.I. We can manage this overwhelming amount of health data and use it to find the best medical path for each patient.

• There is no place of human error while scanning the medical records, we as a human are prone to fatigue, distractions, and change in moods. These all can lead to error and we can miss the important point. On the other hand, this is not the case with computers.

• It is a very challenging and very tedious task to get the right candidate for the testing of a product. The biological data we are creating at an unprecedented rate. With Data Science and Artificial Intelligence, we can create data models for the selection of the right candidates. We need a fast analysis of data and predictive analytics to improve our decision-making process while developing and testing a product.

• In the future, we will be able to simulate the entire human biological model, that will revolutionize the process of drug discovery. We will be able to reduce the time of drug discovery from years to days or hours. And all this can be possible with the mode advancement in the field of Medical and Data science parallelly.

• The possibilities are endless, countless times we thought about something it is impossible, yet we made it possible.

My current Goal

Due to a full-time high demanding job and lack of the research environment, and the complex nature of research, I couldn’t achieve much so far in this field. But a full-time program in my desired field will be a dream come true for me, I will able to focus most of my time doing the research. At the beginning of this year, I made a successful application to Bournemouth University, Bournemouth, to read for an MSc in Data Science and Artificial intelligence. This is a program which explores in more depth the areas of research that I found fascinating during my undergraduate degree in computer and science and engineering. This program will help me to solely focus on developing and researching faster and cheaper data analysis models for biomedical data. That will help to reduce the time of drug analysis and will be helpful for individual researchers having a limited number of resources.

I have been able to save a significant portion of my needed funding. As I have secured Academic scholarship funding of £3500. I have tried my very best to secure funds through my own means as in addition to my full-time job. I have been undertaking various project roles in the evenings and weekends but still have not achieved the required amount. I need only £11693 to complete this research and degree.

Here is the break-down of costs:

• UK Student Visa (Tier 4) maintenance funds = £9135

• Travel + Visa application + Medical examination = £1865

• Total Funding Target = £9135 + £1865 + £693(processing fee) = £11693

Tuition fees - £14500 (Which I will be covering using my savings and Academic Excellence Scholarship.)

Ultimately, my goal is to follow up the masters with a Ph.D., in the field of Data Science and Artificial intelligence. Only If I will be able to get the required funds that will allow me to take admission in this program. I am so excited by the prospect of doing this degree, and if enough will give a little something, I might be able to. All investments, even the smallest ones, will be impactful and hugely appreciated.


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and you will be invited to join my *exclusive* masters blog, which I'll run for the duration of my degree. I'll be publishing easily digestible summaries of all the essays I write (there should be between one and two per week), discussions about ideas I am introduced to, debates I have or witness, ideas I pick up from lectures, thought-provoking quotes I find, and anything else I think you'll find interesting, educational or entertaining. The blog will not be searchable via Google, and will only be available to donors.

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