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Published on: Apr 04, 2020
2 minute read
By: Dr Rajan Choudhary

THE CHALLENGES FACED IN MAKING A VACCINE FOR COVID-19 — Part 2

Previously we’ve seen the difficulties researches face in trying to make a vaccine. But even if we make a vaccine, that’s just half the battle. Viruses are unique because they can mutate, and they can mutate to astonishing degrees. In humans mutations to tiny parts of our DNA can cause severe diseases or even death. In viruses mutations can change their structure, making them more infective and giving them a new coat. It gives them a survival advantage, the ability to evade our immune system and make our vaccines ineffective. This is why we need a new flu vaccine every year.

If it takes months to a year to develop a vaccine, it will be based off the virus found in December 2019. By this time the virus may have spread and mutated to such a degree that it is not effective. This does not mean all the effort was for nothing. Going through the steps and understanding the issues faced with making a COVID-19 vaccine can make the process quicker for subsequent vaccines against its mutated versions.

https://www.sciencealert.com/who-says-a-coronavirus-vaccine-is-18-months-away So Long to Develop a Vaccine

FAILURES FROM THE PAST

These issues were faced during the Ebola and Zika virus epidemics, and many large companies are understandably hesitant to develop vaccines for COVID-19. Ebola first broke out in 2014, and it was only in December 2019 that the first vaccine was approved for use by the European Commission and the United States. This is despite multiple large institutes in Canada and the UK working together to develop it.

13 different Ebola vaccine candidates had been identified soon after the outbreak, but none had been tested on humans. Unfortunately this is the most expensive part of development, and the area biopharmecuticals stand to loose the most money. Return on investments is also low, since epidemics usually take place in poorer countries, and the potential customers are unable to pay the high prices for these brand new treatments. It is an unfortunate realisation that research into medicines is driven by rich countries, for diseases that affect the rich.

https://newint.org/features/web-exclusive/2016/06/16/why-did-the-market-fail-to-produce-an-ebola-vaccine Ebola

WHERE ARE WE NOW?

This is all well and good, but what does it mean for COVID-19? We know we cannot rush a vaccine, because a poorly designed vaccine with unknown side effects can cause more harm than good, especially if given to children or the elderly. Currently the WHO are tracking 31 different attempts at making a COVID-19 vaccine, using different methods as discussed before. All of these are currently in the pre-clinical stage, focusing on isolating parts of the virus and creating a target the body will recognise and react to.

Researchers at the University of Queensland were one of the first to start using the genetic code of COVID-19, released openly by Chinese researchers. They have developed a test vaccine within 6 weeks using state-of-the-art genetic techniques, used for the first time. If animal models prove successful then human testing might begin within 6 months. In the pharmaceutical industry advancements this quick are almost unheard of.

Additionally over 293 clinical trials are taking place in China using existing drugs on the market. The advantage is we already know these drugs are safe to use in humans, but we are trying to work out if they will work against COVID-19. Some scientists are also looking at medications that were initially developed against SARS and MERS, but never completed because these outbreaks died down and the medication was no longer required.

https://www.aljazeera.com/news/2020/03/china-recovered-develop-effective-covid-19-treatments-200302082850237.html COVID-19 treatments

https://www.who.int/blueprint/priority-diseases/key-action/novel-coronavirus-landscape-ncov.pdf?ua=1 WHO news

https://www.theverge.com/2020/2/28/21156385/covid-coronavirus-vaccine-treatment-moderna-remdesivir-research COVID-19 treatment

Its not all doom and gloom. Challenges drive innovation, and we are already benefitting from this. New forms of genetic sequencing, new methods for extracting viral proteins, new techniques in creating a vaccine. These innovations are already benefitting us on the drive to create an effective vaccine for COVID-19, and they will benefit us in the future when the next pandemic hits.

Dr Rajan Choudhary, Product Manager Second Medic UK

www.secondmedic.com

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Machine Learning in Healthcare India: A New Era of Predictive and Personalized Care

Machine learning is driving one of the biggest transformations in Indian healthcare. Machine learning in healthcare India is improving diagnostics, predicting diseases early, and enabling personalized treatment plans based on large volumes of medical data. India’s enormous population, diverse health patterns, and rising burden of lifestyle diseases make ML an essential technology for improving care outcomes.

SecondMedic integrates machine learning across diagnostics, risk scoring, preventive care, and remote monitoring to create intelligent, data-driven healthcare experiences.

Why Machine Learning Is Crucial for India’s Healthcare

India faces major challenges: increasing chronic diseases, low doctor-to-patient ratio, and gaps in early diagnosis. Machine learning helps overcome these limitations through automated analysis and predictive insights.

ML supports:

Accurate disease prediction
Faster diagnosis
Personalized treatment
Proactive health management
Population-level insights

These benefits significantly improve care outcomes.

Machine Learning in Diagnostics

ML excels at interpreting complex medical data faster than traditional methods.

ML improves diagnostics by:

Identifying abnormal patterns
Analyzing imaging scans
Interpreting lab values
Comparing historical trends
Supporting clinical decisions

This reduces misdiagnosis and saves time.

Predictive Healthcare with Machine Learning

Predictive analytics is one of the most powerful ML applications.

ML predicts risks for:

Heart disease
Diabetes
Kidney disorders
Thyroid imbalances
Mental health issues
Respiratory disorders

SecondMedic provides predictive scoring for early detection.

Personalized Treatment Planning

Machine learning tailors treatment to individual needs.

ML personalizes care based on:

Age and genetics
Lifestyle patterns
Vitals and wearable data
Sleep and stress levels
Previous medical history

This ensures more accurate and effective treatment.

ML in Remote Patient Monitoring

With the rise of home healthcare, ML analyzes continuous vitals data.

ML monitors:

Heart rate
Blood oxygen
Blood sugar
Blood pressure
Sleep cycles

AI-generated alerts support timely intervention.

ML in Medical Imaging

ML enhances imaging interpretation by detecting subtle visual patterns.

Applications include:

Lung infections
Cancer markers
Cardiac abnormalities
Brain lesions
Kidney anomalies

This improves radiology accuracy and speed.

ML for Population Health in India

ML identifies health trends at a large scale, helping policymakers and hospitals plan resources.

ML provides:

Outbreak prediction
Disease burden patterns
Community health insights
Regional risk mapping

These tools help improve national healthcare planning.

Challenges in ML Healthcare Adoption

While ML is powerful, challenges include:

Data quality issues
Need for clinical validation
Privacy concerns
Infrastructure limitations
Need for skilled professionals

SecondMedic follows ethical ML standards and ensures secure data practices.

Future of Machine Learning in Indian Healthcare

Upcoming innovations include:

Deep learning diagnostics
Digital health twins
Fully AI-driven preventive dashboards
ML-based robotic treatments
Genomic ML predictions

SecondMedic is committed to building future-ready ML healthcare solutions.

Conclusion

Machine learning in healthcare India is transforming medical care through predictive analytics, personalized treatment, and early disease detection. SecondMedic uses machine learning across its digital ecosystem to deliver accurate, efficient, and patient-centered care.

To explore ML-powered healthcare tools, visit www.secondmedic.com

References

NITI Aayog – AI & ML in Indian Healthcare
WHO – Machine Learning in Clinical Practice
ICMR – India Chronic Disease Data
IMARC – AI & ML Healthcare India
FICCI – Emerging Health Technologies India

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