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

Anyone With Flu Like Symptoms Are Now Encouraged

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The Bold Strategy the UK has adopted against COVID-19

The UK government had outlined its Coronavirus strategy in three distinct steps. The first stage was to contain the virus. This was implemented when spread of the virus was primarily by infected patients from abroad. Public health advice was provided, campaigns on washing hands, not touching the face, practicing safe hygiene. Potentially infected individuals were informed to self-quarantine for 14 days whilst waiting for symptoms to develop and testing to occur. Now it is evident this is not enough. The virus has spread to enough people that transmission can now occur locally, between people who have never been abroad. Containment is not the aim, mitigation of number of cases to prevent burdening the health services. The second stage aims to reduce the epidemic’s peak, flatten it out so the number of cases do not occur at once.

Anyone with flu like symptoms are now encouraged to stay home for 7 days, and testing will only occur for hospital admissions. Beyond this little appears to have changed. Schools will remain open, social gatherings have not been cut yet (but is expected over the next week) and general life will continue as normal. This is a risky measure, one that suggests the government is not taking the issue seriously. France, Spain and Italy have enacted lockdown measures, Germany has begun cutting social gatherings. So why is the UK not following in step? Are people going to die as a result of this inaction?

DELAY THE SPREAD

The answer is of course complex. PM Boris Johnson has acknowledged that as a result of his decision people may die, especially the elderly who are seen as a very vulnerable population. But this plan has been discussed with multiple scientists, doctors, public health specialists, and there is method in the madness. Currently the UK is in the early stages of the epidemic. The number of infected are expected to rise sharply in 4 weeks, with a peak in 10–14 weeks. Implementing harsh restrictions too early can lead to “self isolation fatigue”, resulting in people not following the restrictions stringently or leaving their homes at the height of the epidemic. Restrictions also come with their own problems, and implementing them may lead to more harm than good. Simple measures such as hand washing and self isolation can itself reduce the peak of cases by 20%.

Schools have not yet been closed because COVID-19 does not appear to affect children as much. Closure of schools would also mean parents having to stay at home to look after their children (after all, nurseries, creches and other forms of childcare would still result in a spread of infection). In some cases these parents are also healthcare professionals, and the UK needs every single doctor, nurse and allied health professional to be on the frontline treating patients.

So what is the strategy? The NHS is currently full of patients due to the winter burden, one that is expected to taper off in the coming months. Slowing the onset of the epidemic’s peak to Summer, spreading it across the next few months so the maximum number of people can be treated in the hospital setting without overburdening. The aim is no longer to prevent the spread of infection but to protect the most vulnerable age groups. This model allows the young and healthy to become infected, almost encourages it.

INFECT YOUR CITIZENS

Now this sentence may sound ridiculous when said out loud. Allow infection. But why? The UK has acknowledged there is no way to stop the infection. Whilst the mortality in the elderly population and those with medical conditions are high, in the young and healthy it manifests as a mild illness with almost all infected cases recovering. More importantly, recovered patients have immunity against the virus, manifesting as antibodies in their blood. Immune patients cannot infect other people, so the more immune patients there are the slower the virus will spread. This is known as herd immunity, and the process is discussed in length in our blog on vaccines. Herd immunity needs to be achieved before the onset of winter in 2020, as winter admissions alongside COVID admissions would result in a disaster.

https://medium.com/@rajneesh.dwivedi/developing-a-vaccine-for-covid-19-part-1-f7263ae9bf88 Rajneesh secondmedic

The strategy is of course risky, and one that has not been implemented before. And since the infection will not be contained people will die. But by spreading the burden of the infection across a longer time period will allow those who require treatment to receive it in a far less burdened health system. And it has received support from health professionals, even those who are fierce critics of the PM and Conservative government. But it has resulted in confusion amongst the population, a population that looks at authoritative action taking place in other countries and not understanding why their own doesn’t follow suit. The issue is these draconian measures are not sustainable, and if implemented correctly the UK strategy may result in far less lasting damage on its health service and the economy.

Dr Rajan Choudhary, London UK

Head Of Products, Second Medic Inc (www.secondmedic.com)

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

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

  1. NITI Aayog – AI & ML in Indian Healthcare

  2. WHO – Machine Learning in Clinical Practice

  3. ICMR – India Chronic Disease Data

  4. IMARC – AI & ML Healthcare India

  5. FICCI – Emerging Health Technologies India

See all

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