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Published on: Jul 07, 2020
1 minute read
By: Dr Rajan Choudhary

Asymptomatic Carriers Of COVID

Why Asymptomatic carriers of COVID are as dangerous as symptomatic patients

Many countries have now begun enforcing masks for everyone to wear when out in public, in enclosed spaces, or on public transport. There is good evidence to support these measures, but unfortunately, some still show resistance to the idea. Whilst some reasons put forward are non-sensical (masks do not cause any noticeable or measurable decrease in oxygen delivery to the body), other people feel like they shouldn’t wear a mask because they do not have symptoms of COVID. And thus would not spread anything. Here we look at a recent publication in Nature, one of the most esteemed peer-reviewed scientific journals in the world, and what it shows us about the transmission of COVID in a population.

This study by Lavezzo et al looked at the suppression of the SARS-CoV-2 outbreak in the Italian municipality of Vo’. This municipality is 50 kilometers west of Venice with a population of just 3,416 people. It experienced its first casualty of COVID on the 21st of February, after which it underwent lockdown for 14 days. During this time data was collected on the clinical presentation and hospitalization of COVID patients, as well as other citizens through the contact tracing network.

In total 2,812 residents were tested in late February, with another 2,343 tests performed two weeks later at the end of lockdown. Nasal and throat swabs were taken to identify infected individuals. The study found only 2.6% of people were positive for COVID at the start of lockdown, which reduced to 1.2% two weeks later.

Out of this infected population, 42.5% were asymptomatic. They had no symptoms at the time of swab testing nor did they develop symptoms afterwards. When the viral load was measured between symptomatic and asymptomatic patients, there was little difference.

Evidence has pointed to a large asymptomatic population, and previous studies have shown it to be as high as 50% in some cases. Many would assume if a patient has no symptoms then they must have fought the infection before it became an issue, or had a very low viral load and therefore were not likely to spread it. This study shows it is simply not true. Asymptomatic carriers of COVID are likely to contribute to the transmission and spread of COVID amongst the population.

If you are asymptomatic you have as high a viral load as someone who is symptomatic. But there is no way to tell without a swab. You have as much of a potential to spread the infection to the vulnerable. Even in people who became symptomatic the height of transmission was found to be before the patient showed any symptoms.

So what does this mean? If you have COVID you are likely to spread it before you show any symptoms or any symptoms at all. We know masks are effective at significantly reducing the spread of COVID, by catching the water droplets that carry the SARS-CoV-2 Virus. Now that shops and public places are re-opening, the risk of spread is high.

Wear a mask. Stop the spread.

https://www.nature.com/articles/s41586-020-2488-1_reference.pdf suppression of a SARS article

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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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