Fighting Future COVID-19s

The Spanish Flu of 1918 was an unusually deadly influenza pandemic, infecting 27% of the then world population and killing 40 – 50 million people. It’s been common knowledge in the global health community that another pandemic ‘Disease X’ whose speed and severity could match that of the Spanish Flu is a matter not of if but of when.

Welcome to 2020, the year of Covid-19. Its efficient transmission between humans and case fatality rate of around 1% (higher in elderly) has made the world come to a standstill. As I write this, there are 93000+ confirmed cases and 3100+ deaths in 60+ countries amidst a massive global effort to contain the spread of this potentially pandemic zoonotic coronavirus.

Learn how to protect yourself against Covid-19 here. Live track Covid-19 spread here.

While you are reading this at home (hopefully trying to avoid travel & public spaces), let’s take a step back and look at the bigger picture of pandemics and the global efforts to deal with it.

What are Pandemics?

The WHO defines pandemic as the worldwide spread of a new disease. Our growing population, encroachment into wildlife habitats and globalization fuelled trade & travel has led to an era when the threats posed by global pandemics and epidemics are accelerating exponentially.

The Middle East Respiratory Syndrome (MERS) coronavirus originated from the camels of Saudi Arabia in 2012 and spread halfway across the world to South Korea in 2015. Severe Acute Respiratory Syndrome (SARS) emerged in China and went on to infect people in over 30 countries in 2003. Add the Ebola and Zika stories to this, and you know how ill prepared we are to predict when, where, or from what species the next emerging virus will break out. Yet our knowledge of viruses is limited to only 260 out of the estimated 700,000 viruses which can possibly infect humans.

Novel viruses usually jump from mammals/birds to humans in regions where dense human populations and biodiversity intersect. Limited laboratory facilities, surveillance and healthcare infrastructure at such places delay early detection and subsequent control efforts. This jumping of a pathogen from a reservoir species to a new species is called Zoonotic Spillover. For example, Ebola virus jumped from bats to humans. It is a poorly understood phenomenon specifically because there are just too many factors influencing it. The idea of building models to predict a zoonotic spillover would give nightmares to even Shri O.P. Tandon, an Organic Chemistry author, whose books have given nightmares to millions of unsuspecting entrance exam aspirants.

Lets breakdown a Zoonotic Spillover event:

First, the amount of pathogen available to the human host at a given point in space and time, known as the pathogen pressure, is determined by interactions among reservoir host distribution, pathogen prevalence and pathogen release from the reservoir host, followed by pathogen survival, development and dissemination outside of the reservoir hosts.

Pathogen Pressure

Second, human and vector behavior determine pathogen exposure; specifically, the likelihood, route and dose of exposure.

Identifying key bottlenecks between barriers will help predict and intervene during a Zoonotic Spillover event.

Third, genetic, physiological and immunological attributes of the recipient human host, together with the dose and route of exposure, affect the probability and severity of infection.

A Zoonotic Spillover is a rare event.

Although we are continually exposed to many potentially infectious pathogens that are derived from other species, barriers at each phase make infection and subsequent disease a rare event. Understanding how these barriers are functionally and quantitatively linked, and how they interact in space and time, will substantially improve our ability to predict or prevent spillover events.

The Global Virome Project

In 2016, our approach to pandemics changed from reacting to outbreaks to proactively preparing for them with the Bellagio Initiative on the Global Virome Project (GVP). Based on USAID’s PREDICT program, which has discovered hundreds of known and unknown viruses in over 30 countries, the Global Virome Project (GVP) is a groundbreaking  global partnership to develop a comprehensive ecologic and genetic database of virtually all naturally-occurring viruses in 10 years. It aims to be the beginning of the end of the Pandemic Era.

If you know the enemy and know yourself, you need not fear the result of a hundred battles.

Sun Tzu

How will this ‘VIRAL BIG DATA’ help us deal with the next pandemic?

The availability and access to human genetic data has revolutionized how we see and treat cancer today. It has pushed cancer from the era of chemotherapy to that of personalized medicine. The Global Virome Project is based on the same principle, and hopes to use this data to predict things like the Top 10 emerging viruses, Zoonotic Spillover hotbeds, Virus migration patterns, Risk mitigation interventions and Drugs/Vaccines discovery strategies. The project will use artificial intelligence across the largest viral data set ever assembled, similar to machine learning techniques that are used in genomics to identify gene function, expression and disease biomarkers. It will also build capacity to further strengthen the global surveillance network.

Influenza pandemics are estimated to cause an average of US$ 570 billion in economic damages per year to the global economy and these costs will rise as our economies expand and become more interconnected. The Global Virome Project will cost US$ 1.2 billion, which is less than 0.2% of this estimated loss. In the late 1980s, the Human Genome Project catalyzed the development of new technologies and ushered in the era of personalized genomics. It is estimated that every U.S. federal dollar put into the Human Genome Project resulted in a $178 return on investment. The Global Virome Project is also expected to go beyond the immediate goal of tackling novel viruses to yield a treasure of publicly accessible unbiased data for advancements in science and global health.

Pandemics are like terrorist attacks: We know roughly where they originate and what’s responsible for them, but we don’t know exactly when the next one will happen. They need to be handled the same way — by identifying all possible sources and dismantling those before the next pandemic strikes.

We Knew Disease X Was Coming. It’s Here Now by Peter Daszak

Was this blog too technical? Watch this Netflix episode on Pandemics instead.

Digital Dissection

Brain Mapping & Brain Image Segmentation

Understanding the anatomic variability of the brain in the population is essential for connecting brain function to brain anatomy and for a deeper understanding of development, aging, and disease. The need of neurosurgeons to visualize the complex central nervous system (CNS) is pushing the use of a more accurate segmentation of different anatomical and pathological structures. While neuroanatomists debate the exact boundary of relatively simpler brain parts and traditional brain atlases identify regions only by pointing to the middle, leaving the interfaces between regions unspecified, the International Consortium for Brain Mapping (ICBM) seeks to create a so-called probabilistic human brain atlas. The technology is so advanced that the model will be able to predict the probability of a particular voxel, in such an interface between regions, being a part of either of the adjoining brain regions. (Voxel in a 3D structure is equivalent to pixel in a 2D image).

The inclusion of physiopathological data from functional MRI (fMRI) provides resolution of a few hundred microns over limited volumes and will identify areas with increased biological aggressiveness within a certain lesion prior to surgery or biopsy procedures. Sectioning, staining and optical digitization of cadaver brains allow even finer spatial and chemical resolution in limited numbers of brains. Such new data acquisition technologies with the concepts of 3D stereotaxic mapping will help create probabilistic maps at a very fine scale with the help of powerful computational tools for analysing high-resolution three-dimensional (3D) brain images. Fully automated neuroanatomic segmentation in large numbers of MRI data sets is essential if questions of normal population variability, normal longitudinal development, and detection of abnormality in single subjects or in groups are to be answered definitively.

Image segmentation is a multi step process of dividing an image into a set of semantically meaningful, homogeneous, and nonoverlapping regions of similar features such as intensity, depth, color, or texture. Subsequent measurement and visualization of anatomical & pathological structures will help in computer aided diagnosis, surgical planning and image-guided interventions.  

Brain pathology visualization


  • In degenerative diseases like Huntington’s disease and Alzheimer’s disease, the sulci become more open and the ventricles become enlarged. Measurements of these changes can lead to early diagnosis and treatment.
  • In Multiple Sclerosis a significant difference in the overall volume & distribution of affected brain tissue between drug and placebo groups can be used as a measure of drug efficacy.
  • Structural analysis of brain can also help to predict outcomes of Traumatic Brain Injuries.
  • On the same principles of brain volumetric analysis, breast volumetric analysis  can be used for aesthetic planning in breast reconstruction.
  • To visualize and store information in Forensic Case analysis.
  • In addition to direct hypothesis testing, the stereotaxic approach of brain mapping may allow for the detection of unsuspected patterns of interaction among normal brain elements and the isolation of constellations of measurements that characterize specific disease states.
Analysis of changes in the Hippocampus anatomy can be used for an early diagnosis of Alzheimer’s disease.


  • Such high technology tools often demand that scientific questions be restated and made more amenable to quantitative analysis.
  • Consent from patients for data collection & utilization and ensuring privacy are important issues which can be tackled by blockchain technology which gives direct ownership of images to patients.
  • Curated medical imaging data collection isn’t an easy job either. Curating medical imaging data includes but is not limited to data anonymization, checking the representative of the data, unification of data formats, minimizing noise of the data, annotation, and creation of structured metadata such as clinical data associated with imaging data.
  • The image segmentation methodology isnt fixed. Research has shown that different segmentation methodologies are better for different structures or pathologies. Validation and quantitative comparison of different segmentation methods is a general problem in medical image analysis. It requires a “ground truth” or gold standard to which the outcome of the segmentation method can be compared. Unfortunately, the “ground truth” does not exist for the analysis of in vivo acquired data in humans. Two methods are being used to establish this ground truth. One is a method by which radiologists review images to make labels and the other is a method based on radiologic reports. In the former case, a lot of time and effort is required and there may be an inter-reader disagreement on the label. In the latter case, the correctness of the labels itself may be unsatisfactory.
  • Incorporation of nonimaging parameters such as behavioral variables, demographic information, and genetic data into the statistical models.
  • Applying deep learning to this data comes with its own problems like absence of an audit trail to explain the decisions of the deep learning model, overfitting and adversarial data impact.

Present & Future

Will brain mapping help speed up clinical trials in case of Multiple Sclerosis? Will an Alzheimer’s patient get an earlier diagnosis so that he has more time to get his personal affairs in order? Will the family of a Road Traffic Accident patient get more concrete answers about their loved one’s chances to pull through? This technology like any other will only make economic sense if it creates significant value in people’s lives.

NeuroShield™, a cloud based Clinical Decision Support Tool for Neurological Disorders, is being developed by InMed Prognostics, a Pune based healthcare tech startup. It provides volume analysis of brain parts segmented from 3D MRI and compares it to Indian reference ranges built specifically from Indian population data.

If Google Maps is showing routes to autonomous vehicles today, the idea of ‘bio-tagging’ target voxels for robotic surgeries with the help of such readily available brain maps doesn’t seem too far fetched. Neither does the idea of two neurosurgeons sitting in a cafe and brainstorming the surgical approach to a holographic reconstruction of a segmented brain pathology seem too futuristic.

SentiAR provides real-time holographic visualization of the patient’s actual anatomy in the clinical setting, literally floating over the patient.