When we discuss a medical emergency where lakhs of people are in their death bed, and few more lakhs are exposed to the immediate threat, what comes to our mind first is the role of the emergency workers like healthcare professionals, leading the way in this time of crisis. Data Scientists, a white-collar job, with the provision of working from a comfortable set up of home, what can they possibly offer to repair the unprecedented loss of human tribe? Well to answer these let’s understand how a data scientist works under the hood. And then let’s look at the role the data scientists can play to ease the effects of COVID-19 pandemic on the well-being of the human tribe.
So what’s the illusively elusive term ‘The data science’? The data science is the collective term for data collection, data cleaning, data exploration and modelling. Data can be found in huge size and in many forms and variables, thus making data management complex and a big deal. When we think about large amount of data, statistics is the subject that comes to our mind.
So are data scientists basically statistician?
The answer would be both Yes and No. Yes, since managing data and fitting it into an efficient model requires an understanding of statistical concepts. A huge area of data science algorithm development covers knowledge about linear algebra and differential calculus. But a statistician may not have knowledge about IDEs and Web scrapping using new-age computing languages like Python and R. So to explain the job profile more clearly, Josh Wills, director of data engineering at Slack said
“Person who is better at statistics than any software engineer and better at software engineering than any statistician”.
So basically implementing statistics and differential calculus, designing a model using artificial intelligence, machine learning, etc. using programming languages such as Python, R and demonstrating the final model using data visualization tools like Tableau and Power BI is what Data Scientists do, in a nutshell.
Now coming to the need of the hour, the COVID 19 pandemic, let’s see how the novel coronavirus is affecting the human race in terms of physical health and well-being and how data science can help ease the situation.
The novel coronavirus, which by the time is slowly losing its novelty as the period of contamination is increasing day by day, is highly contagious. Also, there is no possible medicine to cure the disease discovered so far. As the number of people with the disease is increasing, the healthcare facilities are also saturating due to the high volume of patients, leading to a complete collapse of the healthcare system. Social distancing is a way to check the spread, nevertheless, let’s not forget that humans are after all social beings.
That is why it is of utmost importance to study the virus for obtaining any possible medication or vaccination. The challenges are that: a vaccine against a coronavirus was never found before. Also, other coronaviruses such as common cold do not create long-term immunity upon its exposure to the human body. Without the invention of a vaccine, it is nearly impossible to eliminate the virus from the planet.
Many types of research involving Generative Adversarial Networks (GAN), a deep learning technique that uses two neural networks, competing against each other in order to generate new synthetic data that can very well replicate real data, is being used to discover the possible drug/vaccine for COVID-19. Google DeepMind had released a dataset of the Protein structure of SARS-CoV-2 which is appreciated for predicting the protein structure of COVID-19 accurately. X-Ray and CT-Scan datasets are also being used to study about many effects of COVID-19 on the human body, Pneumonia being the one. Making use of the demographic data available alongside, AI is used to detect the hotspot zone of infectious disease.
Monitoring the availability of hospital resources accounts in dealing with enormous amounts of information. Data science thus comes to rescue, and can be used to keep count of existing essential human resource available like medical professionals, in the vicinity of the zone of interest.
Johns Hopkins University, IBM and Tableau, in association with Centers for Disease Control and Prevention (CDC) of US, China and Europe and the World Health Organization (WHO) have released interactive databases that uses real-time graphic information system for viewing details about the patient infected, recovered or died due to this virus. Data Scientists helped in designing a faster way of Contact Tracing, an efficient move to suppress the spread to a large extent.
Some contact tracing apps with over a million downloads are India’s Arogya Setu, Singapore’s Trace Together and South Korea’s Corona Watch. These apps by utilizing Bluetooth and GPS technology of the mobile phone sends alerts to users when they breach social distancing norms. If for say, someone comes within close proximity of 10 people during a day, all of them will be enlisted as the person contact chain. In case the nodal man is found to be positive, his contact chain will be intimated through the app and will be insisted to go and get tested. It will also alert the healthcare professional of that area.
The MVP features of these apps are Login credentials, Bluetooth access, self-assessment test, symptom reporter, hotspot locator, affected people details. The efficacy of this idea, however, solely depends upon the rate of downloads. Another app called COVID Symptom Tracker uses machine learning to enable people interact with the app and discuss symptoms and travel history for detecting possible cases. Data scientists are also working to forecast the spread of coronavirus.
The machine-learning department of Carnegie Mellon University built a model that processes data from several sources such as flu-related Google searches, Twitter activity, and web traffic to predict the spread of the virus. The data science can be used to study the effect of the virus on some more than others. It can help decide the measures that can help reduce the spread.
Data science can also derive an analogy between environmental or genomic factors to the people who are getting advanced respiratory problems. Thus, in conclusion, data science is being used efficiently by new-age data scientists to stabilize the situation and for reshaping the human livelihood. A lot more things still need to be done. Fellow data scientists, are you listening?