Both before and especially since COVID-19 had been officially declared a pandemic by the WHO on March 11, doctors across the world worked around the clock to fight the virus by treating patients, recommending measures to lower mortality rates, and conducting research to develop an effective vaccine. Panic set in and the world went into lockdown in some form or another. And while the medical community was working diligently and urgently, there were a certain group of individuals who were also hard at work to solve this crisis, not to be found in hospitals and laboratories, but instead behind their computer screens, analysing thousands of lines of code, and creating complicated models and graphs that had an effect on public policy as significant as those of the foremost medical experts. These people are known as data scientists.
Data science is a discipline in which large amounts of data is analyzed and structured in a way to understand, predict, and model different outcomes based on a certain set of conditions. The obvious question that follows is, how could analyzing sets of numbers and writing code possibly help us in stopping the most disruptive crisis of a viral pandemic in recent history?
There are a few ways data science can offer insights into the best policy decisions governments can take in combatting a crisis like coronavirus, and the following are the three best examples of the contributions data scientists have made over the past year.
Mapping the Movement of Those Infected
Analysing the patterns of movement of individuals, clusters of individuals, and larger groups, is key to understanding and tracking viral hotspots where a high rate of infectious transmission could occur, key in stopping continuous waves of cases. Countries like Taiwan harnessed the power of data mapping and only reported 448 cases (as of July 27), while their counterparts across Southeast Asia were reporting cases in the millions. Analyzing travel logs and tracking people who have been in close contact with the infected would be the most important step in the preliminary stages of any pandemic; countries that did this were able to do so with the help of data scientists, and are now able to reduce lockdown periods resulting in lower economic damages.
Identifying Genetic Patterns Across Mutations and Antidotes
Like most infectious diseases, the analysis of recurring characteristics of a disease and it’s constant mutations and matching it with potential vaccines and antidotes, help medical professionals investigate medical solutions to the problem. This is no different in the case of COVID-19. However, the healthcare sector isn’t able to scrutinise the larger sets of data involved with both tracking the mutations of the virus (and it’s genetic implications) as well as the progress made by individual vaccines. In the UK, analysts at Oxford University used countless logistic regression models to see how the virus mutated from person to person and which groups were most likely to face higher levels of mortality. These data scientists influenced the UK’s initial lockdown guidelines.
Predicting the Total Economic Impact
While understanding the virus itself was a major aspect of what data scientists did at the start of the pandemic, another crucial role data scientists played was as advisors to governments to evaluate the economic consequences of necessary measures such as widespread lockdowns and shutdowns of important economic activity. The U.S., for example, had state governments such as Florida take the help of data analytics firms to decide whether a lockdown was worth it using cost benefit analysis.
The aforementioned ways are just a glimpse into the different applications of data science in revolutionising how health care is approached. Clive Humby, a British mathematician and pioneer in the data science sector, said in an interview in 2006 that “Data is the new oil”. But it’s so much more. The vast opportunities it presents can radically change how we approach the most perplexing of crises, find relations that we never knew once existed, and expand learnings in the healthcare system that are obsolete. The doctors have helped patients recover and heal, but the people behind their computers and thousands of lines of code helped identify the problem in the first place.
The Candid Contrarian 