Data Science and Covid-19. It’s been close to 100 years since a global pandemic last plagued the human race. While it was Spanish Flu in 1918-1920s, CoronaVirus or COVID-19 is wreaking havoc in 2020. CoronaVirus, an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was first identified in December 2019 in Wuhan, China. As of June 2020, according to John Hopkins university, close to 6.51 million cases have been reported across 188 countries and territories, resulting in close to 386,000 deaths. The pandemic has not only resulted in gargantuan loss of human life but also has pushed the global economy into a recession. According to the International Monetary Fund (IMF), the global economy is expected to contract by over 3 per cent in 2020 – the steepest slowdown since the Great Depression of the 1930s.
The harm caused by COVID-19 on businesses is not restricted to certain clusters rather it’s a wider ailment that is expected to keep the economy ill for a long time. The scale of businesses getting hurt varies from sector to sector but majorly five industries – Aviation, Retail, Financial , Real Estate and Automobiles – are aching the most under pain. The situation presents us with an important question, whether businesses could have reacted better?
The zippy, worldwide spread of COVID-19 has reiterated the importance of Data Science in the contemporary world. While researchers and developers belonging to healthcare sector are using machine learning and natural language processing to track and reduce the impact of virus, businesses are using the same for business tracking and forecasting. The pandemic has created unforeseeable fluctuations, thus by placing rapid-reporting cycles it can be understood how businesses are being affected, where mitigation is needed and how rapidly operations are recovering. As per a HBR report, Lead Your Business Through the Coronavirus Crisis, “A crisis doesn’t imply immunity from performance management, and sooner or later markets will judge which companies managed the challenge most effectively.”
The importance of Data Science can be illustrated through the example of BlueDot, a Canadian AI startup. The professionals within the company used natural-language processing and machine learning techniques to sift through the mammoth data across news reports in 65 languages, along with airline data and reports of animal disease outbreaks. BlueDot published a report on December 21, 2019 sending out alerts regarding COVID-19 even before WHO had alerted about the pandemic. Taiwan’s detailed pandemic plan after 2003 SARS outbreak along with big data analytics capabilities helped them to predict and rigorously guide their citizens during the latest outbreak. Even with its proximity to China and high inter-country travel, Taiwan has successfully contained the spread of virus with only 443 confirmed cases leading to 428 recoveries and 7 deaths, according to John Hopkins University, so far.
A 2006 HBR report, Preparing for a Pandemic, discusses a pandemic preparedness checklist for businesses developed by the U.S. Department of Health and Human Services and the Centres for Disease Control and Prevention (CDC). It identified steps a company should take to prepare for a possible avian flu pandemic and a majority of its suggestions can also help in other emergency situations including COVID-19. The checklist however said little about how to approach the problems it frames such as “gauge potential impact of a pandemic on company business financials,” “plan for scenarios likely to increase or decrease of demand,” or “evaluate what employee access to mental health and social services would be”? Baruch Fischhoff, a decision researcher and risk communication consultant, within the same HBR report talks about a Machine Learning technique to approach the aforesaid problems. He states that such complex problems, based on uncertain assumptions, are best explored through formal visualisation and a way to do this is Influence Diagrams. A standard tool in decision analysis, influence diagram demands you to think what is known and what not ? They require explicit mapping of all the factors shaping a vital event—like business activity during a pandemic. Influence diagrams translate knowledge into a form that can be shared, pooled, and evaluated. Typically, the exercise reveals vague assumptions, incomplete analyses, or missing information—and thus creates opportunities for better problem solving. The model should encompass sampling of the factors relevant to business planning for a pandemic. Managers in companies can then use Influence diagrams as a starting point to elaborate the factors that concern them. For example, they can specify what business activity means for their firm, then analyse how a pandemic would threaten it and what the consequences of success or failure in responding would be.
Although COVID-19 has redefined normal and battered the economy across nations, it has helped us appreciate the importance of Data Science not only for business expansion but also for pandemic preparedness. And thus going forward Data Science should embed as an essential service within each organisation.