{"id":28162,"date":"2020-06-22T17:54:15","date_gmt":"2020-06-22T21:54:15","guid":{"rendered":"https:\/\/www.bu.edu\/cise\/?p=28162"},"modified":"2021-08-16T22:17:05","modified_gmt":"2021-08-17T02:17:05","slug":"pov-covid-19-shows-us-we-need-rapid-response-data-science-teams","status":"publish","type":"post","link":"https:\/\/www.bu.edu\/cise\/pov-covid-19-shows-us-we-need-rapid-response-data-science-teams\/","title":{"rendered":"POV: COVID-19 Shows Us We Need Rapid Response Data Science Teams"},"content":{"rendered":"

Eric Kolaczyk<\/span><\/a>, Director of Hariri Institute of Computing and Faculty Affiliate of CISE, contributed an opinion piece published by BU Today<\/em><\/span>\u00a0on\u00a0<\/em>June 4th<\/span>\u00a0entitled\u00a0<\/span>\u201cPOV: COVID-19 Shows Us We Need Rapid Response Data Science Teams.\u201d<\/span><\/a>\u00a0In it, Professor Kolacyzk describes how the COVID-19 pandemic is not only an indication of the need for development but also a rapid response from data scientists.<\/span><\/p>\n

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Photo by Shaxiaozi\/iStock<\/p>\n

By\u00a0Eric Kolaczyk<\/a><\/p>\n

During the COVID-19 pandemic, for better and for worse, we are learning much about ourselves as a society. One lesson that\u2019s become especially clear is the critically important role data science can play in times like this.<\/p>\n

Simply put, we\u2019re not prepared\u2014despite over a decade of intensive ramp-up in our capacity for data science education, despite the ubiquity of data-driven innovation, business, and decision-making across the world, despite our ability to do incredibly sophisticated artificial intelligence (AI) computation. Somehow, despite all of this, we are still not prepared for the data-centric challenges needed to manage and control this current pandemic.<\/p>\n

That \u201cwe don\u2019t have all the data\u201d is a complaint we\u2019ve heard far and wide at this point and it\u2019s absolutely true. But it\u2019s an easy excuse. More fundamentally, the point is that we\u2019re not prepared to collect the data. We\u2019ve struggled to track who is infected and when, where they got infected and how, whether or not they\u2019ve been tested and when they\u2019ve recovered, and ultimately, who they might have infected in turn.<\/p>\n

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Eric Kolaczyk, director of BU\u2019s Rafik B. Hariri Institute for Computing and Computational Science & Engineering. Photo by Cydney Scott<\/figcaption><\/figure><\/figure>
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Moreover, just as importantly, even if we had all of that data\u2014collected by a multitude of cities, states, and countries, schools and businesses, airports and train stations, and whatnot\u2014we\u2019re not uniformly prepared to wrangle the data into a usable form, to analyze the data, and extract empirically grounded and actionable insights from the data to help inform decision-making and policies. That is, we\u2019re not prepared to do data science as we\u2019d like to and should.<\/p>\n

As far back as 1854, during London\u2019s cholera epidemic, John Snow famously demonstrated the profound impact that data science (done well) can have in controlling infectious disease, when he used a combination of hospital and public records, along with interviews, to produce a map of cases that implicated the now-infamous Broad Street pump as the local source of the epidemic. The result of Snow\u2019s data science exercise is said to have involved removing the handle of the pump, and thus preventing residents from drawing tainted water.<\/p>\n

If only the solution were as simple for the coronavirus. Unfortunately, the scale of today\u2019s pandemic is vastly greater than that of the London cholera epidemic. In turn, so too is the scale of the data science challenges we face.<\/p>\n

None of this is to dismiss the enormous collective data science efforts across the country, and the world more generally, that have been critical in supporting and informing what success we have had in our management and control of COVID-19. But what\u2019s missing from this pandemic\u2014this global emergency\u2014is a level of coordination and integration among these efforts that emergency responders, militaries, and related units around the world have found is vital to optimizing their effectiveness.<\/p>\n

True, the data science community has responded rapidly with all that we have done. And, at the risk of overusing the phrase, perhaps even heroically in many ways, large and small.\u00a0Nevertheless, we have not produced a rapid response for a crisis that has seen hundreds or thousands of people dying every day around the world for more than three months.<\/p>\n

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We haven\u2019t had a pandemic for 100 years. And data science, at least in its modern form, is an area that is still quite young. So it is understandable, and perhaps even to be expected, that we find ourselves insufficiently prepared for the current crisis we face.<\/div>\n