As the pandemic wears on, I've begun to forget what the inside of my office looks like. The last time I saw it was the second week of March, when my colleagues and I were told to work from home. Most of us had an easy enough time making the transition: At the Computational Health Informatics Program, an initiative jointly run by Boston Children's Hospital and Harvard Medical School, we spend much of our time in front of screens anyway. We had been studying Covid-19 since late January, modeling its spread in hopes of understanding how it might evolve in the weeks and months ahead. Now we'd swap our desk chairs for couches. I switched off my office mood lamp and fairy lights, grabbed my laptop, and quickly familiarized myself with the VPNs I would need to gain remote access to our institutional computing services.
Others in my field weren't so lucky. As I settled in at home, I saw tweet after tweet from scientists all over the world whose professional lives had ground to a halt. Laboratories were shutting down. Clinicians could no longer see their patients. The postdoctoral job market had suddenly dried up, and many recent graduates were concerned about the gaps the pandemic would leave in their CVs. Even among those who still had work to do, there was a feeling of listlessness: Everyone wanted to contribute something to the fight against Covid-19, but some worried they didn't have the ability to do so on their own.
On March 18, five days after the Trump administration declared a national emergency, I decided it was time to harness all this pent-up brainpower. I put out a call on Twitter for qualified volunteers who wanted to use their extra time to tackle a myriad of research questions at the intersection of computing and Covid-19 epidemiology.
Right away, expressions of interest flooded my inbox: I heard from a veterinary clinician in India with expertise in zoonotic diseases, a category that includes Covid-19; an engineer in Canada who'd recently completed her master's degree in artificial intelligence and could help with deep learning; a health law and policy specialist from France who could speak to the pandemic's legal and political implications.
Surprised by the deluge, I enlisted my friend Angel Desai, an infectious disease physician, and my husband, Imran Malek, a recent law school graduate with a decade of experience in software engineering, to form an ad hoc oversight committee. And just like that, the Covid-19 Dispersed Volunteer Research Network was born.
We decided to formally launch our effort with a weekend hackathon. Other groups had organized similar events to develop diagnostic tests and help with the shortage of medical equipment, so why not do the same for research? From the beginning, we knew we'd have to shake up the usual way of doing things. In a traditional lab environment, the structure tends to be hierarchical: A principal investigator sets the agenda and divvies up tasks for the group. Our hope was to proceed more democratically. We didn't want to scare off people who were donating their spare evenings and weekends, an immensely precious commodity at a time when everyone's lives had been upended. And we suspected that a group as diverse as ours, encompassing a wealth of disciplines, 20 different native languages, and 25 self-identified ethnicities, would work best with minimal limits on its ingenuity.
More than 30 volunteers across dozens of different institutions signed up for the event. We started by hosting an all-hands meeting on Zoom, where the oversight committee laid out some of the unanswered questions we'd encountered in our own research: Could we use smartphone mobility data to gauge whether people were adhering to lockdown orders? What might internet search query data reveal about the public's interest in coronavirus treatment scams?
The participants sorted themselves into groups, settled on eight different projects, and got to work. They kept at it for 54 hours; shockingly, no one quit. Many of their studies will soon be published in peer-reviewed scientific journals. One team, made up of epidemiologists and computer programmers, decided to perform a meta-analysis of clinical and epidemiological parameters associated with Covid-19, then develop an interactive online interface to visualize their results. A tool like this can help public health decisionmakers predict where the disease will go next, and it makes the same knowledge accessible to the general public.
This kind of cross-institutional, almost cross-cultural, work is very much at odds with academia's usual way of doing things. Prior to the pandemic, it was rare that any of us ventured outside the bubble of our own universities and hospitals. Over the decades, this siloed approach to research has shaped the way science gets done—and who gets to do it. The system tends to favor the career advancement of those who belong to a select few institutions over all others, irrespective of the depth of their skills or training. A growing body of literature suggests that underrepresented minorities are less likely to attend prestigious universities, even when they are equally qualified to do so. As a result, scientific research suffers from a lack of diversity—despite the fact that deeply diverse teams appear to produce better solutions to problems.
Academia divides researchers in other ways too. Most of us are used to working mostly, if not exclusively, with others in our fields. But as Tenley Brownwright, a postdoctoral scholar at Penn State and member of the volunteer network, puts it, “very few topics exist in a vacuum.” Brownwright is a spatial epidemiologist, which means that she primarily studies how health varies with geography, but she regularly works with theoretical biologists and clinicians. “It's very easy to get stuck in our niche as researchers,” she says.
But the pandemic is a problem that crosses disciplines. It requires devising plans to reopen the economy while being mindful of public health, or developing strategies to distribute antiviral drugs and vaccines while making sure they're affordable. By forcing public health researchers out from behind the walls of their home institutions and into fully virtual workspaces, the pandemic has in many ways enabled the kind of collaboration that science needs most.
Since our first hackathon, the volunteer network has grown to nearly 100 people, with 23 active research projects. One team is analyzing text extracted from hundreds of thousands of news articles to better characterize the quality of the US media's pandemic coverage. Another is sifting through millions of tweets to understand how public sentiment toward face masks has shifted since early April, when the CDC recommended that everyone wear them. Without question, the diversity of the network, across disciplines and institutions but demographically too, has been a tremendous boon to the formulation and investigation of problems that really matter.
The research hasn't been without its challenges. Chief among them is work-life balance—a goal that, as millions of us are now discovering, becomes uniquely elusive when one's home becomes one's full-time office. People's pets and children often chime in with indecipherable key-smashes on Slack, or clamor in the background of Zoom meetings; none of us think it's strange anymore to email a colleague at 1 o'clock in the morning. Among the volunteers, we enforce regular breaks during work sprints to encourage some semblance of normalcy. Unless you're careful, Brownwright says, “time is unstructured and feels endless.”
Since March, a sense of camaraderie—even friendship—has materialized among the researchers. Whenever there's a juicy announcement on the coronavirus front, they react on Slack with a sea of Kermit-sipping-tea GIFs. They shower each other in a set of custom emoji, the most popular of which is a French cartoon chicken called Piu Piu. They swoon over an image of Anthony Fauci, the director of the National Institute of Allergy and Infectious Diseases, in his youth. (“Foxy Fauci,” my husband calls it.)
Yet the connections go further. “I've witnessed a deeper sense of caring emerging from the pandemic's shadow,” says Benjamin Wong, an epidemiologist at the Centre for Global Health Research in Toronto and one of the first volunteers to commit his time to the network. “Conversations about our struggles are now openly shared when, months earlier, they may have been left unsaid.” Grappling with mortality is a professional hazard for many in public health. But the frankness with which we're discussing it today—maybe because some of us have lost loved ones to Covid-19, including the frontline workers we call our friends and colleagues—points the way toward a much needed shift in culture.
These conversations have prompted some of the researchers in the network to begin studying the collective trauma associated with Covid-19, which will likely have population-wide mental health repercussions that long outlive the pandemic itself. They're using natural language processing to examine deidentified text from online therapy sessions, aiming to better understand both the short- and long-term effects of the crisis on anxiety and depression. Work like this requires the expertise of psychologists, epidemiologists, and computer scientists—all of which the network has.
Many of us will be returning to our offices someday in the future, and when that day arrives, we ought to take the things we've learned with us. The very possibility of remote work should be offered as a reasonable alternative to those who might otherwise be asked to uproot their lives for a one- or two-year position—a common occurrence in the postdoctoral phase of an academic career. “In quantitative disciplines like epidemiology, much of our work can be done from home,” Brownwright says. “I hope going forward we can keep this level of support and flexibility in place, so postdocs and labs are able to choose the situation that best fits them.”
So much is possible when we break down the walls and rigid work styles that have traditionally mired academic science. Here's hoping for a post-pandemic future that puts them to their rightful end.
MAIMUNA S. MAJUMDER (@maiamajumder) is a computational epidemiologist and junior faculty member of the Computational Health Informatics Program at Boston Children's Hospital and Harvard Medical School.
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