How Cities Can Avoid Costly Lockdowns with Smart Use of Big Data
Sun Sun Lim and Roland Bouffanais say a tech-based approach is a better way to contain virus
You impose a lockdown. With people confined indoors, shops close and the economy grinds to a halt. Your Covid-19 infections fall, and soon it appears safe to end the lockdown and reopen your economy. Businesses open, people come out in droves and some sense of normalcy returns.
Before you know it, new virus outbreaks emerge and clusters expand and spread, threatening to overwhelm your health care services. You impose a lockdown, rinse and repeat.
Although there is encouraging progress towards viable vaccines, lockdowns of varying extents are the main tactic to contain Covid-19 when exponential growth in community cases occurs. Yet lockdowns are blunt tools that exact heavy costs. Some experts have cautioned that this disruptive lockdown cure is worse than the disease, claiming lives through job losses, social isolation and domestic strife.
Cities in particular can attempt to break free from the lockdown trap by identifying super-spreader locales. Instead of locking down the entire city, a more sustainable approach is shutting down or reconfiguring specific locations with high potential to trigger outbreaks by tapping insights from big data.
Like people, certain places can be spatial super-spreaders, and big data is the key to identifying these weak links. Cities are constantly throbbing with human activity as people transit, converge, mingle and disperse. Currently available human mobility data must therefore be mined to zero-in on vulnerable locations.
Since such high-density places have extensive contact between people from geographically disparate locations, tracking human mobility patterns is vital for uncovering and impeding disease propagation. Urban analytics data capturing ground transport trips is a critical building block in this endeavour.
In some cities, such transport data is analysed for improved urban and mobility planning. Newer data streams from ride-sharing services such as Uber, internet-of-things-connected devices including smart lamp posts and smartphones, can also help map where human mobility patterns and epidemic spread intersect.
Such information can then be integrated with the unfolding epidemiological evidence about the factors affecting Covid-19 transmission. Currently, transient contact seems less risky than sustained interaction in enclosed spaces.
Even so, our understanding of the relationship between disease spread and social interaction in different settings is still extremely limited. This knowledge gap constrains our ability to methodically chart how differences between fleeting contact in public venues versus sustained interaction in discrete communities influence disease transmission. To better refine containment measures in cities, sharpening such insights is a priority.
More systematic data collection initiatives and protocols must be introduced to ensure big data can be marshalled, shared and fully exploited.
Greater funding is required to promote research on the nature of human movement and social interactions in a greater diversity of urban locations.
However, not all cities collect such human mobility data, and fewer still make it publicly available either because of legal and technical hurdles or the absence of regulatory frameworks that facilitate data sharing. It is imperative that such obstacles
are cleared for research and analysis to be accelerated.
While we anxiously await a safe vaccine we can boost other efforts to manage this pandemic and start planning for the next one. Rather than resorting to wholesale lockdowns, governments can distil and blend insights from big data and epidemiology to anticipate super-spreading locales.
They can adopt more targeted safeguards such as social-distancing precautions for specific communities and settings, decongestion protocols in busy locations, traffic diversions during peak periods and restricted shutdowns in selected areas. Precisely because lockdowns spell the distinction between feast or famine for businesses and their employees, a robust, data-grounded approach to imposing targeted restrictions can help governments take tough decisions without seeming arbitrary, capricious or callous.
With a granular and focused containment strategy, cities need not go into a deep slumber to keep the virus at bay. They can strive to liberate themselves from the lockdown trap, the key for which lies with big data.
Sun Sun Lim is Professor of Communication and Technology and dean of Humanities, Arts and Social Sciences at the Singapore University of Technology and Design. Roland Bouffanais is an associate professor at the Singapore University of Technology and Design
