The various lockdown measures introduced to try and slow the spread of Covid-19 relied heavily on their ability to restrict our movements. A new paper from the University of Chicago proposes a new method for implementing similar lockdowns that would result in fewer infections while also limiting the economic hit society would have to take.

“The existing approaches ignored individual mobility and focused only on disease prevalence in a neighborhood. Doing so leads to potentially ineffective solutions where individuals who live in high disease prevalence neighborhoods could spread the disease to other locations, e.g., when traveling for work or leisure. We can fix this issue by using mobility data,” the researchers explain.

Spatial targeting

They highlight how spatial targeting can more accurately track the movement of the population, and indeed areas that would benefit from lockdowns to reduce the spread of infection. The approach utilizes mobile data and produces improved results compared to the largely uniform and non-targeted closures that were used during the Covid pandemic.

This, therefore, helps to limit the spread of a virus while also reducing the scale of economic impact society incurs because much smaller areas are placed into lockdown at any particular moment in time.

“Our results show that appropriate targeting achieves a reduction in infections with up to 23–42% lower economic cost, and by enabling 4–6 times more economic activity than uniform citywide closure policies,” the researchers explain.

The approach suggests that a better understanding the movement of people within a region allows for better and more targeted policies to be introduced than relying instead on local infection rates. These can be improved still further if regions join forces.

“Spatially-targeted restrictions could be extremely valuable in curbing epidemic spread and simultaneously ensure that induced economic losses are limited,” the authors conclude.