Some public-health researchers are embracing data and technology to target small groups with precise health interventions. Others fear that these tactics could fail millions.
A man has a nasal swab at a mobile COVID-19 testing site in Manhattan, New York City. Credit: Todd Heisler/From their offices in a high-rise building in Queens, epidemiologist Sharon Greene and her colleagues watched the COVID-19 pandemic sweep through New York City in April 2020. Using an open-source data-analytics program called SaTScan, her team mapped outbreaks as they unfolded across individual neighbourhoods, almost in real time.
“Instead of just parking a testing van somewhere in an affected zip code, we can park it at an intersection in the middle of the cluster,” Greene says. “It’s hyper-local public health.” By the middle of the year, cases in the city began to drop. The definition of precision public health is sprawling and variable: for most researchers in the field it includes a sweep of data-driven techniques, such as sequencing pathogens to detect outbreaks and turbo-charging data collection to monitor harmful environmental exposures. It also encompasses an ambition to target interventions to specific people who need them.
The debate over the merits of precision public health has typically taken place in the pages of academic journals. But funders are putting hundreds of millions of dollars behind precision-public-health initiatives, and some researchers worry about the implications for conventional public health. Spending on public health is already sliding: although national health expenditure in the United States grew by 4.
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