During the COVID-19 pandemic, the United States is flying blind, unable to track its spread or accurately predict when the disease will peak in different parts of the country because of a lack of diagnostic tests.
To try to fix that, some researchers are turning to tools that first debuted after the 9/11 World Trade Center and anthrax letter attacks of 2001, asking whether data that is collected under other auspices—or volunteered by people who think they might be infected—could buttress the official detection system and offer early warnings of hot spots. Some think it could be one of the pillars for a national strategy that carries the United States into a safe zone beyond the peak—provided key issues of reliability, privacy, and equity can be worked out in time.
The term for collecting that kind of data is “syndromic surveillance.” In medicine, a syndrome is a cluster of signs (things that can be measured, such as temperature) and symptoms (things that are subjective, like a headache) that might be associated with more than one diagnosis or hasn’t received a diagnosis yet. Noticing signs and symptoms is what propels people to seek a doctor’s visit if they can and get tested if a test is available, so identifying syndromes can provide useful data days or weeks before test results arrive. But because syndromes are nonspecific—fever, body aches and cough, for instance, could indicate either COVID-19 or the flu—lifting the signal out of the background noise is a challenge to solve.
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