COVID-19 case counts in Miami-Dade tracked who could get tested, not who got sick

What is it about?

We mapped nearly half a million reported COVID-19 cases across 1,831 small neighborhood units in Miami-Dade County, Florida, from March 2020 to May 2021. These units, called census block groups, hold about 1,400 people each, so they show differences that ZIP codes blur. We split the fifteen months into five periods that matched major changes in testing and public health policy, and for each period we asked where reported cases clustered and what those neighborhoods had in common. Early in the pandemic, the neighborhoods with the most reported cases were lower-income. By September 2020, as testing expanded, the highest reported counts had moved to wealthier neighborhoods. At the same time, historically Black neighborhoods along the I-95 corridor, including Liberty City, Little Haiti, and parts of Opa-Locka and Miami Gardens, showed unusually low reported counts, even though their residents had lower incomes, less insurance, and less access to care. Features that should raise the risk of infection, such as renting and relying on Medicaid, predicted fewer reported cases. We call this the detection paradox. The most likely explanation is that reported case counts followed access to testing rather than the spread of the virus.

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Photo by Mick Haupt on Unsplash

Photo by Mick Haupt on Unsplash

Why is it important?

Health departments use reported case counts to decide where to send tests, vaccines, and mobile clinics. If those counts rise where testing is easy to reach and fall where it is hard to reach, resources flow toward the neighborhoods that already have the most access, and the gap grows. Our findings show this happened in Miami-Dade within the first year of the pandemic. Most earlier studies of COVID-19 in this region used ZIP codes. A single ZIP code can contain neighborhoods that differ sharply in income, race, and access to care, so the pattern we found was invisible at that scale. Working at the block group level made it visible. The paper points to practical fixes. Surveillance should not depend only on people seeking a test. Wastewater monitoring, random population sampling, and tracking of severe illness all measure disease without requiring a trip to a testing site. Health departments can flag neighborhoods where risk is high but reported counts are low and send testing to them, with hours that fit shift work and locations on transit routes. The problem is not limited to COVID-19. Any disease tracked through voluntary testing will show the same distortion wherever testing is unevenly distributed.

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