The detection paradox: How COVID-19 surveillance patterns shifted from lower-income to more affluent neighborhoods in Miami-Dade County

Passive disease surveillance systems may systematically undercount burden in low-resource minority neighborhoods, yet fine-scale evidence of this phenomenon remains limited. We examined spatiotemporal patterns of reported COVID-19 cases across Miami-Dade County's diverse neighborhoods and investigated which socioeconomic and demographic characteristics predicted areas of elevated and suppressed case rates. In an ecological, cross-sectional analysis of 491,769 geocoded cases across 1,831 census block groups from March 2020 to May 2021, we applied negative binomial regression with spatial eigenvector filtering and Getis-Ord Gi* hot spot analysis across five pandemic periods aligned with major policy shifts.The analysis revealed a detection paradox. Early hot spots concentrated in lower-income census block groups (median household income: $52,022–$55,676), but by September 2020, hot spots had shifted to more affluent areas (median: $77,069). Historically Black neighborhoods along the I-95 corridor emerged as persistent cold spots despite multiple structural risk factors: lower incomes, reduced insurance coverage, and limited healthcare access. From Period 3 onward, predominantly Black census block groups showed significantly lower reported rates (incidence rate ratio [IRR]: 0.991–0.995, p