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Domestic wells provide drinking water to 44 million people nationwide. Many of these wells, which remain federally unregulated and rarely tested for pollutants, serve rural populations clustered near surface-contaminated sites (e.g., hazardous waste sites, animal agriculture operations, coal ash ponds, etc.). The potential for natural disasters to deteriorate drinking water quality is well documented. Less understood is whether opportunistic post-disaster sampling might underrepresent vulnerable populations. When disaster strikes, well water sampling campaigns offer a glimpse into the quality of water for exposed residents. We examined over 8,000 well water samples from 2016 and 2017 to measure Hurricane Matthew’s impact on the presence of indicator bacteria. Bacteria presence was predicted at the household level following Hurricane Matthew’s landfall. The residential addresses associated with birth records as well as clinically estimated dates of conception and birth dates were used to predict the likelihood of indicator bacteria in drinking water sources that were unsampled but likely to have served pregnant women. We estimate that opportunistic well water sampling captures the average predicted contamination rates among households with pregnant women. Our approach documents a distribution of contamination risk where 2.7% of the vulnerable sample (670 unsampled households) have a 75% likelihood of total coliform presence. The predicted likelihood of indicator bacteria is elevated for a small share of households nearby swine lagoons that experienced the most torrential rainfall. However, the gap between sampled and unsampled households cannot otherwise be explained by the storm event or proximity to surface-contaminated sites. Findings suggest that sophisticated and holistic water quality prediction models may support post-disaster sampling campaigns by targeting individual households within vulnerable groups that are likely to experience higher risks from groundwater contamination.