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Wastewater-based epidemiology (WBE) played a vital role during the COVID-19 pandemic by providing early warnings of outbreaks through SARS-CoV-2 RNA detection in sewage. Many rural communities did not benefit from WBE because limited centralized sewer infrastructure challenged conventional WBE surveillance strategies. We present a multi-agent computer model simulating COVID-19 spread in a U.S. county with both sewered and non-sewered zones to assess the performance of WBE in this setting. We evaluate how the sewage service status of the first SARS-CoV-2 carrier, cross-zone community mobility, and WBE detection thresholds influence outbreak detection timing at the county’s wastewater treatment plant under basic reproduction numbers (R0) of 4 and 8. Our key findings include that (1) a detection threshold of 10 gc/mL can identify outbreaks up to six days earlier than a threshold of 50 gc/mL; (2) outbreaks originating in non-sewered zones are detected 1–2 days later, compared with outbreaks in sewered zones; and (3) cross-zone community mobility impacts detection timing only when outbreaks begin in non-sewered zones. Furthermore, once detected, disease prevalence can increase by five- to eleven-fold within the following week. These results underscore the importance of WBE sensitivity and tailored surveillance strategies in both sewered and non-sewered zones of a community. Strengthening WBE capabilities at local treatment facilities can improve early outbreak detection, thereby supporting timely public health interventions.