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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. 

The adoption and implementation of the Next Generation Science Standards (NGSS) require significant shifts in how K-12 science teachers implement instruction and in the ways that science teacher educators prepare future science teachers. The use of anchoring phenomena to drive instruction is one of these significant shifts. However, identifying phenomena that anchor K-12 student learning and support students in developing conceptual understanding while aligning with the NGSS Performance Expectations can be challenging for teachers, especially for preservice teachers (PSTs). This paper outlines the development and implementation of the ASET Phenomenon Tool (Phenomenon Tool) by a group of science education faculty in a networked improvement community (NIC). The Phenomenon Tool aims to help PSTs identify and evaluate student-centered NGSS phenomena. Based on data collected over five academic years, we have found that more PSTs were able to identify a phenomenon after the NIC faculty implemented the Phenomenon Tool in their science methods courses. In this paper, we also present the PSTs’ alternative concepts about phenomena and their weaknesses in describing phenomena. The strategies and possible activities for the use of the phenomenon tool with teachers are discussed at the end.