Daily SARS-CoV-2 loading for two sewersheds in Austin, TX were calculated.

ZIP code level daily COVID-19 cases were apportioned to admin units within each sewershed.

LOWESS cross-correlations identify correspondence between SARS-CoV-2 loading and cases.

Variations in lag period and the strength of correlation exist across admin units.

Disaggregating and apportioning cases provides a more nuanced view of community spread.

Severe acute respiratory syndrome - coronavirus 2 (SARS-CoV-2) continues to effect communities across the world. One way to combat these effects is to enhance our collective ability to remotely monitor community spread. Monitoring SARS-CoV-2 in wastewater is one approach that enables researchers to estimate the total number of infected people in a region; however, estimates are often made at the sewershed level which may mask the geographic nuance required for targeted interdiction efforts. In this work, we utilize an apportioning method to compare the spatial and temporal trends of daily case count with the temporal pattern of viral load in the wastewater at smaller units of analysis within Austin, TX. We find different lag-times between wastewater loading and case reports. Daily case reports for some locations follow the temporal trend of viral load more closely than others. These findings are then compared to socio-demographic characteristics across the study area.

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