Predicting COVID-19 cases in diverse population groups using SARS-CoV-2 wastewater monitoring across Oklahoma City.

SARS-CoV-2 was discovered among humans in late 2019 and rapidly spread across the world. Although the virus is transmitted by respiratory droplets, most infected persons also excrete viral particles in their feces. This fact prompted a range of studies assessing the usefulness of wastewater surveillance to determine levels of infection and transmission and produce early warnings of outbreaks in local communities, independently of human testing. In this study, we collected samples of wastewater from 13 locations across Oklahoma City, representing different population types, twice per week from November 2020 to end of March 2021. Wastewater samples were collected and analyzed for the presence and concentration of SARS-CoV-2 RNA using RT-qPCR. The concentration of SARS-CoV-2 in the wastewater showed notable peaks, preceding the number of reported COVID-19 cases by an average of one week (ranging between 4 and 10 days). The early warning lead-time for an outbreak or increase in cases was significantly higher in areas with larger Hispanic populations and lower in areas with a higher household income or higher proportion of persons aged 65 years or older. Using this relationship, we predicted the number of cases with an accuracy of 81-92% compared to reported cases. These results confirm the validity and timeliness of using wastewater surveillance for monitoring local disease transmission and highlight the importance of differences in population structures when interpreting surveillance outputs and planning preventive action.

Community change and resident needs: Designing a Participatory Action Research study in Metropolitan Boston.

The health implications of urban development, particularly in rapidly changing, low-income urban neighborhoods, are poorly understood. We describe the Healthy Neighborhoods Study (HNS), a Participatory Action Research study examining the relationship between neighborhood change and population health in nine Massachusetts neighborhoods. Baseline data from the HNS survey show that social factors, specifically income insecurity, food insecurity, social support, experiencing discrimination, expecting to move, connectedness to the neighborhood, and local housing construction that participants believed would improve their lives, identified by a network of 45 Resident Researchers exhibited robust associations with self-rated and mental health. Resident-derived insights into relationships between neighborhoods and health may provide a powerful mechanism for residents to drive change in their communities.