Separating signal from noise in wastewater data: An algorithm to identify community-level COVID-19 surges in real time

Wastewater monitoring has provided health officials with early warnings for new COVID-19 outbreaks, but to date, no approach has been validated to distinguish signal (sustained surges) from noise (background variability) in wastewater data to alert officials to the need for heightened public health response. We analyzed 62 wk of data from 19 sites participating in the North Carolina Wastewater Monitoring Network to characterize wastewater metrics around the Delta and Omicron surges. We found that wastewater data identified outbreaks 4 to 5 d before case data (reported on the earlier of the symptom start date or test collection date), on average. At most sites, correlations between wastewater and case data were similar regardless of how wastewater concentrations were normalized and whether calculated with county-level or sewershed-level cases, suggesting that officials may not need to geospatially align case data with sewershed boundaries to gain insights into disease transmission. Although wastewater trend lines captured clear differences in the Delta versus Omicron surge trajectories, no single wastewater metric (detectability, percent change, or flow-population normalized viral concentrations) reliably signaled when these surges started. After iteratively examining different combinations of these three metrics, we developed the Covid-SURGE (Signaling Unprecedented Rises in Groupwide Exposure) algorithm, which identifies unprecedented signals in the wastewater data. With a true positive rate of 82%, a false positive rate of 7%, and strong performance during both surges and in small and large sites, our algorithm provides public health officials with an automated way to flag community-level COVID-19 surges in real time.

How Do Women Learn They Are Pregnant? The Introduction of Clinics and Pregnancy Awareness in Nepal

The earlier a woman learns about her pregnancy status, the sooner she can make decisions about her own and infant's health. This paper examines how women learn about their pregnancy status and measures how access to pregnancy tests affects earlier pregnancy knowledge. Using 10 years of individual-level monthly panel data in Nepal, we find that, on average, women learn they are pregnant in their 4.6th month of pregnancy. Living approximately a mile further from a clinic offering pregnancy tests increases the time a woman knows she is pregnant by one week (5 percent increase) and decreases the likelihood of knowing in the first trimester by 4.5 percentage points (16 percent decrease). Women with prior pregnancies experience the most substantial effects of distance within the first two trimesters, while, for women experiencing their first pregnancy, distance does not affect knowledge. These results suggest that, while access to clinics can increase pregnancy awareness for women who recognize pregnancy symptoms, other complementary policies are needed to increase pregnancy awareness of women in their first pregnancy.