As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1–3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Underwater acoustic (UWA) communication suffers from the limited available bandwidth for data transmission. Fullduplex (FD) communication has demonstrated the ability of achieving high spectral efficiency in terrestrial radio communications. There is a significant potential in adopting the benefits of FD in UWA systems. The major obstacle in FD communications is the severe self-interference (SI) introduced by the near-end transmitted signal. For FD UWA communications, the low signal frequency allows high-resolution ADCs to be used. With higher performance ADCs, it might be possible to achieve higher digital SI cancellation performance than that in FD radio systems. In this paper, we present experimental results of digital SI cancellation in FD UWA system, based on the use of the low-complexity recursive least-squares (RLS) adaptive filter with dichotomous coordinate descent iterations. The experimental results demonstrate that up to 46 dB of SI is cancelled when we use the transmitted digital data as the regressor in the adaptive filter. To improve the SI cancellation performance without introducing high-complexity operation, we use the digitalized power amplifier (PA) output as the regressor to deal with the non-linear distortions caused by the PA in the transmitted chain. With this technique, as high a level as 69 dB of digital SI cancellation is achieved.

In underwater acoustic (UWA) communications, Doppler estimation is one of the major stages in a receiver. Two Doppler estimation methods are often used: cross-ambiguity function (CAF) method and single-branch autocorrelation (SBA) method. The former results in accurate estimation but with a high complexity, whereas the latter is less complicated but also less accurate. In this paper, we propose and investigate a multi-branch autocorrelation (MBA) Doppler estimation method. The proposed method can be used in communication systems with periodically transmitted pilot signals or repetitive data transmission. For comparison of the Doppler estimation methods, we investigate an OFDM communication system in multiple dynamic scenarios using the Waymark simulator, allowing virtual underwater acoustic signal transmission between moving transmitter and receiver. For the comparison, we also use the OFDM signals recorded in a sea trial. The comparison shows that the receiver with the proposed MBA Doppler estimation method outperforms the receiver with the SBA method and its detection performance is close to that of the receiver with the CAF method, but with a significantly lower complexity.

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Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. While wastewater monitoring has been implemented to mitigate outbreak risk in universities and residential settings, its effectiveness in community K-12 sites is unknown. We implemented a wastewater and surface monitoring system to detect SARS-CoV-2 in nine elementary schools in San Diego County. Ninety-three percent of identified cases were associated with either a positive wastewater or surface sample; 67% were associated with a positive wastewater sample, and 40% were associated with a positive surface sample. The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Passive environmental surveillance can complement approaches that require individual consent, particularly in communities with limited access and/or high rates of testing hesitancy.