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We develop a method to compute synthetic kilonova light curves that combine numerical relativity simulations of neutron star mergers and the SNEC radiation–hydrodynamics code. We describe our implementation of initial and boundary conditions, r-process heating, and opacities for kilonova simulations. We validate our approach by carefully checking that energy conservation is satisfied and by comparing the SNEC results with those of two semi-analytic light-curve models. We apply our code to the calculation of colour light curves for three binaries having different mass ratios (equal and unequal mass) and different merger outcome (short-lived and long-lived remnants). We study the sensitivity of our results to hydrodynamic effects, nuclear physics uncertainties in the heating rates, and duration of the merger simulations. We find that hydrodynamics effects are typically negligible and that homologous expansion is a good approximation in most cases. However, pressure forces can amplify the impact of uncertainties in the radioactive heating rates. We also study the impact of shocks possibly launched into the outflows by a relativistic jet. None of our models match AT2017gfo, the kilonova in GW170817. This points to possible deficiencies in our merger simulations and kilonova models that neglect non-LTE effects and possible additional energy injection frommore »the merger remnant and to the need to go beyond the assumption of spherical symmetry adopted in this work.

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Abstract Since the start of the coronavirus disease-2019 (COVID-19) pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve the interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average 6 days (50% uncertainty interval (UI): 6–7; 95% UI: 4–8) following infection, and that wastewater measurements are highly overdispersed [negative binomial dispersion parameter, k = 0.39 (95% credible interval: 0.32–0.48)]. This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA has demonstrated useful correlation with both coronavirus disease 2019 (COVID-19) cases and clinical testing positivity at the community level. Wastewater surveillance on college campuses has also demonstrated promising predictive capacity for the presence and absence of COVID-19 cases. However, to date, such monitoring has most frequently relied upon composite samplers and reverse transcription quantitative PCR (RT-qPCR) techniques, which limits the accessibility and scalability of wastewater surveillance, particularly in low-resource settings. In this study, we trialed the use of tampons as passive swabs for sample collection and reverse transcription loop-mediated isothermal amplification (RT-LAMP), which does not require sophisticated thermal cycling equipment, to detect SARS-CoV-2 RNA in wastewater. Results for the workflow were available within three hours of sample collection. The RT-LAMP assay is approximately 20 times less analytically sensitive than RT-droplet digital PCR. Nonetheless, during a building-level wastewater surveillance campaign concurrent with independent weekly clinical testing of all students, the method demonstrated a three-day positive predictive value (PPV) of 75% (excluding convalescent cases) and same-day negative predictive value (NPV) of 80% for incident COVID-19 cases. These predictive values are comparable to that reported by wastewater monitoring using RT-qPCR. These observationsmore »suggest that even with lower analytical sensitivity the tampon swab and RT-LAMP workflow offers a cost-effective and rapid approach that could be leveraged for scalable building-level wastewater surveillance for COVID-19 potentially even in low-resource settings.« less

Human exposure to pathogenic viruses in environmental waters results in a significant global disease burden. Current microbial water quality monitoring approaches, mainly based on fecal indicator bacteria, insufficiently capture human health impacts posed by pathogenic viruses in water. The emergence of the ‘microbiome era’ and high-throughput metagenome sequencing has led to the discovery of novel human-associated viruses, including both pathogenic and commensal viruses in the human microbiome. The discovery of novel human-associated viruses is often followed by their detection in wastewater, highlighting the great diversity of human-associated viruses potentially present in the water environment. Novel human-associated viruses provide a rich reservoir to develop viral water quality management tools with diverse applications, such as regulating wastewater reuse and monitoring agricultural and recreational waters. Here, we review the pathway from viral discovery to water quality monitoring tool, and highlight select human-associated viruses identified by metagenomics and subsequently detected in the water environment (namely Bocavirus, Cosavirus, CrAssphage, Klassevirus, and Pepper Mild Mottle Virus). We also discuss research needs to enable the application of recently discovered human-associated viruses in water quality monitoring, including investigating the geographic distribution, environmental fate, and viability of potential indicator viruses. Examples suggest that recently discovered human pathogens aremore »likely to be less abundant in sewage, while other human-associated viruses (e.g., bacteriophages or viruses from food) are more abundant but less human-specific. The improved resolution of human-associated viral diversity enabled by metagenomic tools provides a significant opportunity for improved viral water quality management tools.

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