Development of a COVID-19 warning system for neighborhood-scale wastewater-based epidemiology in low incidence situations

Wastewater-based epidemiology (WBE), an emerging approach for community-wide COVID-19 surveillance, was primarily characterized at large sewersheds such as wastewater treatment plants serving a large population. Although informed public health measures can be better implemented for a small population, WBE for neighborhood-scale sewersheds is less studied and not fully understood. This study applied WBE to seven neighborhood-scale sewersheds (average population of 1,471) from January to November, 2021. Community testing data showed an average of 0.004% incidence rate in these sewersheds (97% of monitoring periods reported two or fewer daily infections). In 92% of sewage samples, SARS-CoV-2 N gene fragments were below the limit of quantification. We statistically determined 10-2.6 as the threshold of the SARS-CoV-2 N gene concentration normalized to pepper mild mottle virus (N/PMMOV) to alert high COVID-19 incidence rate in the studied sewershed. This threshold of N/PMMOV identified neighborhood-scale outbreaks (COVID-19 incidence rate higher than 0.2%) with 82% sensitivity and 51% specificity. Importantly, neighborhood-scale WBE can discern local outbreaks that would not otherwise be identified by city-scale WBE. Our findings suggest that neighborhood-scale WBE is an effective community-wide disease surveillance tool when COVID-19 incidence is maintained at a low level. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/22276055v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1a7c112org.highwire.dtl.DTLVardef@74b1b3org.highwire.dtl.DTLVardef@13e82e8org.highwire.dtl.DTLVardef@1045944_HPS_FORMAT_FIGEXP M_FIG C_FIG

Regional and temporal variations affect the accuracy of variant-specific SARS-CoV-2 PCR assays

Monitoring the prevalence of SARS-CoV-2 variants is necessary to make informed public health decisions during the COVID-19 pandemic. PCR assays have received global attention, facilitating rapid understanding of variant dynamics because they are more accessible and scalable than genome sequencing. However, as PCR assays target only a few mutations, their accuracy could be compromised when these mutations are not exclusive to target variants. Here we show how to design variant-specific PCR assays with high sensitivity and specificity across different geographical regions by incorporating sequences deposited in the GISAID database. Furthermore, we demonstrate that several previously developed PCR assays have decreased accuracy outside their study areas. We introduce PRIMES, an algorithm that enables the design of reliable PCR assays, as demonstrated in our experiments to track dominant SARS-CoV-2 variants in local sewage samples. Our findings will contribute to improving PCR assays for SARS-CoV-2 variant surveillance. ImportanceMonitoring the introduction and prevalence of variants of concern (VOCs) and variants of interest (VOIs) in a community can help the local authorities make informed public health decisions. PCR assays can be designed to keep track of SARS-CoV-2 variants by measuring unique mutation markers that are exclusive to the target variants. However, the mutation markers can not be exclusive to the target variants depending on regional and temporal differences in variant dynamics. We introduce PRIMES, an algorithm that enables the design of reliable PCR assays for variant detection. Because PCR is more accessible, scalable, and robust to sewage samples over sequencing technology, our findings will contribute to improving global SARS-CoV-2 variant surveillance.

Rapid SARS-CoV-2 Detection and Classification Using Phase Imaging with Computational Specificity

Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical pathlength sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A), HAdV (adenovirus), and ZIKV (Zika). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically. One Sentence SummaryThis work proposes a rapid (<1 min.), label-free testing method for SARS-CoV-2 detection, using quantitative phase imaging and deep learning.

Converting Microwave Ovens into Plasma-Generating Decontamination Units for N-95 Respirators

We show how a common microwave oven, a coat-hanger and a coffee cup can be used to decontaminate N-95 respirators in 30 seconds. Tulane virus in artificial saliva was reduced by >3 log and Geobacillus stearothermophilus spores were reduced by >6 log. Respirators maintained filtration and fitting after 10 cycles.