Efficient Detection of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) from Exhaled Breath.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is transmitted through airborne particles in exhaled breath, causing severe respiratory disease, coronavirus disease-2019 (COVID-19), in some patients. Samples for SARS-CoV-2 testing are typically collected by nasopharyngeal swab, with the virus detected by PCR; however, patients can test positive for 3 months after infection. Without the capacity to assay SARS-CoV-2 in breath, it is not possible to understand the risk for transmission from infected individuals. To detect virus in breath, the Bubbler-a breathalyzer that reverse-transcribes RNA from SARS-CoV-2 particles into a sample-specific barcoded cDNA-was developed. In a study of 70 hospitalized patients, the Bubbler was both more predictive of lower respiratory tract involvement (abnormal chest X-ray) and less invasive than alternatives. Samples tested using the Bubbler were threefold more enriched for SARS-CoV-2 RNA than were samples from tongue swabs, implying that virus particles were being directly sampled. The barcode-enabled Bubbler was used for simultaneous diagnosis in large batches of pooled samples at a lower limit of detection of 334 genomic copies per sample. Diagnosis by sequencing can provide additional information, such as viral load and strain identity. The Bubbler was configured to sample nucleic acids in water droplets circulating in air, demonstrating its potential in environmental monitoring and the protective effect of adequate ventilation.

Evaluating Closures of Fresh Fruit and Vegetable Vendors During the COVID-19 Pandemic: Methodology and Preliminary Results Using Omnidirectional Street View Imagery.

BACKGROUND: The COVID-19 pandemic has significantly disrupted the food retail environment. However, its impact on fresh fruit and vegetable vendors remains unclear; these are often smaller, more community centered, and may lack the financial infrastructure to withstand supply and demand changes induced by such crises. OBJECTIVE: This study documents the methodology used to assess fresh fruit and vegetable vendor closures in New York City (NYC) following the start of the COVID-19 pandemic by using Google Street View, the new Apple Look Around database, and in-person checks. METHODS: In total, 6 NYC neighborhoods (in Manhattan and Brooklyn) were selected for analysis; these included two socioeconomically advantaged neighborhoods (Upper East Side, Park Slope), two socioeconomically disadvantaged neighborhoods (East Harlem, Brownsville), and two Chinese ethnic neighborhoods (Chinatown, Sunset Park). For each neighborhood, Google Street View was used to virtually walk down each street and identify vendors (stores, storefronts, street vendors, or wholesalers) that were open and active in 2019 (ie, both produce and vendor personnel were present at a location). Past vendor surveillance (when available) was used to guide these virtual walks. Each identified vendor was geotagged as a Google Maps pinpoint that research assistants then physically visited. Using the "notes" feature of Google Maps as a data collection tool, notes were made on which of three categories best described each vendor: (1) open, (2) open with a more limited setup (eg, certain sections of the vendor unit that were open and active in 2019 were missing or closed during in-person checks), or (3) closed/absent. RESULTS: Of the 135 open vendors identified in 2019 imagery data, 35% (n=47) were absent/closed and 10% (n=13) were open with more limited setups following the beginning of the COVID-19 pandemic. When comparing boroughs, 35% (28/80) of vendors in Manhattan were absent/closed, as were 35% (19/55) of vendors in Brooklyn. Although Google Street View was able to provide 2019 street view imagery data for most neighborhoods, Apple Look Around was required for 2019 imagery data for some areas of Park Slope. Past surveillance data helped to identify 3 additional established vendors in Chinatown that had been missed in street view imagery. The Google Maps "notes" feature was used by multiple research assistants simultaneously to rapidly collect observational data on mobile devices. CONCLUSIONS: The methodology employed enabled the identification of closures in the fresh fruit and vegetable retail environment and can be used to assess closures in other contexts. The use of past baseline surveillance data to aid vendor identification was valuable for identifying vendors that may have been absent or visually obstructed in the street view imagery data. Data collection using Google Maps likewise has the potential to enhance the efficiency of fieldwork in future studies.