Background: Sampling the air in indoor congregate settings, where respiratory pathogens are ubiquitous, may constitute a valuable yet underutilised data source for community-wide surveillance of respiratory infections. However, there is a lack of research comparing air sampling and individual sampling of attendees. Therefore, it remains unclear how air sampling results should be interpreted for the purpose of surveillance.
Methods: In this prospective observational study, we compared the presence and concentration of several respiratory pathogens in the air with the number of attendees with infections and the pathogen load in their nasal mucus. Weekly for 22 consecutive weeks, we sampled the air in a single childcare setting in Belgium. Concurrently, we collected the paper tissues used to wipe the noses of 23 regular attendees: children aged zero to three and childcare workers. All samples were tested for 29 respiratory pathogens using PCR.
Findings: Air sampling sensitively detected most respiratory pathogens found in nasal mucus. Some pathogens (SARS-CoV-2, Pneumocystis jirovecii) were found repeatedly in the air, but rarely in nasal mucus, whilst the opposite was true for others (Human coronavirus NL63). All three pathogens with a clear outbreak pattern (Human coronavirus HKU-1, human parainfluenza virus 3 and 4) were found in the air one week before or concurrent with the first detection in paper tissue samples. The presence and concentration of pathogens in the air was best predicted by the pathogen load of the most infectious case. However, air pathogen concentrations also correlated with the number of attendees with infections. Detection and concentration in the air were associated with CO concentration, a marker of ventilation and occupancy.
Interpretation: Our results suggest that air sampling could provide sensitive, responsive epidemiological indicators for the surveillance of respiratory pathogens. Using air CO concentrations to normalise such signals emerges as a promising approach.
Funding: KU Leuven; DURABLE project, under the EU4Health Programme of the European Commission; Thermo Fisher Scientific.
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http://dx.doi.org/10.1016/j.ebiom.2024.105512 | DOI Listing |
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Animal Health Unit, Department of Veterinary Science, School of Veterinary Medicine, Rakuno Gakuen University, Ebetsu, Hokkaido, Japan.
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School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge.
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Department of Respiratory and Critical Care, Haining Branch, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
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Department of Animal Sciences, School of Life Sciences, Central University of Himachal Pradesh, Kangra, 176206, India.
Nanoplastic (NP) pollution poses serious health hazards to aquatic ecosystems, impacting various physiological systems of aquatic organisms. This review examines the complex interplay between NPs and different physiological systems. In the digestive system, NPs downregulate the hsp70-like gene in Mytilus galloprovincialis, leading to decreased metabolic processes and impaired digestion.
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