Observational studyCurrent microbiology2025
Decoding Weather Fingerprints of Upper Respiratory Pathogens: A One-year Observational Study in Northern Greece.
Observational study in Current microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
abstactRespiratory infections demonstrate characteristic seasonal variations influenced by environmental factors; however, the specific meteorological determinants for individual pathogens remain insufficiently defined. This retrospective study investigated associations between weather parameters and the prevalence of upper respiratory pathogens in northern Greece from December 2023 to November 2024. Nasopharyngeal specimens collected from patients at AHEPA University Hospital of Thessaloniki with suspected infections were analyzed using multiplex polymerase chain reaction (PCR). Meteorological data were incorporated through a novel timeweighted approach that aligned weather data with pathogen-specific incubation periods. Among 1,754 samples analyzed, winter exhibited the highest positivity rate. Distinct seasonal and meteorological relationships were identified across pathogens. Influenza A was associated with colder and more humid conditions, Adenovirus predominated in warmer periods, and other viruses displayed year-round or seasonally limited circulation. Distinct correlation profiles were also observed, including variations in temperature-humidity relationships among specific pathogens. These findings reveal pathogen-specific meteorological signatures extending beyond conventional seasonality and highlight the utility of time-weighted environmental modeling for improving predictive surveillance and public health preparedness.
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