ArticlePathogens (Basel, Switzerland)2021
Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors.
Article in Pathogens (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Global meta-analysis of short-term associations between ambient temperature and pathogen-specific respiratory infections, 2004 to 2023.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2025Pooled it
- Seasonal Patterns of Common Respiratory Viral Infections in Immunocompetent and Immunosuppressed Patients.Pathogens (Basel, Switzerland) · 2024Article
- The association of COVID-19 incidence with temperature, humidity, and UV radiation - A global multi-city analysis.The Science of the total environment · 2023Article
- The benefits, costs and feasibility of a low incidence COVID-19 strategy.The Lancet regional health. Europe · 2022Review
- Extreme Precipitation Events and Infectious Disease Risk: A Scoping Review and Framework for Infectious Respiratory Viruses.International journal of environmental research and public health · 2021Article
- A look into the future of the COVID-19 pandemic in Europe: an expert consultation.The Lancet regional health. Europe · 2021Review
- Occurrence of COVID-19 Symptoms During SARS-CoV-2 Infection Defines Waning of Humoral Immunity.Frontiers in immunology · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeasonality is a characteristic of some respiratory viruses. The aim of our study was to evaluate the seasonality and the potential effects of different meteorological factors on the detection rate of the non-SARS coronavirus detection by PCR.
methodsWe performed a retrospective analysis of 12,763 respiratory tract sample results (288 positive and 12,475 negative) for non-SARS, non-MERS coronaviruses (NL63, 229E, OC43, HKU1). The effect of seven single weather factors on the coronavirus detection rate was fitted in a logistic regression model with and without adjusting for other weather factors.
resultsCoronavirus infections followed a seasonal pattern peaking from December to March and plunged from July to September. The seasonal effect was less pronounced in immunosuppressed patients compared to immunocompetent patients. Different automatic variable selection processes agreed on selecting the predictors temperature, relative humidity, cloud cover and precipitation as remaining predictors in the multivariable logistic regression model, including all weather factors, with low ambient temperature, low relative humidity, high cloud cover and high precipitation being linked to increased coronavirus detection rates.
conclusionsCoronavirus infections followed a seasonal pattern, which was more pronounced in immunocompetent patients compared to immunosuppressed patients. Several meteorological factors were associated with the coronavirus detection rate. However, when mutually adjusting for all weather factors, only temperature, relative humidity, precipitation and cloud cover contributed independently to predicting the coronavirus detection rate.
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