Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Feb 9;10(2):187.
doi: 10.3390/pathogens10020187.

Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors

Olympia E Anastasiou  1 , Anika Hüsing  2 , Johannes Korth  3 , Fotis Theodoropoulos  4 , Christian Taube  4 , Karl-Heinz Jöckel  2   5 , Andreas Stang  2 , Ulf Dittmer  1
Affiliations

Seasonality of Non-SARS, Non-MERS Coronaviruses and the Impact of Meteorological Factors

Olympia E Anastasiou et al. Pathogens. .

Abstract

Background: Seasonality 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.

Methods: We 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.

Results: Coronavirus 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.

Conclusions: Coronavirus 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.

Keywords: coronavirus; immunosuppression; meteorological; seasonality; weather.

PubMed Disclaimer

Conflict of interest statement

OEA BMS, research grant from Hexal, all unrelated to the submitted work, AH nothing to declare, JK grants speaker’s fee, honoraria and travel expenses from Astellas, Basilea, Chiesi, Janssen, Novartis and Roche, all unrelated to the submitted work, FT nothing to declare, CT nothing to declare, KHJ nothing to declare, AS nothing to declare, UD nothing to declare.

Figures

Figure 1
Figure 1
Coronavirus infections followed a seasonal pattern. The needles in the A and B panels show the monthly detection rate. The curves in panels A and B depict the models describing a seasonality effect (a) and a combined effect of seven weather factors (daily average ambient temperature, relative humidity, wind speed, cloud cover, atmospheric pressure, precipitation and number of sunlight hours) (b) on coronavirus detection rate, respectively. Both models had an adequate fit; however, a better model fit was obtained using seasonality to predict virus detection rates.
Figure 2
Figure 2
Logistic regression models were calculated for each of the weather factors with and without adjusting for other weather factors and/or seasonality. OR: odds ratio; CI: confidence interval. Temp5: daily average temperature (per 5 °C change); rhum5: daily average relative humidity (per 5% change); wind: daily average wind speed (per 1m/s change); press5: daily average atmospheric pressure (per 5hPa change); ocast: daily average cloud cover; sun: sunlight hours daily; rain5: precipitation daily (per 5 mm change).
Figure 3
Figure 3
Effect of weather factors on coronavirus detection rate according to a reduced logistic regression model of the combined effect of weather factors. OR: odds ratio; CI: confidence interval. Temp5: daily average temperature (per 5 °C change); rhum5: daily average relative humidity (per 5% change); ocast: daily average cloud cover; rain5: precipitation daily (per 5 mm change).
Figure 4
Figure 4
The seasonality and weather influence on coronavirus detection rates was compared between immunosuppressed and immunocompetent patients. Logistic regression models were calculated for the effect of seasonality (a) and the effect of selected weather factors (b) on the coronavirus detection rate in immunosuppressed and immunocompetent patients. OR: odds ratio; CI: confidence interval; Temp5: daily average temperature (per 5 °C change); rhum5: daily average relative humidity (per 5% change); ocast: daily average cloud cover; rain5: precipitation daily (per 5 mm change); season: grand average year-specific seasonal effect.
See this image and copyright information in PMC

References

    1. WHO . Global Health Estimates 2016: Disease burden by Cause, Age, Sex, by Country and by Region, 2000–2016. World Health Organization; Geneva, Switzerland: 2018.
    1. Dandachi D., Rodriguez-Barradas M.C. Viral pneumonia: Etiologies and treatment. J. Investig. Med. 2018;66:957. doi: 10.1136/jim-2018-000712. - DOI - PubMed
    1. Stang A., Standl F., Jöckel K.-H. Characteristics of COVID-19 pandemic and public health consequences. Herz. 2020;45:313–315. doi: 10.1007/s00059-020-04932-0. - DOI - PMC - PubMed
    1. Welfens P.J.J. Macroeconomic and health care aspects of the coronavirus epidemic: EU, US and global perspectives. Int. Econ. Econ. Policy. 2020:1–68. doi: 10.1007/s10368-020-00465-3. - DOI
    1. Heikkinen T., Jarvinen A. The common cold. Lancet. 2003;361:51–59. doi: 10.1016/S0140-6736(03)12162-9. - DOI - PMC - PubMed

LinkOut - more resources