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The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada

Author

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  • Irma Varela-Lasheras
  • Lilia Perfeito
  • Sara Mesquita
  • Joana Gonçalves-Sá

Abstract

The flu season is caused by a combination of different pathogens, including influenza viruses (IVS), that cause the flu, and non-influenza respiratory viruses (NIRVs), that cause common colds or influenza-like illness. These viruses exhibit similar dynamics and meteorological conditions have historically been regarded as a principal modulator of their epidemiology, with outbreaks in the winter and almost no circulation during the summer, in temperate regions. However, after the emergence of SARS-CoV2, in late 2019, the dynamics of these respiratory viruses were strongly perturbed worldwide: some infections displayed near-eradication, while others experienced temporal shifts or occurred “off-season”. This disruption raised questions regarding the dominant role of weather while also providing an unique opportunity to investigate the roles of different determinants on the epidemiological dynamics of IVs and NIRVs. Here, we employ statistical analysis and modelling to test the effects of weather and mobility in viral dynamics, before and during the COVID-19 pandemic. Leveraging epidemiological surveillance data on several respiratory viruses, from Canada and the USA, from 2016 to 2023, we found that whereas in the pre-COVID-19 pandemic period, weather had a strong effect, in the pandemic period the effect of weather was strongly reduced and mobility played a more relevant role. These results, together with previous studies, indicate that behavioral changes resulting from the non-pharmacological interventions implemented to control SARS-CoV2, interfered with the dynamics of other respiratory viruses, and that the past dynamical equilibrium was disturbed, and perhaps permanently altered, by the COVID-19 pandemic.Author summary: The flu season, which results in millions of cases of severe illness and hundreds of thousands of deaths worldwide, per year, is caused not only by influenza viruses but also by other respiratory viruses that cause similar symptoms. These viruses have similar circulation patterns, with outbreaks in the winter and almost no activity in the summer. Weather has been considered a main driver of their dynamics but, after the start of the COVID-19 pandemic, these dynamics changed so drastically that questions were raised regarding the relative roles of different factors. We used data on several respiratory viruses, from Canada and the USA, and tested the effects of weather and mobility in their dynamics before and during the COVID-19 pandemic. Using statistical modelling, we found that, whereas in the pre-COVID-19 pandemic period weather had a strong effect and mobility a limited effect, in the pandemic period the effect of weather was strongly reduced while mobility played a more relevant role. These results might help us better understand the complex system of interactions between different factors that drive respiratory virus dynamics and have important consequences for public health policies.

Suggested Citation

  • Irma Varela-Lasheras & Lilia Perfeito & Sara Mesquita & Joana Gonçalves-Sá, 2023. "The effects of weather and mobility on respiratory viruses dynamics before and during the COVID-19 pandemic in the USA and Canada," PLOS Digital Health, Public Library of Science, vol. 2(12), pages 1-26, December.
    • Handle: RePEc:plo:pdig00:0000405
    DOI: 10.1371/journal.pdig.0000405
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    References listed on IDEAS

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    1. Edward Goldstein & Sarah Cobey & Saki Takahashi & Joel C Miller & Marc Lipsitch, 2011. "Predicting the Epidemic Sizes of Influenza A/H1N1, A/H3N2, and B: A Statistical Method," PLOS Medicine, Public Library of Science, vol. 8(7), pages 1-12, July.
    2. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    3. Q. Sue Huang & Tim Wood & Lauren Jelley & Tineke Jennings & Sarah Jefferies & Karen Daniells & Annette Nesdale & Tony Dowell & Nikki Turner & Priscilla Campbell-Stokes & Michelle Balm & Hazel C. Dobin, 2021. "Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand," Nature Communications, Nature, vol. 12(1), pages 1-7, December.
    4. Ryusuke Ae & Yoshihide Shibata & Toshiki Furuno & Teppei Sasahara & Yosikazu Nakamura & Hiromichi Hamada, 2022. "Human Mobility and Droplet-Transmissible Pediatric Infectious Diseases during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(11), pages 1-11, June.
    5. Vijaykrishna Dhanasekaran & Sheena Sullivan & Kimberly M. Edwards & Ruopeng Xie & Arseniy Khvorov & Sophie A. Valkenburg & Benjamin J. Cowling & Ian G. Barr, 2022. "Human seasonal influenza under COVID-19 and the potential consequences of influenza lineage elimination," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    6. Simon Cauchemez & Alain-Jacques Valleron & Pierre-Yves Boëlle & Antoine Flahault & Neil M. Ferguson, 2008. "Estimating the impact of school closure on influenza transmission from Sentinel data," Nature, Nature, vol. 452(7188), pages 750-754, April.
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