The effects of weather and mobility on respiratory viruses dynamics before and after the COVID-19 pandemic
varela-lasheras, i.; Perfeito, L.; Mesquita, S.; Goncalves-Sa, J.
Show 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 have similar circulation patterns, and weather has been considered a main driver of their dynamics, with peaks in the winter and almost no circulation during the summer in temperate regions. However, after the emergence of SARS-CoV2, in 2019, the dynamics of these respiratory viruses were strongly perturbed worldwide: some infections almost disappeared, others were delayed or occurred "off-season". This disruption raised questions regarding the dominant role of weather while also providing an unique opportunity to investigate the relevance of different driving factors on the epidemiological dynamics of IVs and NIRVs, including viral interactions, non-pharmacological individual measures (such as masking), or mobility. Here, we use epidemiological surveillance data on several respiratory viruses from Canada and the USA from 2016 to 2023, and tested the effects of weather and mobility in their dynamics before and after the COVID-19 pandemic. Using statistical modelling, we found evidence that whereas in the pre-COVID-19 pandemic period, weather had a strong effect and mobility a limited effect on dynamics; in the post-COVID-19 pandemic period the effect of weather was strongly reduced and mobility played a more relevant role. These results, together with previous studies, indicate that at least some of the behavioral changes resulting from the non-pharmacological interventions implemented during COVID-19 pandemic had a strong effect on the dynamics of respiratory viruses. Furthermore, our results support the idea that these seasonal dynamics are driven by a complex system of interactions between the different factors involved, which probably led to an equilibrium that was disturbed, and perhaps permanently altered, by the COVID-19 pandemic.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Interactions among common non-SARS-CoV-2 respiratory viruses and influence of the COVID-19 pandemic on their circulation in New York City 96%
- The role of viral interference in shaping RSV epidemics following the 2009 H1N1 influenza pandemic 94%
- Evaluating seasonal variations in human contact patterns and their impact on the transmission of respiratory infectious diseases 93%
Similar papers in this journal
- Turnover of SARS-CoV-2 lineages shaped the pandemic and enabled the emergence of new variants in the state of Rio de Janeiro, Brazil 94%
- Characterizing the countrywide epidemic spread of influenza A(H1N1)pdm09 virus in Kenya between 2009 and 2018 93%
- The spread of SARS-CoV-2 variant Omicron with the doubling time of 2.0–3.3 days can be explained by immune evasion 93%
Similar papers in this journal
Similar papers in this journal
- Characterizing Co-Circulating Respiratory Virus Genomic Diversity in Switzerland with Hybrid-Capture Sequencing and Phylogenetic Reconstructions: Insights into the 2023/24 Season 93%
- Characterizing Potential Interaction Between Respiratory Syncytial Virus and Seasonal Influenza in the U.S. 93%
- Population-Level Associations in the Spread of Co-Circulating Respiratory Viruses: A Multi-Method Statistical Investigation Using Incidence Data 92%
Similar papers in this journal
- Colder and drier winter conditions are associated with greater SARS-CoV-2 transmission: a regional study of the first epidemic wave in north-west hemisphere countries 94%
- Emergence of Novel SARS-CoV-2 Variants in the Netherlands 93%
- Predicting hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic in 2020 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.