The effects of weather and mobility on respiratory viruses dynamics before and after the COVID-19 pandemic
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".