The effects of weather and mobility on respiratory viruses before and during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background The flu season is caused by a combination of different pathogens, including influenza viruses, which cause the flu, and non-influenza respiratory viruses, that cause common colds or influenza-like illness. These viruses exhibit similar dynamics and, given that outbreaks occur mostly in the winter and there is almost no circulation during the summer, in temperate regions, meteorological conditions have historically been regarded as a principal modulator of their epidemiology. 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 other determinants in their epidemiological dynamics. Methods Weather, mobility and epidemiological surveillance data was collected for Influenza, RSV, hCOV and hMPV, from Canada and the USA, from 2016 to 2023. Statistical analysis and modeling were employed to test the effects of weather and mobility on viral dynamics, before and during the COVID-19 pandemic. Results Using Beta Regressions, we found that whereas in the pre-COVID-19 pandemic period, weather had a strong effect, in the pandemic period, this effect was strongly reduced post-pandemic with mobility playing a more significant role. Conclusions These results, together with previous studies, dispute the general belief that respiratory viral dynamics are mostly dictated by weather and indicate that behavioral changes resulting from the non-pharmacological interventions implemented to control SARS-CoV2, played a key role. This disruption of past dynamical equilibrium raises important questions regarding the factors that modulate them, particularly in a context of climate change. Key messages • We took advantage of the disruption caused by COVID-19 to study dynamics of other respiratory viruses and to disentangle the effects of weather from those of human behavior. • While before 2020 cold temperatures were highly correlated with incidence, afterwards cold weather was no longer a necessary condition and human mobility became central.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".