Web searches related to insomnia correlate with the escalation/de-escalation measures enforced during the COVID-19-induced quarantine and lockdown in Italy: insights from a Big Data-based infodemiological survey (Preprint)
Bibliographic record
Abstract
BACKGROUND Since late December, a novel, emerging coronavirus has quickly spread out from the first epicenter, the city of Wuhan, province of Hubei, mainland China, becoming a pandemic. To contain the outbreak unprecedented public health measures have been enforced, including self-isolation, physical/social distancing, quarantine and lockdown of entire communities and territories. Despite being effective, these interventions are imposing a severe psycho-social distress. OBJECTIVE In the present investigation, we assessed the impact of behavioral non pharmacological interventions in terms of digital activities related to sleep disorders and specifically insomnia in Italy, one of the countries hit hard by the pandemic. METHODS We used a Big Data-based infodemiological approach, employing Google Trends, an open source instrument enabling real-time tracking and monitoring of web searches and digital activities. RESULTS We found that, on average, during the three months of quarantine and lockdown, searches related to COVID-19 and insomnia represented 6.8% and 12.4% of the entire search volumes related to COVID-19 and insomnia, respectively, peaking on May 17th 2020. A significant different in the geographic location of searches could be noted. The COVID-19 quarantine has caused an increase in searches related to insomnia. More in detail, the volumes correlated with the escalation of the measures adopted, decreasing during the de-escalation/re-opening phases. CONCLUSIONS Healthcare providers and other relevant stakeholders, when enforcing particularly restrictive measures, should routinely employ Big Data-based tools in order screen for sleep problems such as insomnia and other mental issues, in that sleep is a vital and integral component of human life and normal emotional functioning, and sleep disturbances can lead to or worsen pre-existing mental health diseases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".