Do the weather parameters have influences on the proliferation of Covid-19? An analysis based on metrological reports of geographically different eight regions over the globe.
Bibliographic record
Abstract
A contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is known as Coronavirus disease 2019 (Covid-19). It engendered the whole civilization within a couple of months over the globe since it was first detected in Wuhan, China in late December 2019. Variation of proliferation rates in different regions assume that climatic parameters might have a vital role in Covid-19 transmission. In this study, the correlation between Covid-19 proliferation with demographic parameter (population density), and weather parameters (temperature, humidity, precipitation, wind speed, and sunshine hour) were investigated separately within the first 60 days of Covid-19 cases. To obtain a precedent correlation, weather and infection-related data of eight different geographically coordinated regions such as Alberta (Canada), Barcelona (Spain), Dhaka (Bangladesh), Île-de-France (France), Lombardy (Italy), New York (USA), Rio de Janeiro (Brazil) and West Bengal (India) having the diversity of climates were considered. It was observed that less densely populated regions (New York, Lombardy, Barcelona) were even highly affected than the highly populated regions like Bangladesh, West Bengal. A negative correlation between total cases and temperature perhaps made this difference. The higher the wind speed perhaps accountable for long-distance viral transmission. The non-steady humidity tentatively makes the people vulnerable towards Covid-19 infections. Higher precipitation may positively affect viral infection. Sunshine along with the higher temperatures are suspected to impede the contagion by Covid-19. Consequently, peoples in the regions of lower temperatures, higher wind speed, and unstable humidity have higher risks of Covid-19 infection.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".