Data analysis of COVID-19 wave peaks in relation to latitude and temperature for multiple nations
Bibliographic record
Abstract
Abstract It was observed that the multiple peaks of coronavirus disease-19 (COVID-19) appeared in different seasons in different countries. There were countries where the COVID-19 peak occurred during extremely low temperatures, such as Norway, Canada and on the other hand there were countries with high-temperature ranges such as Brazil, India, UAE. Most of the high-latitude countries received their outbreak in winter and most of the countries near the equator mark the outbreak during the summer. Most of the biological organisms have their growth dependant on the temperature, and hence we explored that if there is any relation of temperature versus COVID-19 outbreak in the particular country. It was also seen that people are not behaving differently during the peak of the COVID-19 wave, hence it was important to know whether the COVID-19 virus has evolved or the global temperature variation caused these multiple peaks. This work focuses on finding the effect of temperature variation on the COVID-19 outbreak. We used Levenberg–Marquardt technique to find the correlation between the temperature at which COVID-19 outbreak peaks and the latitude of the particular country. We found that between the temperature range of 14 ° C to 20 ° C spread of the COVID-19 is minimal. Based on our results we can also say that the COVID-19 outbreak is seen in lower temperature (0 ° C to 13 ° C) ranges as well as in the higher temperature ranges (21 ° C to 35 ° C). The current data analysis will help the authorities to manage their resources in advance to prepare for any further outbreaks that might occur in the COVID-19 or even in the next 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".