The role of temperature on the global spread of COVID‑19 and urgent solutions
Bibliographic record
Abstract
This study explored whether the global temperature had any role in the spread and vulnerability to COVID-19 and how that knowledge can be used to arrest the fast-spreading disease. It highlighted that for transmitting the virus, global temperature played an important role and a moderately cool environment was the most favourable state. The risk from the virus was reduced significantly for warm places and countries. Based on the temperature of March and April, various degrees of vulnerability were identified and countries were specified. The maximum reported case, as well as death, was noted when the temperature was in the range of around 275 °K (2 °C) to 290 °K (17 °C). Countries like the USA, UK, Italy and Spain belonged to this category. The vulnerability was moderate when the temperature was less than around 275 °K (2 °C), e.g. Russia, parts of Canada and a few Scandinavian countries. For temperature 300 °K (27 °C) and above, a significantly lesser degree of vulnerability was noted. Countries from South Asian Association for Regional Cooperation, South-East Asia, the African continent and Australia fell in that category. This work discussed that based on the variability of temperature, countries can switch from one vulnerability state to another. That influence of temperature on the virus and results of previous clinical trials with similar viruses provided a useful insight that regulating the level of temperature can offer remarkable results to arrest and stop the outbreak. Based on that knowledge, some urgent and simple solutions are proposed, which are practically without side effects and very cost-effective too.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".