CLIMATE CHANGE AND DENGUE: ANALYSIS OF HISTORICAL HEALTH AND ENVIRONMENT DATA FOR PERU
Bibliographic record
Abstract
Background and Aims: Dengue, a mosquito-borne virual infection that is the most common cause of hemorrhagic fever globally, is rapidly spreading worldwide. An estimated 40% of the world’s population is at risk for this disease that is transmitted by Aedes sp. mosquitos (WHO 2009). The Aedes mosquito-Dengue virus lifecycle varies with temperature, and climate change may increase the risk of Dengue epidemics in the future (Watts et al. 1987). We examined whether changes in sea surface temperature (SST) along the Peruvian coast were associated with dengue incidence from 2002-2010. In Peru the effects of the El Niño cycle on weather conditions are pronounced, providing an ideal place to study fluctuations in climate and dengue incidence. Methods: We used negative binomial models (Hilbe 2007) to examine the relationship between Dengue cases and changes in SST across regions of Peru. Spearman’s rank test was used to determine the lagged SST term that was most correlated with Dengue incidence in each region. The negative binomial models included terms for tthe optimum lagged SST and a term for the trend of increasing Dengue incidence over the study period. Results: The magnitude and sign of the correlation coefficient of dengue and SST varied between the 15 regions of Peru with Dengue cases. 9 provinces had positive correlations between the two while 6 had negative correlations. The optimum lag ranged from 0 months to 6 months. In all of the regions but 1 lagged SST was a significant predictor of Dengue cases in the negative binomial model. Conclusions: The relationship between dengue and sea surface temperature in Peru appears to be significant across the country. Given the varied nature of the relationship between regions it is not possible to make accurate generalisations about this relationship in Peru. Accounting for additional climatic variables such as precipitation may help in improving the predictive model.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".