Global risk mapping for major diseases transmitted by Aedes aegypti and Aedes albopictus
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to map the global risk of the major arboviral diseases transmitted by Aedes aegypti and Aedes albopictus by identifying areas where the diseases are reported, either through active transmission or travel-related outbreaks, as well as areas where the diseases are not currently reported but are nonetheless suitable for the vector. METHODS: Data relating to five arboviral diseases (Zika, dengue fever, chikungunya, yellow fever, and Rift Valley fever (RVF)) were extracted from some of the largest contemporary databases and paired with data on the known distribution of their vectors, A. aegypti and A. albopictus. The disease occurrence data for the selected diseases were compiled from literature dating as far back as 1952 to as recent as 2017. The resulting datasets were aggregated at the country level, except in the case of the USA, where state-level data were used. Spatial analysis was used to process the data and to develop risk maps. RESULTS: Out of the 250 countries/territories considered, 215 (86%) are potentially suitable for the survival and establishment of A. aegypti and/or A. albopictus. A. albopictus has suitability foci in 197 countries/territories, while there are 188 that are suitable for A. aegypti. There is considerable variation in the suitability range among countries/territories, but many of the tropical regions of the world provide high suitability over extensive areas. Globally, 146 (58.4%) countries/territories reported at least one arboviral disease, while 123 (49.2%) reported more than one of the above diseases. The overall numbers of countries/territories reporting autochthonous vector-borne occurrences of Zika, dengue, chikungunya, yellow fever, and RVF, were 85, 111, 106, 43, and 39, respectively. CONCLUSIONS: With 215 countries/territories potentially suitable for the most important arboviral disease vectors and more than half of these reporting cases, arboviral diseases are indeed a global public health threat. The increasing proportion of reports that include multiple arboviral diseases highlights the expanding range of their common transmission vectors. The shared features of these arboviral diseases should motivate efforts to combine interventions against these diseases.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| 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".