Molecular insights into Aedes aegypti (L.) populations and vector surveillance in the urban areas of Jeddah and Jizan, Saudi Arabia
Bibliographic record
Abstract
Introduction The Aedes aegypti constitutes the primary vector for dengue fever, yellow fever, chikungunya, Zika, West Nile, and encephalitis viruses, all of which have impacted One Health (human, animal, and environmental health) significantly. It has been distributed widely in urban settings in Saudi Arabia, particularly in cities like Jeddah and Jizan, a situation that underscores the urgent need for innovative and sustainable vector control strategies. Molecular tools, such as DNA barcoding using mitochondrial markers, have become essential for identifying mosquito species accurately and understanding their role in disease transmission. Such knowledge is vital for informing effective, climate-resilient public health interventions. Methods This research focuses on identifying Aedes species in various regions of Saudi Arabia using polymerase chain reaction (PCR) and sequencing techniques, in order to evaluate the molecular diversity of these dengue vectors in Jeddah and Jizan. The study utilizes the cytochrome one oxidase (COI) gene as a molecular marker for phylogenetic analysis to compare the populations of Aedes species. Results The findings reveal the presence of significant genetic variation among mosquito populations. In the Jeddah region, the Ae. aegypti types MN299016.1 and KU495081.1 match completely (100%) the respective populations found in Argentina and Australia, with 93.1% (27/29) and 6.9% (2/29) respectively. Meanwhile, the samples from the Jizan region are 100% and 99.6% similar to the Ae. aegypti types MN298998.1, MK300226.1, PP892777.1, and MF043259.1 found in Canada, Kenya, India, and England. Conclusion This study underscores the necessity of using molecular techniques in vector surveillance to mitigate the spread of Zoonotic and vector borne diseases in Saudi Arabia. Moreover, these efforts align with the objectives of the Saudi Vision 2030 by promoting environmentally responsive vector surveillance in Jeddah and Jizan, thereby supporting integrated approaches to public health and ecological sustainability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".