Spatial and Temporal Distribution of Aedes Species and Their Infection Status by Dengue and Chikungunya Viruses along the Coastline of Kenya
Bibliographic record
Abstract
A surge in outbreaks of arboviral infections has been documented in spots along Kenyan Coast. However, there is paucity of documented information on distribution of Aedes species involved in transmission of arboviruses and their infection status. This study determined spatial and temporal distribution of Aedes species and their infection status for Dengue (DENV) and Chikungunya (CHIKV) arboviruses along the coastline of Kenya during dry and wet seasons. Indoor and outdoor sampling of adults Aedes species was done using Biogent Sentinel trap baited with solid carbon dioxide and aspiration technique. Samples were identified to sex and species by morphological features. Sites coordinates were noted by GPS with maps drawn using geomap and ggplot packages. RNA from the samples was extracted using Trizole®. cDNA was generated from RNA using one step real time PCR for identification of arboviruses. Proportions of arboviruses were analyzed by R-statistics. A total of 37,220 Aedes mosquitoes were collected and pooled in pools of 20 mosquitoes. Aedes species identified and their respective proportions were: Aedes aegypti formosus (62.5%), Aedes aegypti aegypti (13.2%), Aedes mcintoshi (9.56%), Aedes ochraceus (5.79%), Aedes pembaensis (5.51%), Aedes tricholabis (1.31%), Aedes albicosta (1.11%), Aedes fulgens (0.54%) and Aedes fryeri (0.43%). Aedes aegypti aegypti had not been identified along the coastline in previous studies. The Aedes mosquito sample sizes and their distribution along the coastline were insignificantly different between the two seasons. Vector infections by DENV and CHIKV were insignificantly different between the seasons. In this study, DENV-4 was identified in Aedes species along the coastline of Kenya. Our data confirms the previous reports for increased risk of infection during wet seasons and further identifies other regions with increased risk of arboviral transmission along Coastal Kenya. This information is important as it gives knowledge on areas at high risk for arboviral disease outbreaks in cases where human-vector contacts occur. Consecutively, up scaled survey and implementation of control and prevention measures should be taken appropriately.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".