Identification of arboviruses in mosquito populations in KwaZulu-Natal, South Africa and the first record of Wyeomyia mitchellii in the Old World
Bibliographic record
Abstract
Mosquito-borne viruses have the potential to spread and cause outbreaks with significant public and veterinary health consequences. Although historically a significant number of arboviruses were identified in South Africa with potential to cause sporadic outbreaks, there is limited information on the current situation in some regions of the country. Hence a study was initiated to investigate which arboviruses are currently circulating within mosquito populations in a major metropolitan area, eThekwini, KwaZulu-Natal Province. Mosquitoes were collected from seven sites throughout the metropole and a subset were screened for arboviruses from the families Togaviridae, Phenuiviridae and Peribunyaviridae. The subset of 1831 mosquitoes were collected between October 2020 and July 2021, identified morphologically, and pooled according to species, collection site and collection date. RNA was extracted from a total of 261 mosquito pools and screened using in-house nested and hemi-nested reverse transcriptase polymerase chain reaction (RT-PCR). Primers targeting conserved genes for each viral genus were used in a nested or hemi-nested two-step RT-PCR. Amplicons were sequenced to determine the virus species. Arboviral RNA was detected from 15/261 mosquito pools. The amplicons were subsequently sequenced using the Oxford Nanopore MinION. The positive samples included a Sindbis virus (SINV) isolate, three isolates of Witwatersrand virus (WITV), and 11 isolates of Bunyamwera virus (BUNV). Phylogenetic analysis of partial sequence data suggested that none were newly introduced but closely related isolates previously detected in the country. SINV is known to cause outbreaks of human disease after heavy rainfall, favoring an increase in mosquito populations. Bunyamwera virus has been associated with human febrile disease, but severe disease and regular outbreaks have not been reported previously and requires further investigation. The medical significance of WITV is currently unknown. Wyeomyia mitchellii, a New World species, is for the first time confirmed as an introduced species in South Africa and highlights the importance of vector surveillance. Identification of circulating viruses and raising the awareness of the presence of these viruses is important for early detection and determining the public health significance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".