SARS‐CoV‐2 and dengue virus co‐infection: Epidemiology, pathogenesis, diagnosis, treatment, and management
Bibliographic record
Abstract
SARS-CoV-2 and dengue virus co-infection cases have been on the rise in dengue-endemic regions as coronavirus disease 2019 (COVID-19) spreads over the world, posing a threat of a co-epidemic. The risk of comorbidity in co-infection cases is greater than that of a single viral infection, which is a cause of concern. Although the pathophysiologies of the two infections are different, the viruses have comparable effects within the body, resulting in identical clinical symptoms in the case of co-infection, which adds to the complexity. Overlapping symptoms and laboratory features make proper differentiation of the infections important. However, specific biomarkers provide precise results that can be utilised to diagnose and treat a co-infection, whether it is simply COVID-19, dengue, or a co-infection. Though their treatment is distinguished, it becomes more complicated in circumstances of co-infection. As a result, regardless of whatever infection the first symptom points to, confirmation diagnosis of both COVID-19 and dengue should be mandatory, particularly in dengue-endemic regions, to prevent health deterioration in individuals treated for a single infection. There is still a scarcity of concise literature on the epidemiology, pathophysiology, diagnosis, therapy, and management of SARS-CoV-2 and dengue virus co-infection. The epidemiology of SARS-CoV-2 and dengue virus co-infection, the mechanism of pathogenesis, and the potential impact on patients are summarised in this review. The possible diagnosis with biomarkers, treatment, and management of the SARS-CoV-2 and dengue viruses are also discussed. This review will shed light on the appropriate diagnosis, treatment, and management of the patients suffering from SARS-CoV-2 and dengue virus co-infection.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".