Bayesian Inference and Sensitivity Analysis of Dengue Transmission in Sudan
Bibliographic record
Abstract
Background: Dengue fever is a significant public health concern in Sudan as well as tropical regions. Mathematical and statistical methodologies are crucial for comprehending its transmission dynamics and informing effective control tactics. Methods: We developed a two-population compartmental model to capture dengue transmission between humans (susceptible, infected, recovered and disease- induced mortality) and mosquito vectors (susceptible and infected). Using the next-generation matrix approach, we derive an explicit expression for the basic re- production number (R0). For the assessment of critical epidemiological parameters such as the mosquito biting rate, probability of human to vector transmission, recovery rate, and dengue-induced fatality rate, Bayesian inference was employed. To evaluate the robustness of these findings, a global sensitivity analysis was performed utilizing Latin hypercube sampling and partial rank correlation coefficients. Results: Posterior estimates indicated R0 1.25 (95% credible interval: 1.11– 1.40), with the model showing strong agreement with case report data (R2 = 0.93). Sensitivity analysis showed that the mosquito biting rate as well as the transmission probability were the main drivers of epidemic potential with recovery and dengue- induced mortality exhibiting inhibiting negative effects on transmission. Conclusions: The results suggest that transmissible vector factors are an important component for dengue transmission in East Sudan. The preferred method for the control of future outbreaks is expected to concentrate on mosquito bites/human vector transmission.
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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 | 0.052 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".