Application of Chebyshev Polynomial-Exponential Method and Tamimi-Ansari Method in Dengue Transmission Dynamics: A Comparative Study
Bibliographic record
Abstract
Dengue virus transmitted by mosquitoes, poses a significant global health threat, affecting millions of people annually. In this paper, we explore the dynamics of a dengue virus transmission model, structured as an epidemiological mathematical framework. The model divides the total population into seven compartments: susceptible humans S(t), exposed humans E(t), infected humans I(t), recovered humans R(t), susceptible mosquitoes M(t), exposed mosquitoes ME(t), and infected mosquitoes MI(t). We employed the Chebyshev polynomial-exponential method (CPEM) and Tamimi-Ansari method (TAM) to conduct an in-depth semi-analytical examination of this model. The numerical simulation using MATLAB® ode45 solver was used to compare the results with CPEM and TAM, validating the accuracy and effectiveness of the obtained solutions. The comparison shows no significant differences between the CPEM with numerical results, which leads to a interesting findings. Additionally, by varying the sensitive parameters, we analyzed the behavior of the different compartments within the model. This investigation provides valuable insights into the responses of dengue transmission under various conditions, demonstrating the potential of novel semi-analytical methods for studying epidemiological models of infectious diseases, which is highly beneficial for researchers in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| 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".