Global Properties of Secondary DENV Infection Models with Pre-Existing CTL Immunity and Discrete/Distributed Delays
Bibliographic record
Abstract
Dengue, caused by the dengue virus (DENV), is a serious vector-borne disease mainly prevalent in tropical areas. In certain cases, it can lead to death, especially when a person is infected a second time, resulting in a secondary infection. This research begins by presenting an in-host model for secondary DENV infection under the effect of two types of cytotoxic T lymphocytes (CTLs), non-specific and strain-specific CTLs. The first model is incorporating two distinct discrete-time delays. Additionally, the model is refined by integrating two forms of distributed time delays to provide a more realistic representation of secondary DENV infection dynamics. The main objective is to examine the dynamic behavior of both models, including the non-negativity and boundedness of solutions. A qualitative stability analysis is conducted for their steady states, revealing that the uninfected steady state in both models remains globally asymptotically stable when the basic reproduction number (R0) is below one but becomes unstable when R0 exceeds this threshold. Additionally, an infected steady state emerges and is globally asymptotically stable when R0 is greater than one. The stability conditions for the two steady states are determined using the Lyapunov method. To confirm the qualitative results, comprehensive numerical simulations are conducted, offering valuable biological insights. To assess the influence of specific parameters, we conduct a sensitivity analysis on the model. The results indicate that the infection rate and viral production rate significantly impact the sensitivity of R0, ultimately affecting the dynamics of DENV. These insights could contribute to the development of antiviral treatments aimed at inhibiting viral entry and replication. Furthermore, the study explores the impact of time delays on DENV infection dynamics, highlighting that prolonged delays can mimic the effects of antiviral treatments. A sufficiently long delay slows down the virus’s progression, aiding in its control and eventual eradication. These findings suggest potential strategies for developing new treatments that could extend the viral replication or maturation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".