A mathematical model of Zika disease by considering transition from the asymptomatic to symptomatic phase
Bibliographic record
Abstract
Abstract This work presents a mathematical model of Zika disease considering infected individual transition from the asymptomatic to symptomatic phase. Zika virus (ZIKV) itself is a virus that belongs to arbovirus transmitted by the Aedes aegypti mosquitoes. It can also be transmitted through human contact such as sexual contact, blood transfussion, and transplacental infection. As a matter of fact, 80% of those who get infected by ZIKV are asymptomatic. In this work, we investigate the Zika model by considering individual transition case from the asymptomatic to symptomatic phase using SEAIR (host) - SI (vector) model. In this model, we involve human and mosquito populations which have a big role to the transmission of ZIKV itself. In this study, basic reproduction number (R 0) calculated as the largest eigenvalue of Next-Generation Matrix. Furthermore, analytical results also be conducted to determine the existence and local stability of the equilibrium point. A numerical simulation presented to analyze the sensitivity and elasticity of R 0 for some parameters involved in the model, and followed with simulation of autonomous system. We find that transition of asymptomatic to symptomatic case in Zika transmission hold an important role in determining the size of the basic reproduction number. More transition to symptomatic case are better to know the “dark” figure of the real cases in the field.
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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 | 0.001 | 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) | 0.004 | 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".