Modelling <i>Trypanosoma cruzi</i>-<i>Trypanosoma rangeli</i> co-infection and pathogenic effect on Chagas disease spread
Bibliographic record
Abstract
A mathematical model is developed to investigate the impact of Trypanosoma cruzi and Trypanosoma rangeli co-infection and Trypanosoma rangeli-induced pathogenicity of triatomine bugs on the spread of Chagas disease. Due to the presence of two parasites, basic reproduction numbers of one parasite in the absence of the other parasite ( \begin{document}$ \mathcal{R}_{10} $\end{document} and \begin{document}$ \mathcal{R}_{20} $\end{document} ) and invasion reproduction numbers of one parasite invading the other parasite ( \begin{document}$ \mathcal{R}_{12} $\end{document} and \begin{document}$ \mathcal{R}_{21} $\end{document} ) are derived to determine the dynamics of the co-infection system. With a simple case of two parasites' independent transmission, we have found that both parasites go extinct if both \begin{document}$ \mathcal{R}_{i0}<1\,(i=1,2) $\end{document} , thus no Chagas disease spread. Nevertheless, the condition of \begin{document}$ \mathcal{R}_{i0}>1\,(i=1,2) $\end{document} is not sufficient to cause Chagas disease persistence, the invasion reproduction number of Trypanosoma cruzi invading Trypanosoma rangeli transmission \begin{document}$ \mathcal{R}_{12} $\end{document} plays an important role. Specifically, Chagas disease could go extinct if \begin{document}$ \mathcal{R}_{12}<1 $\end{document} , and uniformly persistent if \begin{document}$ \mathcal{R}_{12}>1 $\end{document} . Moreover, due to pathogenicity, oscillation pattern of Chagas disease is observed, which is different from other mechanisms such as maturation delay, seasonality and regular spraying with insecticides for vector control. In conclusion, we have found that the presence of Trypanosoma rangeli infection leads to the risk reduction of Chagas disease infection. Our findings are beneficial to the prevention and control of Chagas disease.
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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 | 0.000 | 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) | 0.008 | 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".