ArticleBMC public health2026
Dual-Model framework for CHIKV transmission modeling: ODE and Petri Net Analysis of the 2025 Foshan outbreak.
Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
This study addresses the 2025 Chikungunya outbreak in Foshan City, Guangdong Province, China, by constructing a dual-model framework based on Ordinary Differential Equations (ODE) and Petri Nets (PN) for comparative analysis of Chikungunya transmission dynamics and reproduction number estimation methods. The research employs SEICR (Susceptible-Exposed-Infectious-Chronic-Recovered) compartmental modeling to compare two formal representations under matched epidemiological assumptions, and evaluates the timing of epidemic control measures through a three-phase intervention fitting protocol. Model validation results show that both models achieve root mean square errors (RMSE) of 30.98 (ODE) and 31.05 (PN), mean absolute errors (MAE) of 15.57 and 15.78, and [Formula: see text] and 0.9498, respectively. Both models predict epidemic peaks at day 33 (406 cases), occurring 3 days earlier than the observed peak (432 cases), with a peak value error of 6.0%. Residual analysis reveals that negative residuals account for 71.4% (ODE) and 73.8% (PN) of the observation-window residuals, suggesting a structured overprediction pattern in descriptive diagnostics. Reproduction number analysis reveals that the initial transmission indicators are approximately 14.67 (ODE)/13.90 (PN), with effective values progressively decreasing through three intervention phases: 7.85/7.86 after Phase 1, 7.59/7.56 after Phase 2, and 0.059 in Phase 3, below the transmission threshold. An additional no-demography robustness check shows that removing demographic turnover changes total predicted cases by only 0.03%, suggesting that the remaining uncertainty lies mainly in omitted vector-side dynamics rather than in human-side demography. Sensitivity analysis indicates that the recovery rate (γ) is the most sensitive parameter affecting [Formula: see text] within this formulation, with a Sobol index of 0.9672, explaining 96.72% of total [Formula: see text] variation. This study provides a controlled comparison between ODE and Petri Net representations of the same epidemiological structure, offering a transparent comparative framework for outbreak fitting, intervention phase identification, and future extension toward explicit host-vector models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.