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Mathematical models linked with vector and case data have proven powerful for understanding arboviral transmission dynamics, but it remains unclear if and which environmental, vector, and host features are essential for accurately capturing transmission dynamics and short-term forecasting. We began with a baseline modified SEIR compartmental model, extended it into a weekly probabilistic model aligned with reported surveillance data, and evaluated variants that include or exclude mosquito dynamics (M), temperature effects (T), and precipitation effects (P). We calibrated these variants to weekly CHIKV and Zika incidence, temperature, and precipitation data from nine regions in French Polynesia and the French West Indies. We used AIC-based model comparison to assess goodness-of-fit, rolling-window forecasting to compare predictive performance with statistical and null baselines, and the best-fitting model to estimate biologically meaningful transmission parameters. Incorporating mosquito dynamics, temperature, and precipitation substantially improves prediction accuracy, with precipitation acting as the primary environmental driver and temperature serving as a modulator. The corresponding SEI+M+T+P formulation achieves forecasting accuracy comparable to ARIMA while providing mechanistically interpretable predictions. CHIKV possesses higher transmissibility than Zika, with regionally heterogeneous parameters driven by varying mosquito-human transmission rates and mosquito densities, whereas Zika parameters are relatively homogeneous across regions. These findings suggest uniform vector control may be effective for Zika, whereas region-specific strategies are needed for CHIKV. Sensitivity analysis indicates that rapid diagnosis and isolation of symptomatic individuals are among the most effective measures for reducing transmission of both viruses.\n                  <\/jats:p>","DOI":"10.1007\/s11538-026-01728-x","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T13:17:02Z","timestamp":1787145422000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Identifying Key Factors in Predicting Chikungunya and Zika Transmission Across French Polynesia and the French West Indies: A Data-Driven Probabilistic and Mathematical Modeling Approach"],"prefix":"10.1007","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0871-676X","authenticated-orcid":false,"given":"Zhiyuan","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Huo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter J.","family":"Thomas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qimin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,19]]},"reference":[{"key":"1728_CR1","unstructured":"Akaike H (1973) Information theory and an extension of the maximum likelihood principle. 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