Predictive association between control measures and chikungunya fever incidence based on random forest and SHAP analysis.

Publication date: Sep 01, 2026

Using the 2025 Chikungunya fever (CHIK) outbreak in Chancheng District, Foshan City, this study applied a random forest (RF) regression model combined with SHapley Additive exPlanations (SHAP) to explore predictive associations, nonlinear relationships and potential thresholds between environmental-social factors and village/community-level cumulative incidence, to inform stratified control of mosquito-borne diseases. In this cross-sectional ecological study of 143 villages/communities, the outcome was cumulative incidence, and nine candidate covariates were assessed, including the hospitalization isolation rate, construction-site density, and population density. Multicollinearity was checked using the variance inflation factor. Model fit was evaluated by the out-of-bag (OOB) R^2, RMSE, and MAE, and variable importance by %IncMSE. Robustness was tested with 100 repeated runs, bootstrap thresholds from SHAP dependence plots, and a sensitivity analysis excluding the endogenous isolation rate. On the log(1 + incidence) scale, the OOB R^2 was 0. 206, with underestimation of high-incidence areas. The hospitalization isolation rate had the highest importance (%IncMSE = 18. 43) and was negatively correlated with predictions (ρ = -0. 749), but this likely reflects reverse causation and is predictive only. Construction-site density was strongly positive (ρ = 0. 855), with a stable threshold near 12. 8 sites/km^2; it remained the most robust predictor after removing the isolation rate (%IncMSE = 8. 54). Construction-site density was the most robust environmental predictor, whereas population density contributed little. These exploratory, predictive associations-not causal effects-should guide risk stratification and require prospective validation with time-matched longitudinal data.

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