Epidemics · 2025
Modeling transmission dynamics and socio-economic determinants of scarlet fever in Chengdu, China: An integrated SEIAR and machine learning approach.
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Yang Tianlong, Du Xunbo, Li Junfan, Zhang Tin, Wang Yao, Wang Liang
Аннотация
BACKGROUND: Scarlet fever (SF) is an acute infectious disease that poses a significant public health threat; however, its transmission dynamics, particularly the impact of asymptomatic carriers and socioeconomic determinants, remain unclear.
METHODS: We developed a susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) model that incorporates asymptomatic infections to estimate the time-varying reproduction number (Rt) for SF in Chengdu (2005-2019) using local epidemiological data. The model was evaluated using the coefficient of determination (R²), and sensitivity analysis confirmed its robustness. We further integrated Boruta, Extreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP) to systematically assess the influence of socioeconomic variables on Rt.
RESULTS: Between 2005 and 2019, Chengdu reported 11,499 cases of SF, with an average incidence of 4.87 per 100,000. Two distinct seasonal peaks occurred in April-May and November-December, and incidence rates were notably lower during school holidays. The majority of cases affected children aged 3-7, with a male-to-female ratio of 1.59:1. In addition, core districts such as Wuhou and Xindu exhibited the highest incidence. The SEIAR model demonstrated strong predictive performance (overall R² = 0.831, P < 0.001) and estimated a median Rt of 0.963; however, several regions exceeded this threshold, with Rt peaking approximately two months prior to incidence surges. Spatial analyses revealed significant clustering in central urban areas, and integrated socioeconomic analysis identified the one-child rate as the primary driver of Rt, followed by population density and healthcare facility density (P < 0.01).
CONCLUSION: By integrating epidemiological data with socioeconomic factors, this study quantitatively elucidates the transmission characteristics of SF in Chengdu, providing data-driven support for monitoring and targeted intervention strategies in the absence of vaccination.
METHODS: We developed a susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) model that incorporates asymptomatic infections to estimate the time-varying reproduction number (Rt) for SF in Chengdu (2005-2019) using local epidemiological data. The model was evaluated using the coefficient of determination (R²), and sensitivity analysis confirmed its robustness. We further integrated Boruta, Extreme Gradient Boosting (XGBoost), and SHapley Additive exPlanations (SHAP) to systematically assess the influence of socioeconomic variables on Rt.
RESULTS: Between 2005 and 2019, Chengdu reported 11,499 cases of SF, with an average incidence of 4.87 per 100,000. Two distinct seasonal peaks occurred in April-May and November-December, and incidence rates were notably lower during school holidays. The majority of cases affected children aged 3-7, with a male-to-female ratio of 1.59:1. In addition, core districts such as Wuhou and Xindu exhibited the highest incidence. The SEIAR model demonstrated strong predictive performance (overall R² = 0.831, P < 0.001) and estimated a median Rt of 0.963; however, several regions exceeded this threshold, with Rt peaking approximately two months prior to incidence surges. Spatial analyses revealed significant clustering in central urban areas, and integrated socioeconomic analysis identified the one-child rate as the primary driver of Rt, followed by population density and healthcare facility density (P < 0.01).
CONCLUSION: By integrating epidemiological data with socioeconomic factors, this study quantitatively elucidates the transmission characteristics of SF in Chengdu, providing data-driven support for monitoring and targeted intervention strategies in the absence of vaccination.