Scholarlink

Background <p>This study investigates malaria incidence trends in mainland China from 2005 to 2020, to elucidate its epidemiological characteristics and investigate potential associations with air pollution. Reasonable prediction is of great significance to control the epidemic of malaria.</p> Methods <p>First, time series analysis and machine learning methods were employed to predict malaria incidence. Weighted quantile sum (WQS) model and distributed lag nonlinear model (DLNM) were utilized to assess the risk of malaria linked to persistent organic pollutants (POPs).</p> Results <p>For the new Kalman filter model, showing good results across both overall malaria and individual subtypes (MAE ∈ [0.001, 0.016]). Models with the best performance are Gradient Boosting (XGBoost) and Support Vector Machine (SVM). Risk levels for Polychlorinated Biphenyls (PCB) and Hexachlorobenzene (HCB) were coefficients (95% CI): -1.48 (-2.69, -0.27) and − 1.39 (-2.57, -0.22), respectively. Cumulative effect of extremely low-level HCB during the first 3 and 4 months were 3.602 (1.103, 11.765) and 4.749 (1.11, 20.31), respectively, indicating an increased risk of malaria incidence.</p> Conclusions <p>Our current study not only investigated the spatiotemporal surveillance and early warning systems for malaria incidence in mainland China but also elucidated the lagged exposure-response relationships and potential associations between organic pollutants and malaria occurrence. Strengthening POPs emission control activities during this period may help reduce the risk of seasonal malaria susceptibility.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Association between persistent organic pollutants and malaria incidence in mainland China: a population-based surveillance and modeling analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

查看全文
  • Guolong Qu,
  • Jianqiang Han,
  • Zhenyao Song,
  • Weiming Hou

摘要

Background

This study investigates malaria incidence trends in mainland China from 2005 to 2020, to elucidate its epidemiological characteristics and investigate potential associations with air pollution. Reasonable prediction is of great significance to control the epidemic of malaria.

Methods

First, time series analysis and machine learning methods were employed to predict malaria incidence. Weighted quantile sum (WQS) model and distributed lag nonlinear model (DLNM) were utilized to assess the risk of malaria linked to persistent organic pollutants (POPs).

Results

For the new Kalman filter model, showing good results across both overall malaria and individual subtypes (MAE ∈ [0.001, 0.016]). Models with the best performance are Gradient Boosting (XGBoost) and Support Vector Machine (SVM). Risk levels for Polychlorinated Biphenyls (PCB) and Hexachlorobenzene (HCB) were coefficients (95% CI): -1.48 (-2.69, -0.27) and − 1.39 (-2.57, -0.22), respectively. Cumulative effect of extremely low-level HCB during the first 3 and 4 months were 3.602 (1.103, 11.765) and 4.749 (1.11, 20.31), respectively, indicating an increased risk of malaria incidence.

Conclusions

Our current study not only investigated the spatiotemporal surveillance and early warning systems for malaria incidence in mainland China but also elucidated the lagged exposure-response relationships and potential associations between organic pollutants and malaria occurrence. Strengthening POPs emission control activities during this period may help reduce the risk of seasonal malaria susceptibility.