Leveraging Climate Data for Dengue Forecasting in Ba Ria Vung Tau Province, Vietnam: An Advanced Machine Learning Approach.

IF 2.8 4区 医学 Q2 INFECTIOUS DISEASES
Dang Anh Tuan, Tran Ngoc Dang
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Abstract

Dengue fever is a persistent public health issue in tropical regions, including Vietnam, where climate variability plays a crucial role in disease transmission dynamics. This study focuses on developing climate-based machine learning models to forecast dengue outbreaks in Ba Ria Vung Tau (BRVT) province, Vietnam, using meteorological data from 2003 to 2022. We utilized four predictive models-Negative Binomial Regression (NBR), Seasonal AutoRegressive Integrated Moving Average with Exogenous Regressors (SARIMAX), Extreme Gradient Boosting (XGBoost) v2.0.3, and long short-term memory (LSTM)-to predict weekly dengue incidence. Key climate variables, including temperature, humidity, precipitation, and wind speed, were integrated into these models, with lagged variables included to capture delayed climatic effects on dengue transmission. The NBR model demonstrated the best performance in terms of predictive accuracy, achieving the lowest Mean Absolute Error (MAE), compared to other models. The inclusion of lagged climate variables significantly enhanced the model's ability to predict dengue cases. Although effective in capturing seasonal trends, SARIMAX and LSTM models struggled with overfitting and failed to accurately predict short-term outbreaks. XGBoost exhibited moderate predictive power but was sensitive to overfitting, particularly without fine-tuning. Our findings confirm that climate-based machine learning models, particularly the NBR model, offer valuable tools for forecasting dengue outbreaks in BRVT. However, improving the models' ability to predict short-term peaks remains a challenge. The integration of meteorological data into early warning systems is crucial for public health authorities to plan timely and effective interventions. This research contributes to the growing body of literature on climate-based disease forecasting and underscores the need for further model refinement to address the complexities of dengue transmission in highly endemic regions.

越南巴里望道省利用气候数据进行登革热预测:先进的机器学习方法。
登革热是包括越南在内的热带地区长期存在的公共卫生问题,而气候多变性在疾病传播动态中起着至关重要的作用。本研究利用 2003 年至 2022 年的气象数据,重点开发了基于气候的机器学习模型,用于预测越南巴里望头省(BRVT)的登革热疫情。我们采用了四种预测模型--负二项回归(NBR)、带外源回归因子的季节自回归整合移动平均(SARIMAX)、极端梯度提升(XGBoost)v2.0.3 和长短期记忆(LSTM)--来预测登革热的每周发病率。温度、湿度、降水量和风速等关键气候变量被整合到这些模型中,并加入了滞后变量,以捕捉气候对登革热传播的延迟影响。与其他模型相比,NBR 模型在预测准确性方面表现最佳,平均绝对误差(MAE)最低。加入滞后气候变量大大提高了模型预测登革热病例的能力。SARIMAX 模型和 LSTM 模型虽然能有效捕捉季节性趋势,但也存在过度拟合的问题,无法准确预测短期疫情。XGBoost 模型表现出适度的预测能力,但对过拟合很敏感,尤其是在没有微调的情况下。我们的研究结果证实,基于气候的机器学习模型,尤其是 NBR 模型,为预测 BRVT 登革热疫情提供了有价值的工具。然而,提高模型预测短期高峰的能力仍是一项挑战。将气象数据整合到早期预警系统中对于公共卫生部门规划及时有效的干预措施至关重要。这项研究为越来越多的基于气候的疾病预测文献做出了贡献,并强调了进一步完善模型以应对登革热在高流行地区传播的复杂性的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Tropical Medicine and Infectious Disease
Tropical Medicine and Infectious Disease Medicine-Public Health, Environmental and Occupational Health
CiteScore
3.90
自引率
10.30%
发文量
353
审稿时长
11 weeks
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