Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal's competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.
翻译:登革热是一种恶性疾病,在非洲、美洲和亚洲的100多个热带和亚热带国家蔓延。这种狂暴疾病影响着全球约4亿人,使保健系统受到严重影响。缺乏一种具体的药物和现用疫苗使情况更加糟糕。因此,决策者必须依靠预警系统来控制与干预有关的决定。预测经常为危险的流行病事件提供关键信息。然而,现有的预测模型(例如天气驱动的机械、统计时间序列和机器学习模型)缺乏对不同组成部分的明确了解,无法提高预测准确性,常常提供不稳定和不可靠的预测。本研究提出了具有外源因素(XEWNet)模型的混合波状神经网络网络,可以为三个地理区域,即圣胡安、伊基托斯和艾哈迈哈迈达巴德的登革热疫情预测提供可靠的估计数。拟议的XEWNet模型具有灵活性,而且很容易将基于统计因果关系测试的短期气候变量纳入其可变性测试中。拟议的模型是一种综合方法,利用波状的登革热频率变化和不稳妥的模型,在复杂的统计周期、可测算的模型中采用若干可测算的模型。