Background: Early forecasts of dengue are an important tool for disease mitigation. Neural networks are powerful predictive models that have made contributions to many areas of public health. In this study, we reviewed the application of neural networks in the dengue forecasting literature, with the objective of informing model design for future work.
Methods: Following PRISMA guidelines, we conducted a systematic search of studies that use neural networks to forecast dengue in human populations. We summarized the relative performance of neural networks and comparator models, architectures and hyper-parameters, choices of input features, geographic spread, and model transparency.
Results: Sixty two papers were included. Most studies implemented shallow feed-forward neural networks, using historical dengue incidence and climate variables. Prediction horizons varied greatly, as did the model selection and evaluation approach. Building on the strengths of neural networks, most studies used granular observations at the city level, or on its subdivisions, while also commonly employing weekly data. Performance of neural networks relative to comparators, such as tree-based supervised models, varied across study contexts, and we found that 63% of all studies do include at least one such model as a baseline, and in those cases about half of the studies report neural networks as the best performing model.
Conclusions: The studies suggest that neural networks can provide competitive forecasts for dengue, and can reliably be included in the set of candidate models for future dengue prediction efforts. The use of deep networks is relatively unexplored but offers promising avenues for further research, as does the use of a broader set of input features and prediction in light of structural changes in the data generation mechanism.
An ensemble neural network approach to forecast Dengue outbreak based on climatic condition
International Institute of Information Technology Bangalore（班加罗尔国际信息技术学院）；Sorbonne University Abu Dhabi（索邦大学阿布扎比分校）；Sorbonne Center for Artificial Intelligence（索邦人工智能中心）；University of Georgia（佐治亚大学）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；National University of Singapore（新加坡国立大学）
TimeSynth: A Framework for Uncovering Systematic Biases in Time Series Forecasting
Scientific Computing and Imaging Institute, University of Utah（科学计算与成像研究所，犹他大学）；Kahlert School of Computing, University of Utah（Kahlert计算学院，犹他大学）；Mountain Biometrics Inc.（山景生物医学公司）；Department of Psychiatry, University of Utah（精神病学系，犹他大学）
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
AT&T Center for Virtualization（AT&T虚拟化中心）；Southern Methodist University（南卫理公会大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Ohio State University（俄亥俄州立大学）；Universidad Internacional Isabel I de Castilla（卡斯蒂利亚伊莎贝尔一世国际大学）；University of Florida（佛罗里达大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）
Enhancing AI-Based Tropical Cyclone Track and Intensity Forecasting via Systematic Bias Correction
DAMO Academy, Alibaba Group（阿里集团达摩院）；State Key Laboratory of Severe Weather Meteorological Science and Technology, Nanjing University（南京大学 severe weather meteorological science and technology 国家重点实验室）
The impact of data set similarity and diversity on transfer learning success in time series forecasting
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Fraunhofer Center for Applied Research on Supply Chain Services SCS（弗劳恩霍夫供应链服务应用研究中心）；Christian-Albrechts-Universität zu Kiel（基尔基督教阿尔布雷希特大学）；Otto-Friedrich-Universität Bamberg（班贝格奥托-弗里德里希大学）；Vives University of Applied Science（维维斯应用科技大学）；University of Haute-Alsace（上阿尔萨斯大学）；Monash University（莫纳什大学）
Energy Price Modelling: A Comparative Evaluation of four Generations of Forecasting Methods
Bucharest University of Economic Studies（布加勒斯特经济研究大学）；Humboldt University of Berlin（柏林洪堡大学）；California Institute of Technology（加州理工学院）；Institute for Economic Forecasting, Romanian Academy（罗马尼亚科学院经济预测研究所）
Product Progression: a machine learning approach to forecasting industrial upgrading
Università Sapienza（罗马大学）；Centro Ricerche Enrico Fermi（恩里科·费米研究中心）；Joint Research Centre（联合研究中心）；Istituto dei Sistemi Complessi - CNR（意大利国家研究委员会复杂系统研究所）
Operational evaluation of data-driven forest fire forecasting models
CMCC Foundation - Euro-Mediterranean Center on Climate Change, Italy（CMCC基金会 - 欧洲-地中海气候变化中心，意大利）；Department of Engineering for Innovation, University of Salento, Italy（萨伦托大学创新工程系，意大利）
Quantum Long Short-Term Memory (QLSTM) vs Classical LSTM in Time Series Forecasting: A Comparative Study in Solar Power Forecasting
National University of Sciences and Technology（国家科学技术大学）；Ghulam Ishaq Khan Institute of Engineering Sciences and Technology（古拉姆·伊沙克·汗工程科学与技术学院）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；Uppsala University（乌普萨拉大学）
Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach
Independent Researcher（独立研究者）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Southern Methodist University（南卫理公会大学）；Shanghai University of Finance and Economics（上海财经大学）
