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The application of traditional epidemiological models such as susceptible-exposed-infectious-recovered (SEIR) may face difficulties in accurately predicting such complex epidemics. Data-driven prediction approaches lack the ability to generalize and exhibit low accuracy on small datasets due to their reliance on large amounts of data without incorporating prior knowledge. To overcome this limitation, we introduce a flexible ensemble data-driven framework (Neural-SEIR) that \"neuralizes\" the SEIR model by approximating the core parameters through neural networks while preserving the propagation structure of SEIR. Neural-SEIR employs long short-term memory (LSTM) neural network to capture complex correlation features, exponential smoothing (ES) to model seasonal information, and prior knowledge from SEIR. By incorporating SEIR parameters into the neural network structure, Neural-SEIR leverages prior knowledge while updating parameters with real-world data. Our experimental results demonstrate that Neural-SEIR outperforms traditional machine learning and epidemiological models, achieving high prediction accuracy and efficiency in forecasting epidemic diseases.&lt;\/p&gt;&lt;\/abstract&gt;<\/jats:p>","DOI":"10.3934\/mbe.2023749","type":"journal-article","created":{"date-parts":[[2023,8,23]],"date-time":"2023-08-23T10:11:01Z","timestamp":1692785461000},"page":"16807-16823","source":"Crossref","is-referenced-by-count":12,"title":["Neural-SEIR: A flexible data-driven framework for precise prediction of epidemic disease"],"prefix":"10.3934","volume":"20","author":[{"given":"Haoyu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xihe","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinghan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Tan","sequence":"additional","affiliation":[{"name":"INF Technology (Shanghai) Co., Ltd., Shanghai 201203, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology-Head and Neck Surgery, Eye &amp; ENT Hospital of Fudan University, Shanghai 200031, China"},{"name":"Sleep Disordered Medical Center, Shanghai Municipal Key Clinical Specialty, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2023749-1","doi-asserted-by":"publisher","unstructured":"R. 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