Zoonotic outbreak risk prediction with long short-term memory models: a case study with schistosomiasis, echinococcosis, and leptospirosis
Our study successfully developed LSTM hybrid models for disease outbreak risk prediction using three zoonotic diseases as case studies. We demonstrate that the LSTM, when combined with time series decomposition, delivers more accurate results compared to conventional LSTM models using the raw data series. Disease outbreak trends can be predicted more accurately using hybrid models.
Authors
- Zhongting Chen (ORCID: https://orcid.org/0000-0003-2209-7248)
- Jin Zhao (ORCID: https://orcid.org/0000-0003-1149-2031)
- Gengjie Jia (ORCID: https://orcid.org/0009-0001-2881-962X)
- Zhaoyuan He
- Chun‐Rong Chen
- Xuhui Zhu (ORCID: https://orcid.org/0009-0000-7845-4086)
- Hailan Chen (ORCID: https://orcid.org/0000-0002-1733-5267)
- Jiabao Li
- Xinnan Wu
Institutions
- Guangxi University (CN)
- Huazhong Agricultural University (CN)
- Agricultural Genomics Institute at Shenzhen (CN)
- Taiyuan University of Technology (CN)
Publication Details
- Journal
- BMC Infectious Diseases
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1186/s12879-024-09892-y
- Citations
- 10
- Primary Topic
- Zoonotic diseases and public health
- Type
- article
- Field-Weighted Citation Impact
- 5.43
Funders
- Chinese Academy of Agricultural Sciences