Zoonotic outbreak risk prediction with long short-term memory models: a case study with schistosomiasis, echinococcosis, and leptospirosis
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BACKGROUND: Zoonotic infections, characterized with huge pathogen diversity, wide affecting area and great society harm, have become a major global public health problem. Early and accurate prediction of their outbreaks is crucial for disease control. The aim of this study was to develop zoonotic diseases risk predictive models based on time-series incidence data and three zoonotic diseases in mainland China were employed as cases. METHODS: The incidence data for schistosomiasis, echinococcosis, and leptospirosis were downloaded from the Scientific Data Centre of the National Ministry of Health of China, and were processed by interpolation, dynamic curve reconstruction and time series decomposition. Data were decomposed into three distinct components: the trend component, the seasonal component, and the residual component. The trend component was used as input to construct the Long Short-Term Memory (LSTM) prediction model, while the seasonal component was used in the comparison of the periods and amplitudes. Finaly, the accuracy of the hybrid LSTM prediction model was comprehensive evaluated. RESULTS: This study employed trend series of incidence numbers and incidence rates of three zoonotic diseases for modeling. The prediction results of the model showed that the predicted incidence number and incidence rate were very close to the real incidence data. Model evaluation revealed that the prediction error of the hybrid LSTM model was smaller than that of the single LSTM. Thus, these results demonstrate that using trending sequences as input sequences for the model leads to better-fitting predictive models. CONCLUSIONS: 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.
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DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Zoonotic outbreak risk prediction with long short-term memory models: a case study with schistosomiasis, echinococcosis, and leptospirosis
- Date Crossref
- 27/09/2024
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Guangxi University pays non établi dans la noticeUniversité ou école supérieure
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Agricultural Genomics Institute at Shenzhen pays non établi dans la noticeStructure de recherche
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Huazhong Agricultural University pays non établi dans la noticeUniversité ou école supérieure
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Taiyuan University of Technology pays non établi dans la noticeUniversité ou école supérieure
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College of Animal Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs pays non établi dans la noticeStructure de recherche
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College of Informatics pays non établi dans la noticeUniversité ou école supérieure
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College of Data Science pays non établi dans la noticeUniversité ou école supérieure
Guangxi University, Agricultural Genomics Institute at Shenzhen et Huazhong Agricultural University, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.