Regional distribution of non-human H7N9 avian influenza virus detections in China and construction of a predictive model

Huang, Ziyan; Li, Haijun; Huang, Beixun

doi:10.2478/jvetres-2021-0034

Cited by 1 publication

(1 citation statement)

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“…The DEFNN can not only make effective use of the seasonal prediction advantages of SARIMA but also explain the nonlinear trend in the dataset [ 36 ]. In addition, the evolution process based on a metaheuristic algorithm is conducive to improving the accuracy of the neural network model hyperparameter search [ 37 – 39 ]. The verification results of the external dataset in this paper show that SA-LSTM is suitable not only for the national dataset of a variety of infectious diseases but also for the regional dataset of a variety of infectious diseases.…”

Section: Discussionmentioning

confidence: 99%

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

Yao,

Chen,

Wang

et al. 2024

BMC Bioinformatics

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Background Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. Results The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. Conclusions We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

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Section: Discussionmentioning

confidence: 99%