Abstract
BackgroundAcquired immunodeficiency syndrome (AIDS) is a malignant infectious disease with high mortality caused by HIV (human immunodeficiency virus, and up to now there are no curable drugs or effective vaccines. In order to understand AIDS’s development trend, we establish hybrid EMD-BPNN (empirical modal decomposition and Back-propagation artificial neural network model) model to forecast new HIV infection in Dalian and to evaluate model’s performance.MethodsThe monthly HIV data series are decomposed by EMD method, and then all decomposition results are used as training and testing data to establish BPNN model, namely BPNN was fitted to each IMF (intrinsic mode function) and residue separately, and the predicted value is the sum of the predicted values from the models. Meanwhile, using yearly HIV data to established ARIMA and using monthly HIV data to established BPNN, and SARIMA (seasonal autoregressive integrated moving average) model to compare the predictive ability with EMD-BPNN model.ResultsFrom 2004 to 2017, 3310 cases of HIV were reported in Dalian, including 101 fatal cases. The monthly HIV data series are decomposed into four relatively stable IMFs and one residue item by EMD, and the residue item showed that the incidence of HIV increases firstly after declining. The mean absolute percentage error value for the EMD-BPNN, BPNN, SARIMA (1,1,2) (0,1,1)12 in 2018 is 7.80%, 10.79%, 9.48% respectively, and the mean absolute percentage error value for the ARIMA (3,1,0) model in 2017 and 2018 is 8.91%.ConclusionsThe EMD-BPNN model was effective and reliable in predicting the incidence of HIV for annual incidence, and the results could furnish a scientific reference for policy makers and health agencies in Dalian.
Highlights
Acquired immunodeficiency syndrome (AIDS) is a malignant infectious disease with high mortality caused by Human immunodeficiency virus (HIV)
Data analysis The monthly HIV data series are decomposed by Empirical modal decomposition (EMD) method, and all decomposition results are used as training and testing data to establish Back-propagation artificial neural network model (BPNN) model, namely BPNN was fitted to each intrinsic mode function (IMF) and residue separately, the predicted value is the sum of the predicted values from the models, and the annual incidence equal to the sum of the predicted results of the 12 months
The residue item showed that the monthly incidence data of HIV in Dalian during2004 to 2015 is increasing and during 2016 to 2017 is declining
Summary
Acquired immunodeficiency syndrome (AIDS) is a malignant infectious disease with high mortality caused by HIV The full name of AIDS is acquired immunodeficiency syndrome, which is a malignant infectious disease with high mortality caused by human immunodeficiency virus infection [1], and has become one of global public health problem [2]. Some scholars have used ARIMA model, GM (1,1) model, BP neural network model to predict the incidence trend of HIV. Yang et al [8] using ARIMA to build modeling HIV incidence from 2000 to 2014 in China and the mean absolute percentage error was 19.90%. Wu et al [9] use Back-propagation neural network (BP-ANN) as a model to predict HIV prevalence, and the ratios of accuracy for training, calibration and detection were 93.94%, 88.48% and 89.60%, respectively
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