Prediction of SARS epidemic by BP neural networks with online prediction strategy

Bai, Yanping; Jin, Zhen

doi:10.1016/j.chaos.2005.01.064

Cited by 67 publications

(39 citation statements)

References 2 publications

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“…A BP neural network with a hidden layer can approximate with arbitrary precision an arbitrary non-linear function that’s defined on a compact set of R n [43], [44].We employed three-layer BP neural network including input layer, hidden layer and output layer. The number of neurons in the hidden layer is one of the primary parameters of BPN algorithm; currently however there is no authoritative rule to determine it.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

Zhong

Dou

et al. 2013

PLoS ONE

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Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments.

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Section: Experiments and Discussionmentioning

confidence: 99%

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

Zhong

Dou

et al. 2013

PLoS ONE

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“…The weights determine the strength of the input signals. Although different ANN architectures and learning paradigms [5,43] can be used for disease forecasting, this method is presently better suited to short-term projections because recognizing complex long-term patterns with ANNs would require too much training data.…”

Section: Machine Learning-based and Case-based Methodsmentioning

confidence: 99%

Long-Term Forecasting of Influenza-Like Illnesses in Russia

Kondratyev

Цыбалова²

2013

Int. J. of Pure and Appl. Math.

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This paper compares the feasible methods for the long-term forecasting of the incidence rates of influenza-like illnesses (ILI) and acute respiratory infections (ARI), which is important for strategic management. A literature survey shows that the most appropriate techniques for long-term ILI & ARI morbidity projections are the following well-known statistical methods: simple averaging of observations, point-to-point linear estimates, Serfling-type regression models, autoregressive models such as autoregressive integrated moving average (ARIMA) models, and generalized exponential smoothing using the Holt-Winters approach. Using these methods and official data on the total number of ILI & ARI cases per week in 2000-2012 in Moscow, St. Petersburg, Novosibirsk, Yekaterinburg, Nizhny Novgorod and Yakutsk, we developed oneyear projections and evaluated their accuracy. Different methods yielded the best results, depending on the time series. Generally, it is preferable to use the Serfling model. The Serfling model forecasts almost matched the pointto-point linear estimates. In certain cases, ARIMA outperformed the Serfling model. Simple averaging can ensure a fairly good prediction when the ILI & ARI time series do not exhibit a trend. The results of exponential smoothing were poorer than those of other techniques.

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“…The spread of an infectious disease in a population depends mainly on the character of the disease [4][5][6][7][8]. In an SI model, the population is divided into two disjoint classes, susceptible individuals and infective individuals, whose numbers are denoted by SðtÞ and IðtÞ at time t, respectively.…”

Section: Introductionmentioning

confidence: 99%

The dynamics of an impulsive delay SI model with variable coefficients

Pei

Liu

et al. 2009

Applied Mathematical Modelling

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a b s t r a c tAn impulsive delayed SI model with variable coefficients and a nonlinear incidence is formulated and analyzed. By introducing three thresholds, we obtain sufficient conditions for eradication and permanence of the disease, respectively. It is shown that the conditions depend on time delay for both the global attractivity of the positive infection-free periodic solution and permanence of the model. Furthermore, our results indicate that the disease will disappear if the ratio of the maximum to minimum of the pulse vaccination rate is lager than some value. The main feature of this paper is that we introduce multi-delays and variable coefficients into the SI model, and exhibit a new method which is applied to investigate this model. Numerical results show that the system we considered has complex dynamics including periodic and quasi-periodic oscillations.

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Prediction of SARS epidemic by BP neural networks with online prediction strategy

Cited by 67 publications

References 2 publications

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

Long-Term Forecasting of Influenza-Like Illnesses in Russia

The dynamics of an impulsive delay SI model with variable coefficients

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