Modeling seasonal measles transmission in China

Bai, Zhenguo; Liú, Dan

doi:10.1016/j.cnsns.2014.09.030

Cited by 18 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the data in [41], we choose the transmission rate as β = 9.2329 × 10 −11 day −1 , and the initial infectious population number in 2005 as I(0) = 14706.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Global stability and Hopf bifurcations of an SEIR epidemiological model with logistic growth and time delay

Wang

Zhang

2015

Applied Mathematics and Computation

View full text Add to dashboard Cite

“…According to the data in [41], we choose the transmission rate as β = 9.2329 × 10 −11 day −1 , and the initial infectious population number in 2005 as I(0) = 14706.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Global stability and Hopf bifurcations of an SEIR epidemiological model with logistic growth and time delay

Wang

Zhang

2015

Applied Mathematics and Computation

View full text Add to dashboard Cite

“…In addition, the numbers of new confirmed cases of the four types of infectious diseases were used as inputs in the model. Because the numbers of cases of different diseases might have correlations and seasonal fluctuations [19][20][21][22][23][24], they were standardized in advance. After standardization was completed, the average value of each input variable should be approximately zero, there should be no correlation between variables, and covariances should be approximately equal.…”

Section: Data Sources 22 Real-time Recurrent Learningmentioning

confidence: 99%

Early warning of some notifiable infectious diseases in China by the artificial neural network

Guo

Xiao

2020

R. Soc. open sci.

View full text Add to dashboard Cite

In order to accurately grasp the timing for the prevention and control of diseases, we established an artificial neural network model to issue early warning signals. The real-time recurrent learning (RTRL) and extended Kalman filter (EKF) methods were performed to analyse four types of respiratory infectious diseases and four types of digestive tract infectious diseases in China to comprehensively determine the epidemic intensities and whether to issue early warning signals. The numbers of new confirmed cases per month between January 2004 and December 2017 were used as the training set; the data from 2018 were used as the test set. The results of RTRL showed that the number of new confirmed cases of respiratory infectious diseases in September 2018 increased abnormally. The results of the EKF showed that the number of new confirmed cases of respiratory infectious diseases increased abnormally in January and February of 2018. The results of these two algorithms showed that the number of new confirmed cases of digestive tract infectious diseases in the test set did not have any abnormal increases. The neural network and machine learning can further enrich and develop the early warning theory.

show abstract

“…In this section, we will numerically analyze model (1) and identify the effects of some parameters we are interested in on the outbreak of measles. According to reference [9], we obtain the recruitment rate Λ � 1589357 per month. Assuming the current average life expectancy of Chinese people is 73 years, then we calculate the natural death rate μ � 0.001142 per month.…”

Section: Lemma 1 Assume That (A1)-(a7) Holdmentioning

confidence: 99%

“…In contrast, for some countries where measles has been eliminated, Choi et al [7] estimated susceptibility to measles by age and Mckee et al [8] investigated the optimal age target for two routine dose without changing vaccination coverage in order to maintain elimination. Furthermore, Bai and Liu [9] fitted the model to the case data in China and Huang et al [10] investigated the effect of various interventions on measles infection. orrington et al [11] attempted to quantify the loss of QALY (quality-adjusted life years) due to measles at a population level in England and provided important parameters for future control interventions.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Periodic Outbreak of Measles in Mainland China

Xue

Ruan

Xiao

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In mainland China, measles infection reached the lowest level in 2012 but resurged again after that with a seasonally fluctuating pattern. To investigate the phenomenon of periodic outbreak and identify the crucial parameters that play in the transmission dynamics of measles, we formulate a mathematical model incorporating periodic transmission rate and asymptomatic infection with waning immunity. We define the basic reproduction number as the threshold value to govern whether measles infection dies out or not. Fitting the reported measles cases from 2013 to 2016 to our proposed model, we estimate the basic reproduction number R0 with immunization to be 1.0077. From numerical simulations, we conclude asymptomatic infection does not cause much new infections and the key parameters affecting the transmission of measles are vaccination rate, transmission rate, and recovery rate, which suggests the public to enhance vaccination and protection measures to reduce effective contacts between susceptible and infective individuals and treat infected individuals timely. To minimize the number of infected individuals at a minimal cost, we formulate an optimal control system to design optimal control strategies. Numerical simulations show the effectiveness of optimal control strategies and recommend us to implement the control strategies as soon as possible. In particular, enhancing vaccination is especially effective in lowering the initial outbreak and making disease recurrence less likely.

show abstract

Modeling seasonal measles transmission in China

Cited by 18 publications

References 21 publications

Global stability and Hopf bifurcations of an SEIR epidemiological model with logistic growth and time delay

Global stability and Hopf bifurcations of an SEIR epidemiological model with logistic growth and time delay

Early warning of some notifiable infectious diseases in China by the artificial neural network

Modelling the Periodic Outbreak of Measles in Mainland China

Contact Info

Product

Resources

About