Modeling and prediction for the trend of outbreak of NCP based on a time-delay dynamic system

Abstract

In late December 2019, a series of novel coronavirus pneumonia (NCP) cases emerged in Wuhan, and the outbreak of NCP began to spread rapidly to the whole country and even overseas within a few days. The scientific and effective understanding of epidemic development is essential for the prevention and control. In this paper, based on the cumulative number of confirmed and cured cases reported daily by the National Health Committee, we propose a novel dynamic system with time-delay to describe the outbreak of NCP. The time-delay process is introduced to describe the latent period and treatment cycle. Numerical simulations show that the parameters in the model are identified accurately, and the trend of the outbreak of NCP is effectively simulated. Moreover, the prediction for the tendency of NCP is provided for reference. Finally, we would conclude that the situation would be better and better under the current effective and efficient measures of government.

A time delay dynamic system with external source for the local outbreak of 2019-nCoV

“…In this paper, we have proposed a novel time delay dynamic system with external source. In this system, the suspected people of Area a transfer to Area b is concerned, and it is more reasonable and appropriate than the one in [4,5] to describe the trend of local outbreak for the 2019-nCoV. The numerical simulations are carried out to verify the effectiveness and accuracy of the novel time delay dynamic system with external source.…”

“…Some other part of the infected people are isolated during latent period according to investigation of diagnosed cases. (5) No matter the cumulative confirmed people J(t) are isolated before diagnosed or not, they consist of the population infected at time t − τ 1 averagely. (6) Suppose the individual would no longer transmit the coronavirus to others when he/she is isolated or in the treatment.…”

“…However, they are not appropriate to describe the outbreak of 2019-nCoV in China since they don't consider the latent period and the external sources. Recently we propose a novel time delay dynamical system to describe the 2019-nCoV spread in China [4,5], but it is not suitable to describe the trend of local outbreak for the 2019-nCoV. In this paper, we propose a novel time delay dynamic system with external source.…”

The Framework for the Prediction of the Critical Turning Period for Outbreak of COVID-19 Spread in China based on the iSEIR Model

“…On the other hand, modelling the situation of COVID-19 and effects of different containment strategies in China with dynamic differential equations and parameters estimation have also been paid a lot attention by a number of scholars, e.g., see Gu et al [17], Hu et al [18], Zhao et al [18], Yan et al [20], Wang et al [21], Tang et al [22], Huang et al [23], Cui and Hu [24] and related references wherein.…”

Dynamic models for Coronavirus Disease 2019 and data analysis

“…[9][10][11][12][13][14][15] In 23 February, the Fudan-CCDC model is specially used to warn that there could be a rapid outbreak in Japan if no effective quarantine measures are carried out immediately. 7 In this paper, we provide a brief introduction of the TDD-NCP model 1,2 and Fudan-CCDC model. 5 We use the Fudan-CCDC model to reconstruct some important parameters (including growth rate, isolation rage, initial date) and predict the cumulative number of confirmed cases in some of cities in China.…”