Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models

Pei, Lijun; Zhang, Mengyu

doi:10.1007/s11571-021-09701-1

Cited by 17 publications

(6 citation statements)

References 37 publications

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“…Regression outcomes provided coarse estimates of controlling performance comparisons of COVID-19 pandemic. This study was in line with early simulation outcomes which found that their NH rates were the approximately linear increasing functions and the ND rates were the small constants [ 42 ]. This could partially explained by an early study which indicated that socio-economic determinants and city sizes had high impacts on the change of COVID-19 transmission in China [ 43 ].…”

Section: Discussionsupporting

confidence: 88%

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Guan

2021

J Epidemiol Glob Health

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Background Currently, the knowledge of associations among newly recovered cases (NR), newly healed cases (NH), newly confirmed cases (NC), and newly dead cases (ND) can help to monitor, evaluate, predict, control, and curb the spreading of coronavirus disease 2019 (COVID-19). This study aimed to explore the panel associations of ND, NH, and NR with NC. Methods Data from China Data Lab in Harvard Dataverse with China (January 15, 2020 to January 14, 2021), the United States of America (the USA, January 21, 2020 to April 5, 2021), and the World (January 22, 2020 to March 20, 2021) had been analyzed. The main variables included in the present analysis were ND, NH, NR, and NC. Pooled regression, stacked within-transformed linear regression, quantile regression for panel data, random-effects negative binomial regression, and random-effects Poisson regression were conducted to reflect the associations of ND, NH, and NR with NC. Event study analyses were performed to explore how the key events influenced NC. Results Descriptive analyses showed that mean value of ND/NC ratio regarding China was more than those regarding the USA and the World. The results from tentative analysis reported the significant relationships among ND, NH, NR, and NC regarding China, the USA, and the World. Panel regressions confirmed associations of ND, NH, and NR with NC regarding China, the USA, and the World. Panel event study showed that key events influenced NC regarding USA and the World more greatly than that regarding China. Conclusion The findings in this study confirmed the panel associations of ND, NH, and NR with NC in the three datasets. The efficiencies of various control strategies of COVID-19 pandemic across the globe were compared by the regression outcomes. Future direction of research work could explore the influencing mechanisms of the panel associations.

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Section: Discussionsupporting

confidence: 88%

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Guan

2021

J Epidemiol Glob Health

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“…The removed rate is approximately a piecewise linear increasing function instead of a linear increasing function which is ( + b )heaviside( t -14). They are very different from those of the previous SIR model [ 18 , 19 ] and bring about the more accurate predictions of the COVID-19 epidemics. Due to the degree of illness and personal recovery, the first recovery may appear at different times, which it is roughly on the 14th day.…”

Section: Introductioncontrasting

confidence: 69%

“…Alos [ 14 ], Lounis [ 15 ], Ferrari [ 16 ], and Sedaghat [ 17 ] used SIRD model to predict the spread trend of COVID-19 in some countries. Long-term forecast of COVID-19 was made in some regions at home and abroad in [ 18 , 19 ]. The PSO algorithm was used for parameter estimation of SEIR model by He [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Long-term prediction of the sporadic COVID-19 epidemics induced by $$\delta$$-virus in China based on a novel non-autonomous delayed SIR model

Pei

2022

Eur. Phys. J. Spec. Top.

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With the outbreaks of the COVID-19 epidemics in several provinces of China, government takes prevention and control measures to contain the epidemics. It is more difficult to make the long-term prediction of the sporadic COVID-19 epidemics than widespread ones in that the former cannot obey the laws of the infectious disease well like the latter. In this paper, we make long-term predictions including end time and final size, peak and peak time of current confirmed cases and the number of accumulative removed cases of the sporadic COVID-19 epidemics in different regions of China by a novel non-autonomous delayed SIR compartment model (S—susceptible, I—infected, R—removed). The key contribution of this paper is that under the rigorous containments, we find transmission rate is approximately an exponential decreasing function with respect to time t, rather than a fixed constant. In addition, the removed rate is approximately a piecewise linear increasing function instead of a linear increasing function which is (at + b)heaviside (t-14). First, according to the few data in the early stage, i.e., roughly the first 7 days, issued by the National Health Commission of China and local Health Commissions, we can accurately estimate these parameters, i.e., transmission and removed rates of the model. Then, by them, we accurately predict the evolution of the COVID-19 there. On the basis of them to predict Category A of the sporadic COVID-19 epidemics since July 20th, 2021 in this summer. The results agree very well to the actual ones. It is also adopted to predict Category B the tour group epidemics since October 17th, 2021 and Category C other sporadic epidemics since October 27th, 2021. The results show that although our method is simple and the needed data are very few, the long-term prediction of the sporadic COVID-19 epidemics in China is quite effective. We can use this novel non-autonomous delayed SIR model to accurately predict its end time and final size, peak and peak time of current confirmed cases and the number of accumulative removed cases in China. This work can help governments and policy-makers make optimal prevention and control policies for all cities and provinces to contain the COVID-19 epidemics, and prepare well for the resumption of work, production and classes in advance to reduce the economic and social losses.

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“…Many countries are already experiencing economic downturns due to the COVID-19 pandemic. Many scholars made use of the existing data to model and predict the development trend of the epidemic [5][6][7] and gave some suggestions on epidemic control from the perspective of mathematics. Xiao et al analyzed the piecewise incidence rate in a SIR system to show the effect of threshold densities and control intensities on the outbreak of infectious disease [8].…”

Section: Introductionmentioning

confidence: 99%

Hopf bifurcation and patterns in a modified SIR model

Yang,

Zheng,

Shen

et al. 2023

Front. Phys.

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Infectious diseases have constantly threatened human safety because the diffusion of the susceptible and infected may make more individuals infected and even die. In this paper, a modified SIR model with both external stimulus and diffusion is considered to illustrate the dynamical mechanism of the periodic outbreak and pattern formation. Firstly, we propose a modified SIR model based on the propagation behaviour of infectious diseases to show the effects of the different parameters and diffusion on the outbreak. The Hopf bifurcation and multiscale methods are performed to analyze the stability of this model, which explains the dynamical mechanism of the periodic outbreak. Then, the pattern formation and Turing instability are discussed through comparison principles to reveal the role of periodic disturbances and diffusion in selecting pattern formation. Also, we find rich patterns that may occur when the frequency modulation is close to the intrinsic frequency. Finally, our theoretical results are verified by numerical simulation.

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Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models

Cited by 17 publications

References 37 publications

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic

Long-term prediction of the sporadic COVID-19 epidemics induced by $$\delta$$-virus in China based on a novel non-autonomous delayed SIR model

Hopf bifurcation and patterns in a modified SIR model

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