Prediction of the COVID-19 outbreak in China based on a new stochastic dynamic model

AbstractThe current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID-19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing models. In this paper, a novel stochastic model is proposed which aims to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We find that, (1) instead of aberration, there is a remarkable amount of asymptomatic individuals, (2) an individual with symptoms is approximately twice more likely to pass the disease to others than that of an asymptomatic patient, (3) the transmission rate has reduced significantly since the implementation of control measures in Mainland China, (4) it is expected that the epidemic outbreak would be contained by early March in the the selected provinces and cities.

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Is Nigeria really on top of COVID-19? Message from effective reproduction number

10.1101/2020.05.16.20104471 ◽

2020 ◽

Author(s):

Adeshina Israel Adekunle ◽

Oyelola Adegboye ◽

Ezra Gayawan ◽

Emma McBryde

Keyword(s):

Public Health ◽

Compartmental Model ◽

Reproduction Number ◽

Transmission Rate ◽

Credible Interval ◽

Control Measures ◽

Maximum Value ◽

Intervention Measures ◽

Death Cases ◽

Epidemiological Parameters

Following the importation of Covid-19 into Nigeria on the 27 February 2020 and then the outbreak, the question is: how do we anticipate the progression of the ongoing epidemics following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of Covid-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R_0). This also enables us to estimate the daily transmission rate (β) that determines the effect of social distancing. We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on Covid-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 1.78. Most importantly, the R(t) is strictly greater than one from April 13 till 7 May 2020. The R_0 is estimated to be 2.42 with credible interval: (2.37, 2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below one. Also, the estimated fractional reported symptomatic cases are between 10 to 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

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The effect of control measures on COVID-19 transmission in South Korea

PLoS ONE ◽

10.1371/journal.pone.0249262 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0249262

Author(s):

Taeyong Lee ◽

Hee-Dae Kwon ◽

Jeehyun Lee

Keyword(s):

Transmission Rate ◽

School Closure ◽

Control Measures ◽

After School ◽

Epidemic Threshold ◽

Social Distancing ◽

Control And Prevention ◽

Age Structured ◽

Net Reproduction ◽

The Impact

Countries around the world have taken control measures to mitigate the spread of COVID-19, including Korea. Social distancing is considered an essential strategy to reduce transmission in the absence of vaccination or treatment. While interventions have been successful in controlling COVID-19 in Korea, maintaining the current restrictions incurs great social costs. Thus, it is important to analyze the impact of different polices on the spread of the epidemic. To model the COVID-19 outbreak, we use an extended age-structured SEIR model with quarantine and isolation compartments. The model is calibrated to age-specific cumulative confirmed cases provided by the Korea Disease Control and Prevention Agency (KDCA). Four control measures—school closure, social distancing, quarantine, and isolation—are investigated. Because the infectiousness of the exposed has been controversial, we study two major scenarios, considering contributions to infection of the exposed, the quarantined, and the isolated. Assuming the transmission rate would increase more than 1.7 times after the end of social distancing, a second outbreak is expected in the first scenario. The epidemic threshold for increase of contacts between teenagers after school reopening is 3.3 times, which brings the net reproduction number to 1. The threshold values are higher in the second scenario. If the average time taken until isolation and quarantine reduces from three days to two, cumulative cases are reduced by 60% and 47% in the first scenario, respectively. Meanwhile, the reduction is 33% and 41%, respectively, for rapid isolation and quarantine in the second scenario. Without social distancing, a second wave is possible, irrespective of whether we assume risk of infection by the exposed. In the non-infectivity of the exposed scenario, early detection and isolation are significantly more effective than quarantine. Furthermore, quarantining the exposed is as important as isolating the infectious when we assume that the exposed also contribute to infection.

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Prediction of the COVID-19 epidemic trends based on SEIR and AI models

PLoS ONE ◽

10.1371/journal.pone.0245101 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0245101

Author(s):

Shuo Feng ◽

Zebang Feng ◽

Chen Ling ◽

Chen Chang ◽

Zhongke Feng

Keyword(s):

Mainland China ◽

Control Measures ◽

Chinese Government ◽

Epidemic Control ◽

Close Attention ◽

Intervention Measures ◽

The World ◽

The Impact ◽

The City

In December 2019, the outbreak of a new coronavirus-caused pneumonia (COVID-19) in Wuhan attracted close attention in China and the world. The Chinese government took strong national intervention measures on January 23 to control the spread of the epidemic. We are trying to show the impact of these controls on the spread of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The epidemic control measures taken by the Chinese government, especially the city closure measures, reduced the scale of the COVID-19 epidemic.

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A descriptive study of the impact of diseases control and prevention on the epidemics dynamics and clinical features of SARS-CoV-2 outbreak in Shanghai, lessons learned for metropolis epidemics prevention

10.1101/2020.02.19.20025031 ◽

2020 ◽

Cited By ~ 20

Author(s):

Hongzhou Lu ◽

Jingwen Ai ◽

Yinzhong Shen ◽

Yang Li ◽

Tao Li ◽

...

Keyword(s):

Clinical Features ◽

Transmission Rate ◽

Early Stage ◽

Disease Onset ◽

Descriptive Study ◽

Lessons Learned ◽

Control Measures ◽

Imported Cases ◽

Control And Prevention ◽

The Impact

AbstractObjectiveTo describe and evaluate the impact of diseases control and prevention on epidemics dynamics and clinical features of SARS-CoV-2 outbreak in Shanghai.DesignA retrospective descriptive studySettingChinaParticipantsEpidemiology information was collected from publicly accessible database. 265 patients admitted to Shanghai Public Health Center with confirmed COVID-19 were enrolled for clinical features analysis.Main outcome measurePrevention and control measures taken by Shanghai government, epidemiological, demographic, clinical, laboratory and radiology data were collected. Weibull distribution, Chi-square test, Fisher’s exact test, t test or Mann-Whitney U test were used in statistical analysis.ResultsCOVID-19 transmission rate within Shanghai had reduced over 99% than previous speculated, and the exponential growth has been stopped so far. Epidemic was characterized by the first stage mainly composed of imported cases and the second stage where >50% of cases were local. The incubation period was 6.4 (95% CI 5.3 to 7.6) days and the mean onset-admission interval was 5.5 days (95% CI, 5.1 to 5.9). Median time for COVID-19 progressed to severe diseases were 8.5 days (IQR: 4.8-11.0 days). By February 11th, proportion of patients being mild, moderate, severe and critically ill were 1.9%(5/265), 89.8%(238/265), 3.8%(10/265), 4.5%(12/265), respectively; 47 people in our cohort were discharged, and 1 patient died.ConclusionStrict controlling of the transmission rate at the early stage of an epidemic in metropolis can quickly prohibit the spread of the diseases. Controlling local clusters is the key to prevent outbreaks from imported cases. Most COVID-19 severe cases progressed within 14 days of disease onset. Multiple systemic laboratory abnormalities had been observed before significant respiratory dysfunction.

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On the study of the dynamic model of COVID-19 in Wuhan

10.21203/rs.3.rs-96422/v2 ◽

2020 ◽

Author(s):

Chong You ◽

Xin Gai ◽

Yuan Zhang ◽

Xiao-Hua Zhou

Keyword(s):

Dynamic Model ◽

Transmission Rate ◽

Control Measure ◽

Transmission Mechanism ◽

Control Measures ◽

Modified Model ◽

Pcr Screening ◽

Active Virus ◽

Current Outbreak ◽

And Control

Abstract The current outbreak of coronavirus disease 2019 (COVID-19) has quickly spread across countries and become a global crisis. Understanding the transmission mechanism and effects of interventions is critical to the prevention and control of the COVID-19 pandemic. A recent study by Hao et al (2020) provided an interesting perspective on the transmission dynamics of COVID-19 in Wuhan and inferred that 87% of the infections before 8 March 2020 were not laboratory-confirmed. However we believe that there are a few major issues due to the vagueness in the definitions of compartments and inconsistence in the settings of parameters. In this paper, we clarify the definitions of the model compartments and raise questions in regard to the underlying homogenous assumption within compartments and settings of the parameters in the dynamic model by Hao et al (2020), and furthermore offer a modified model to resolve these potential limitations. Compared with the model in Hao et al (2020), the active virus carriers were predicted to persist for a longer period in our model which is well consistent with the active virus carriers detected in Wuhan in mid-May. Our model suggests that control measures cannot be easily lifted while continuous efforts are needed to contain the spread of the pandemic; a universal PT-PCR screening is essential to detect hidden cases before lifting control measure. In addition, we also provide a possible solution to solve the problem of heterogeneity transmission rate in disease courses.

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Is Nigeria really on top of COVID-19? Message from effective reproduction number

Epidemiology and Infection ◽

10.1017/s0950268820001740 ◽

2020 ◽

Vol 148 ◽

Cited By ~ 3

Author(s):

A. I. Adekunle ◽

O. A. Adegboye ◽

E. Gayawan ◽

E. S. McBryde

Keyword(s):

Public Health ◽

Compartmental Model ◽

Reproduction Number ◽

Transmission Rate ◽

Credible Interval ◽

Control Measures ◽

Maximum Value ◽

Intervention Measures ◽

Death Cases ◽

Epidemiological Parameters

Abstract Following the importation of coronavirus disease (COVID-19) into Nigeria on 27 February 2020 and then the outbreak, the question is: How do we anticipate the progression of the ongoing epidemic following all the intervention measures put in place? This kind of question is appropriate for public health responses and it will depend on the early estimates of the key epidemiological parameters of the virus in a defined population. In this study, we combined a likelihood-based method using a Bayesian framework and compartmental model of the epidemic of COVID-19 in Nigeria to estimate the effective reproduction number (R(t)) and basic reproduction number (R0) – this also enables us to estimate the initial daily transmission rate (β0). We further estimate the reported fraction of symptomatic cases. The models are applied to the NCDC data on COVID-19 symptomatic and death cases from 27 February 2020 and 7 May 2020. In this period, the effective reproduction number is estimated with a minimum value of 0.18 and a maximum value of 2.29. Most importantly, the R(t) is strictly greater than one from 13 April till 7 May 2020. The R0 is estimated to be 2.42 with credible interval: (2.37–2.47). Comparing this with the R(t) shows that control measures are working but not effective enough to keep R(t) below 1. Also, the estimated fraction of reported symptomatic cases is between 10 and 50%. Our analysis has shown evidence that the existing control measures are not enough to end the epidemic and more stringent measures are needed.

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Interpreting the transmissibility of the avian influenza A(H7N9) infection from 2013 to 2015 in Zhejiang Province, China

Epidemiology and Infection ◽

10.1017/s0950268815002812 ◽

2015 ◽

Vol 144 (8) ◽

pp. 1584-1591 ◽

Cited By ~ 8

Author(s):

K. C. CHONG ◽

X. WANG ◽

S. LIU ◽

J. CAI ◽

X. SU ◽

...

Keyword(s):

Influenza A ◽

Transmission Rate ◽

Credible Interval ◽

Zhejiang Province ◽

Control Measures ◽

Posterior Mean ◽

Key Indicator ◽

Intervention Measures ◽

Live Poultry ◽

H7n9 Infection

SUMMARYThree epidemic waves of human influenza A(H7N9) were documented in several different provinces in China between 2013 and 2015. With limited understanding of the potential for human-to-human transmission, it was difficult to implement control measures efficiently or to inform the public adequately about the application of interventions. In this study, the human-to-human transmission rate for the epidemics that occurred between 2013 and 2015 in Zhejiang Province, China, was analysed. The reproduction number (R), a key indicator of transmission intensity, was estimated by fitting the number of infections from poultry to humans and from humans to humans into a mathematical model. The posterior mean R for human-to-human transmission was estimated to be 0·27, with a 95% credible interval of 0·14–0·44 for the first wave, whereas the posterior mean Rs decreased to 0·15 in the second and third waves. Overall, these estimates indicate that a human H7N9 pandemic is unlikely to occur in Zhejiang. The reductions in the viral transmissibility and the number of poultry-transmitted infections after the first epidemic may be attributable to the various intervention measures taken, including changes in the extent of closures of live poultry markets.

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Mathematical epidemiologic and simulation modelling of first wave COVID-19 in Malaysia

Scientific Reports ◽

10.1038/s41598-021-99541-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Muhammad Rezal Kamel Ariffin ◽

Kathiresan Gopal ◽

Isthrinayagy Krishnarajah ◽

Iszuanie Syafidza Che Ilias ◽

Mohd Bakri Adam ◽

...

Keyword(s):

Transmission Rate ◽

Mainland China ◽

Movement Control ◽

Simulation Modelling ◽

Sir Model ◽

Control Measures ◽

Cumulative Number ◽

Severity Of The Disease ◽

Control Order ◽

Second Wave

AbstractSince the first coronavirus disease 2019 (COVID-19) outbreak appeared in Wuhan, mainland China on December 31, 2019, the geographical spread of the epidemic was swift. Malaysia is one of the countries that were hit substantially by the outbreak, particularly in the second wave. This study aims to simulate the infectious trend and trajectory of COVID-19 to understand the severity of the disease and determine the approximate number of days required for the trend to decline. The number of confirmed positive infectious cases [as reported by Ministry of Health, Malaysia (MOH)] were used from January 25, 2020 to March 31, 2020. This study simulated the infectious count for the same duration to assess the predictive capability of the Susceptible-Infectious-Recovered (SIR) model. The same model was used to project the simulation trajectory of confirmed positive infectious cases for 80 days from the beginning of the outbreak and extended the trajectory for another 30 days to obtain an overall picture of the severity of the disease in Malaysia. The transmission rate, β also been utilized to predict the cumulative number of infectious individuals. Using the SIR model, the simulated infectious cases count obtained was not far from the actual count. The simulated trend was able to mimic the actual count and capture the actual spikes approximately. The infectious trajectory simulation for 80 days and the extended trajectory for 110 days depicts that the inclining trend has peaked and ended and will decline towards late April 2020. Furthermore, the predicted cumulative number of infectious individuals tallies with the preparations undertaken by the MOH. The simulation indicates the severity of COVID-19 disease in Malaysia, suggesting a peak of infectiousness in mid-March 2020 and a probable decline in late April 2020. Overall, the study findings indicate that outbreak control measures such as the Movement Control Order (MCO), social distancing and increased hygienic awareness is needed to control the transmission of the outbreak in Malaysia.

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On the study of full transmission dynamics of COVID-19 in Wuhan

10.21203/rs.3.rs-96422/v1 ◽

2020 ◽

Author(s):

Chong You ◽

Xin Gai ◽

Yuan Zhang ◽

Xiao-Hua Zhou

Keyword(s):

Transmission Rate ◽

Control Measure ◽

Transmission Mechanism ◽

Control Measures ◽

Transmission Dynamics ◽

Modified Model ◽

Pcr Screening ◽

Active Virus ◽

Current Outbreak ◽

And Control

Abstract The current outbreak of coronavirus disease 2019 (COVID-19) has quickly spread across countries and become a global crisis. Understanding the transmission mechanism and effects of interventions is critical to the prevention and control of the COVID-19 pandemic. A recent study by Hao et al (2020) provided an interesting perspective on the transmission dynamics of COVID-19 in Wuhan and inferred that 87% of the infections before 8 March 2020 were not laboratory-confirmed. However we believe that there are a few major issues due to the vagueness in the definitions of compartments and inconsistence in the settings of parameters. In this paper, we clarify the definitions of the model compartments and raise questions in regard to the underlying homogenous assumption within compartments and settings of the parameters in the dynamic model by Hao et al (2020), and furthermore offer a modified model to resolve these potential limitations. Compared with the model in Hao et al (2020), the active virus carriers were predicted to persist for a longer period in our model which is well consistent with the active virus carriers detected in Wuhan in mid-May. Our model suggests that control measures cannot be easily lifted while continuous efforts are needed to contain the spread of the pandemic; a universal PT-PCR screening is essential to detect hidden cases before lifting control measure. In addition, we also provide a possible solution to solve the problem of heterogeneity transmission rate in disease courses.

Download Full-text