scholarly journals A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China

2020 ◽  
Author(s):  
Chenjing Shang ◽  
Yang Yang ◽  
Gui-Ying Chen ◽  
Xiao-Dong Shang
Keyword(s):  
Growth Rate ◽  
Prediction Models ◽  
Mainland China ◽  
Health Sector ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Disease Spread ◽  
Chinese Government ◽  
Model Parameters ◽  
Estimation Of Model Parameters

Abstract Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modeling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91,253, and the maximum number of beds required for hospitalized patients would be 62,794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resourceallocation and planning in the health sector while providing a theoretical basis forgovernments to respond to future global health crises or epidemics.

scholarly journals A simple transmission dynamics model for predicting the evolution of COVID-19 under control measures in China

2021 ◽  
Vol 149 ◽  
Author(s):  
Chenjing Shang ◽  
Yang Yang ◽  
Gui-Ying Chen ◽  
Xiao-Dong Shang
Keyword(s):  
Growth Rate ◽  
Prediction Models ◽  
Mainland China ◽  
Health Sector ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Disease Spread ◽  
Chinese Government ◽  
Model Parameters ◽  
Hospitalised Patients

Abstract Epidemic forecasting provides an opportunity to predict geographic disease spread and counts when an outbreak occurs and plays a key role in preventing or controlling their adverse impact. However, conventional prediction models based on complex mathematical modelling rely on the estimation of model parameters, which yields unreliable and unsustainable results. Herein, we proposed a simple model for predicting the epidemic transmission dynamics based on nonlinear regression of the epidemic growth rate and iterative methods, which is applicable to the progression of the COVID-19 outbreak under the strict control measures of the Chinese government. Our model yields reliable and accurate results as confirmed by the available data: we predicted that the total number of infections in mainland China would be 91 253, and the maximum number of beds required for hospitalised patients would be 62 794. We inferred that the inflection point (when the growth rate turns from positive to negative) of the epidemic across China would be mid-February, and the end of the epidemic would be in late March. This model is expected to contribute to resource allocation and planning in the health sector while providing a theoretical basis for governments to respond to future global health crises or epidemics.

TRANSMISSION DYNAMICS AND CONTROL STRATEGIES OF COVID-19 IN WUHAN, CHINA

2020 ◽  
Vol 28 (03) ◽  
pp. 543-560 ◽  
Author(s):  
LIUYONG PANG ◽  
SANHONG LIU ◽  
XINAN ZHANG ◽  
TIANHAI TIAN ◽  
ZHONG ZHAO
Keyword(s):  
Reproduction Number ◽  
Control Strategies ◽  
Public Awareness ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Published Data ◽  
Model Parameters ◽  
Strict Control ◽  
Dynamics And Control ◽  
Numerical Calculation Method

In December 2019, a novel coronavirus, SARS-COV-2, was identified among patients in Wuhan, China. Two strict control measures, i.e., putting Wuhan on lockdown and taking strict quarantine rule, were carried out to contain the spread of COVID-19. Based on the different control measures, we divided the transmission process of COVID-19 into three stages. An SEIHR model was established to describe the transmission dynamics and was applied to fit the published data on the confirmed cases of Wuhan city from December 31, 2019 to March 25, 2020 to deduce the time when the first patient with COVID-19 appeared. The basic reproduction number was estimated in the first stage to demonstrate the number of secondary infectious cases generated by an average infectious case in the absence of policy intervention. The effective reproduction numbers in second and third stages were estimated to evaluate the effects of the two strict control measures. In addition, sensitivity analysis of the reproduction number according to model parameters was executed to demonstrate the effect of the control measures for containing the spread of COVID-19. Finally, the numerical calculation method was applied to investigate the influence of the different control measures on the spread of COVID-19. The results indicated that following the strict quarantine rule was very effective, and reducing the effective contact rates and improving the diagnosis rate were crucial in reducing the effective reproduction number, and taking control measures as soon as possible can effectively contain a larger outbreak of COVID-19. But a bigger challenge for us to contain the spread of COVID-19 was the transmission from the asymptomatic carriers, which required to raising the public awareness of self-protection and keeping a good physical protection.

scholarly journals Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak

2020 ◽  
Vol 117 (13) ◽  
pp. 7504-7509 ◽  
Author(s):  
Chad R. Wells ◽  
Pratha Sah ◽  
Seyed M. Moghadas ◽  
Abhishek Pandey ◽  
Affan Shoukat ◽  
...  
Keyword(s):  
Mainland China ◽  
International Travel ◽  
Control Measures ◽  
Chinese Government ◽  
Border Control ◽  
The Novel ◽  
Public Health Response ◽  
Global Spread ◽  
Travel Restrictions ◽  
The Impact

The novel coronavirus outbreak (COVID-19) in mainland China has rapidly spread across the globe. Within 2 mo since the outbreak was first reported on December 31, 2019, a total of 566 Severe Acute Respiratory Syndrome (SARS CoV-2) cases have been confirmed in 26 other countries. Travel restrictions and border control measures have been enforced in China and other countries to limit the spread of the outbreak. We estimate the impact of these control measures and investigate the role of the airport travel network on the global spread of the COVID-19 outbreak. Our results show that the daily risk of exporting at least a single SARS CoV-2 case from mainland China via international travel exceeded 95% on January 13, 2020. We found that 779 cases (95% CI: 632 to 967) would have been exported by February 15, 2020 without any border or travel restrictions and that the travel lockdowns enforced by the Chinese government averted 70.5% (95% CI: 68.8 to 72.0%) of these cases. In addition, during the first three and a half weeks of implementation, the travel restrictions decreased the daily rate of exportation by 81.3% (95% CI: 80.5 to 82.1%), on average. At this early stage of the epidemic, reduction in the rate of exportation could delay the importation of cases into cities unaffected by the COVID-19 outbreak, buying time to coordinate an appropriate public health response.

scholarly journals Lessons from mainland China’s epidemic experience about the growth rules of infected and recovered cases of COVID-19 worldwide

2020 ◽  
Author(s):  
Chuanliang Han ◽  
Yimeng Liu ◽  
Jiting Tang ◽  
Yuyao Zhu ◽  
Carlo Jaeger ◽  
...  
Keyword(s):  
Growth Rate ◽  
Strong Evidence ◽  
Logistic Model ◽  
Mainland China ◽  
Linear Connection ◽  
Control Measures ◽  
Logistic Growth ◽  
Strong Positive Correlation ◽  
The Novel ◽  
Novel Coronavirus

AbstractThe novel coronavirus disease (COVID-19) that emerged at the end of 2019 has been controlled in mainland China so far, while it is still spreading globally. When the pandemic will end is a question of great concern. A logistic model depicting the growth rules of infected and recovered cases in mainland China may shed some light on this question. We extended this model to 31 countries outside China experiencing serious COVID-2019 outbreaks. The model well explained the data in our study (R2 ≥ 0.95). For infected cases, the semi-saturation period (SSP) ranges from 63 to 170 days (March 3 to June 18). The logistic growth rate of infected cases is positively correlated with that of recovered cases, and the same holds for the SSP. According to the linear connection between the growth rules for infected and recovered cases identified from the Chinese data, we predicted that the SSP of the recovered cases outside China ranges from 82 to 196 days (March 22 to July 8). More importantly, we found a strong positive correlation between the SSP of infected cases and the timing of government’s response, providing strong evidence for the effectiveness of rapid epidemic control measures in various countries.

scholarly journals Risk estimation and prediction by modeling the transmission of the novel coronavirus (COVID-19) in mainland China excluding Hubei province

2020 ◽  
Author(s):  
Hui Wan ◽  
Jing-an Cui ◽  
Guo-jing Yang
Keyword(s):  
Mainland China ◽  
Hubei Province ◽  
Control Measures ◽  
Contact Tracing ◽  
Published Data ◽  
Chinese Government ◽  
Personal Protection ◽  
Protection Measures ◽  
Reproduction Ratio ◽  
Self Protection

AbstractBackgroundIn December 2019, an outbreak of coronavirus disease (COVID-19) was identified in Wuhan, China and, later on, detected in other parts of China. Our aim is to evaluate the effectiveness of the evolution of interventions and self-protection measures, estimate the risk of partial lifting control measures and predict the epidemic trend of the virus in mainland China excluding Hubei province based on the published data and a novel mathematical model.MethodsA novel COVID-19 transmission dynamic model incorporating the intervention measures implemented in China is proposed. COVID-19 daily data of mainland China excluding Hubei province, including the cumulative confirmed cases, the cumulative deaths, newly confirmed cases and the cumulative recovered cases for the period January 20th-March 3rd, 2020, were archived from the National Health Commission of China (NHCC). We parameterize the model by using the Markov Chain Monte Carlo (MCMC) method and estimate the control reproduction number Rc, as well as the effective daily reproduction ratio Re(t), of the disease transmission in mainland China excluding Hubei province.ResultsThe estimation outcomes indicate that Rc is 3.36 (95% CI 3.20-3.64) and Re(t) has dropped below 1 since January 31st, 2020, which implies that the containment strategies implemented by the Chinese government in mainland China excluding Hubei province are indeed effective and magnificently suppressed COVID-19 transmission. Moreover, our results show that relieving personal protection too early may lead to the spread of disease for a longer time and more people would be infected, and may even cause epidemic or outbreak again. By calculating the effective reproduction ratio, we prove that the contact rate should be kept at least less than 30% of the normal level by April, 2020.ConclusionsTo ensure the epidemic ending rapidly, it is necessary to maintain the current integrated restrict interventions and self-protection measures, including travel restriction, quarantine of entry, contact tracing followed by quarantine and isolation and reduction of contact, like wearing masks, etc. People should be fully aware of the real-time epidemic situation and keep sufficient personal protection until April. If all the above conditions are met, the outbreak is expected to be ended by April in mainland China apart from Hubei province.

Transmission Dynamics of COVID-19 in Mainland China: Impact of Public Health Control Measures

2020 ◽  
Author(s):  
Lin Zhao ◽  
Yun-Xia Liu ◽  
Jia-Te Wei ◽  
Yu-Chen Zhu ◽  
Jie Qian ◽  
...  
Keyword(s):  
Public Health ◽  
Mainland China ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Health Control

scholarly journals On the Transmission Dynamics of SARS-CoV-2 in a Temperate Climate

2021 ◽  
Vol 18 (4) ◽  
pp. 1660
Author(s):  
Ioannis Kioutsioukis ◽  
Nikolaos I. Stilianakis
Keyword(s):  
Dynamic Change ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Temperate Climate ◽  
Model Parameters ◽  
Forecast Horizon ◽  
Public Health Decision ◽  
Epidemic Dynamics ◽  
Positive Effects ◽  
Household Crowding

An epidemiological model, which describes the transmission dynamics of SARS-CoV-2 under specific consideration of the incubation period including the population with subclinical infections and being infective is presented. The COVID-19 epidemic in Greece was explored through a Monte Carlo uncertainty analysis framework, and the optimal values for the parameters that determined the transmission dynamics could be obtained before, during, and after the interventions to control the epidemic. The dynamic change of the fraction of asymptomatic individuals was shown. The analysis of the modelling results at the intra-annual climatic scale allowed for in depth investigation of the transmission dynamics of SARS-CoV-2 and the significance and relative importance of the model parameters. Moreover, the analysis at this scale incorporated the exploration of the forecast horizon and its variability. Three discrete peaks were found in the transmission rates throughout the investigated period (15 February–15 December 2020). Two of them corresponded to the timing of the spring and autumn epidemic waves while the third one occurred in mid-summer, implying that relaxation of social distancing and increased mobility may have a strong effect on rekindling the epidemic dynamics offsetting positive effects from factors such as decreased household crowding and increased environmental ultraviolet radiation. In addition, the epidemiological state was found to constitute a significant indicator of the forecast reliability horizon, spanning from as low as few days to more than four weeks. Embedding the model in an ensemble framework may extend the predictability horizon. Therefore, it may contribute to the accuracy of health risk assessment and inform public health decision making of more efficient control measures.

scholarly journals Assessing the effects of time-dependent restrictions and control actions to flatten the curve of COVID-19 in Kazakhstan

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10806
Author(s):  
Ton Duc Do ◽  
Meei Mei Gui ◽  
Kok Yew Ng
Keyword(s):  
Ad Hoc ◽  
Reproduction Number ◽  
National Level ◽  
Control Measures ◽  
Time Dependent ◽  
Transmission Dynamics ◽  
Model Parameters ◽  
Real World Data ◽  
Control Actions ◽  
And Control

This article presents the assessment of time-dependent national-level restrictions and control actions and their effects in fighting the COVID-19 pandemic. By analysing the transmission dynamics during the first wave of COVID-19 in the country, the effectiveness of the various levels of control actions taken to flatten the curve can be better quantified and understood. This in turn can help the relevant authorities to better plan for and control the subsequent waves of the pandemic. To achieve this, a deterministic population model for the pandemic is firstly developed to take into consideration the time-dependent characteristics of the model parameters, especially on the ever-evolving value of the reproduction number, which is one of the critical measures used to describe the transmission dynamics of this pandemic. The reproduction number alongside other key parameters of the model can then be estimated by fitting the model to real-world data using numerical optimisation techniques or by inducing ad-hoc control actions as recorded in the news platforms. In this article, the model is verified using a case study based on the data from the first wave of COVID-19 in the Republic of Kazakhstan. The model is fitted to provide estimates for two settings in simulations; time-invariant and time-varying (with bounded constraints) parameters. Finally, some forecasts are made using four scenarios with time-dependent control measures so as to determine which would reflect on the actual situations better.

scholarly journals The Change of Public Individual Prevention Practice and Psychological Effect From the Early Outbreak Stage to the Controlled Stage of COVID-19 in China in 2020: Two Cross-Sectional Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Bingfeng Han ◽  
Hanyu Liu ◽  
Tianshuo Zhao ◽  
Bei Liu ◽  
Hui Zheng ◽  
...  
Keyword(s):  
Hand Hygiene ◽  
Mainland China ◽  
Population Based ◽  
Demographic Characteristics ◽  
Psychological Effect ◽  
Control Measures ◽  
Chinese Government ◽  
Cross Sectional ◽  
Prevention And Control Measures ◽  
High Level

BackgroundCOVID-19 broke out in China and spread rapidly in January and February 2020. Following the prevention and control measures of the Chinese government, the outbreak was gradually brought under control after March. The changes in people’s attention to the epidemic, individual prevention practice and psychological effect from the early outbreak stage to the under controlled stage need to be evaluated.MethodsTwo cross-sectional, population-based online surveys were conducted from January 28 to February 1, 2020 and from February1 to March 18, 2020. Socio-demographic information and individual protective practice were collected and the State-Trait Anxiety Inventory (STAI) was used for measuring anxiety. The range of STAI score was 5–25, and the higher the score, the more anxious it was. The respondents of the two surveys were matched on a one-to-one basis according to their province, gender, age, education, and marriage. Wilcoxon signed ranks test and Mann-Whitney U test were used to compare STAI score changes in two stages and in different demographic characteristics.ResultsWe included 9,764 individuals in the first survey and 1,669 in the second survey, covering 30 provincial administrative regions in Mainland China. COVID-19 has affected almost every aspect of people’s normal life, especially lifestyle. The proportion of people who paid attention to it every day had dropped from 97.6 to 88.9%. We identified that vast majority people wore masks when they went out. The proportion has declined from 96.5 to 92.4% for hand hygiene and from 98.4 to 95.3% for not attending parties. People’s anxiety (STAI score) across the country has decreased from a median of 19 in the early outbreak stage to a median of 12, including people with all demographic characteristics, but some have increased in 16 provinces.ConclusionPeople’s attention to information about the epidemic has declined slightly, but a high proportion of people maintained good practices such as wearing masks, hand hygiene, and not attending parties. People’s anxiety had generally declined from the early outbreak stage to the under controlled stage, but it was still at a high level.

scholarly journals Reconstruction of the transmission dynamics of the first COVID-19 epidemic wave in Thailand

2021 ◽  
Author(s):  
Chaiwat Wilasang ◽  
Natcha Jitsuk ◽  
Chayanin Sararat ◽  
Charin Modchang
Keyword(s):  
Disease Transmission ◽  
Infected Individual ◽  
Mainland China ◽  
Stochastic Modelling ◽  
Epidemiological Data ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Thai Government ◽  
And Control ◽  
Epidemic Wave

Abstract Thailand was the first country reporting the first Coronavirus disease 2019 (COVID-19) infected individual outside mainland China. Here we delineated the course of the COVID-19 outbreak together with the timeline of the control measures and public health policies employed by the Thai government during the first wave of the COVID-19 outbreak in Thailand. Based on the comprehensive epidemiological data, we reconstructed the dynamics of COVID-19 transmission in Thailand using a stochastic modelling approach. Our stochastic model incorporated effects of individual heterogeneity in infectiousness on the disease transmission, which allows us to capture relevant features of superspreading events. We found that our model could accurately capture the transmission dynamics of the first COVID-19 epidemic wave in Thailand. The model predicted that at the end of the first wave, the number of cumulative confirmed cases was 3,091 (95%CI: 2,782 - 3,400). We also estimated the time-varying reproduction number (Rt) during the first epidemic wave. We found that after implementing the nationwide interventions, the Rt in Thailand decreased from the peak value of 5.67 to a value below one in less than one month, indicating that the control measures employed by the Thai government during the first COVID-19 epidemic wave were effective. Finally, effects of transmission heterogeneity and control measures on the likelihood of outbreak extinction were also investigated.

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