scholarly journals Tracking and Predicting COVID-19 Epidemic in China Mainland

2020 ◽  
Author(s):  
Haoxuan Sun ◽  
Yumou Qiu ◽  
Han Yan ◽  
Yaxuan Huang ◽  
Yuru Zhu ◽  
...  
Keyword(s):  
Reproduction Number ◽  
Removal Rate ◽  
Public Awareness ◽  
Hubei Province ◽  
Control Measures ◽  
Slowing Down ◽  
Substantial Decline ◽  
China Mainland ◽  
High Level ◽  
Removal Model

AbstractBy proposing a varying coefficient Susceptible-Infected-Removal model (vSIR), we track the epidemic of COVID-19 in 30 provinces in China and 15 cities in Hubei province, the epicenter of the outbreak. It is found that the spread of COVID-19 has been significantly slowing down within the two weeks from January 27 to February 10th with 87.0% and 84.3% reductions in the reproduction number R0 among the 30 provinces and 15 Hubei cities, respectively. This suggests the extreme control measures implemented since January 23, which include cutting off Wuhan and many other cities and towns, a great public awareness and high level of self isolation at home, have contributed to a substantial decline in the reproductivity of the COVID-19 in China. We predict that Hubei province will reach its peak between February 20 and 22, 2020, and if the removal rate can be increased to 0.1, the epidemic outside Hubei province will end in May 2020, and inside Hubei in early June.

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 The spatial transmission of SARS-CoV-2 in China under the prevention and control measures at the early outbreak

2021 ◽  
Vol 79 (1) ◽  
Author(s):  
Jianli Liu ◽  
Yuan Zhou ◽  
Chuanyu Ye ◽  
Guangming Zhang ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Prevention And Control ◽  
Reproduction Number ◽  
Early Stage ◽  
Chinese Mainland ◽  
Hubei Province ◽  
Control Measures ◽  
Provincial Level ◽  
Prevention And Control Measures ◽  
And Control

Abstract Background Since severe acute respiratory syndrome coronavirus, 2 (SARS-CoV-2) was firstly reported in Wuhan City, China in December 2019, Novel Coronavirus Disease 2019 (COVID-19) that is caused by SARS-CoV-2 is predominantly spread from person-to-person on worldwide scales. Now, COVID-19 is a non-traditional and major public health issue the world is facing, and the outbreak is a global pandemic. The strict prevention and control measures have mitigated the spread of SARS-CoV-2 and shown positive changes with important progress in China. But prevention and control tasks remain arduous for the world. The objective of this study is to discuss the difference of spatial transmission characteristics of COVID-19 in China at the early outbreak stage with resolute efforts. Simultaneously, the COVID-19 trend of China at the early time was described from the statistical perspective using a mathematical model to evaluate the effectiveness of the prevention and control measures. Methods In this study, the accumulated number of confirmed cases publicly reported by the National Health Committee of the People’s Republic of China (CNHC) from January 20 to February 11, 2020, were grouped into three partly overlapping regions: Chinese mainland including Hubei province, Hubei province alone, and the other 30 provincial-level regions on Chinese mainland excluding Hubei province, respectively. A generalized-growth model (GGM) was used to estimate the basic reproduction number to evaluate the transmissibility in different spatial locations. The prevention and control of COVID-19 in the early stage were analyzed based on the number of new cases of confirmed infections daily reported. Results Results indicated that the accumulated number of confirmed cases reported from January 20 to February 11, 2020, is well described by the GGM model with a larger correlation coefficient than 0.99. When the accumulated number of confirmed cases is well fitted by an exponential function, the basic reproduction number of COVID-19 of the 31 provincial-level regions on the Chinese mainland, Hubei province, and the other 30 provincial-level regions on the Chinese mainland excluding Hubei province, is 2.68, 6.46 and 2.18, respectively. The consecutive decline of the new confirmed cases indicated that the prevention and control measures taken by the Chinese government have contained the spread of SARS-CoV-2 in a short period. Conclusions The estimated basic reproduction number thorough GGM model can reflect the spatial difference of SARS-CoV-2 transmission in China at the early stage. The strict prevention and control measures of SARS-CoV-2 taken at the early outbreak can effectively reduce the new confirmed cases outside Hubei and have mitigated the spread and yielded positive results since February 2, 2020. The research results indicated that the outbreak of COVID-19 in China was sustaining localized at the early outbreak stage and has been gradually curbed by China’s resolute efforts.

scholarly journals Dynamics of an SIR-Based COVID-19 Model With Linear Incidence Rate, Nonlinear Removal Rate, and Public Awareness

2021 ◽  
Vol 9 ◽  
Author(s):  
Abdelhamid Ajbar ◽  
Rubayyi T. Alqahtani ◽  
Mourad Boumaza
Keyword(s):  
Incidence Rate ◽  
Reproduction Number ◽  
Removal Rate ◽  
Public Awareness ◽  
Sir Model ◽  
Hopf Bifurcations ◽  
Nonlinear Phenomena ◽  
Hospital Bed ◽  
Population Ratio ◽  
Effects Of Media

The paper studies the dynamics of the classical susceptible-infectious-removed (SIR) model when applied to the transmission of COVID-19 disease. The model includes the classical linear incidence rate but considers a nonlinear removal rate that depends on the hospital-bed population ratio. The model also includes the effects of media on public awareness. We prove that when the basic reproduction number is less than unity the model can exhibit a number of nonlinear phenomena including saddle-node, backward, and Hopf bifurcations. The model is fitted to COVID-19 data pertinent to Saudi Arabia. Numerical simulations are provided to supplement the theoretical analysis and delineate the effects of hospital-bed population ratio and public awareness on the control of the disease.

scholarly journals Transmission characteristics of the COVID-19 outbreak in China: a study driven by data

2020 ◽  
Author(s):  
Meili Li ◽  
Pian Chen ◽  
Qianqian Yuan ◽  
Baojun Song ◽  
Junling Ma
Keyword(s):  
Incubation Period ◽  
Symptom Onset ◽  
First Generation ◽  
Reproduction Number ◽  
Public Awareness ◽  
Control Measures ◽  
Transmission Characteristics ◽  
Median Period ◽  
Sources Of Infection ◽  
And Control

Abstract Background The COVID-19 outbreak has been a serious public health threat worldwide. Individually documented case descriptions of COVID-19 are published by Chinese provinces (excluding Hubei). We use these descriptions to study the transmission characteristics in China, and how they are influenced by public awareness and control measures. Methods Dates for infection, symptom onset, quarantine, hospitalization and diagnosis, and sources of infection are tabulated from published cases descriptions. We use MCMC to estimate the exponential growth rate of cases infected in Hubei, and parametrize the distributions for the incubation period, and periods from symptom onset to hospitalization and diagnosis. Graph of infection is constructed and used to estimate the reproduction number in each generation. Results The median incubation period is 6.2 days overall, and 7.3% patients have an incubation period longer than the recommended 14-day quarantine period. The median period from symptom onset to hospitalization is 4.4 days before the lockdown of Wuhan city, and 2.1 days after the lockdown. The median period from symptom onset to diagnosis is 8.8 days before the lockdown, and 4.8 days after the lockdown. The number of cases in Hubei doubled every 3 days before the lockdown. In other provinces, reproduction number decreased from 1.51 in the first-generation patients to less than 0.21 in later generation patients. Conclusions We parametrized incubation period, and the periods from symptom onset to hospitalization and diagnosis. Some (possibly a majority of) patients were infected before the symptom onset of their sources of infection, causing a low success rate of official (not self) quarantines. Social distancing and self-quarantine greatly reduced the reproduction number, and shorted the periods from symptom onset to hospitalization and diagnosis.

scholarly journals Investigation of the Efficiency of Mask Wearing, Contact Tracing, and Case Isolation during the COVID-19 Outbreak

2021 ◽  
Vol 10 (13) ◽  
pp. 2761
Author(s):  
Tatiana Filonets ◽  
Maxim Solovchuk ◽  
Wayne Gao ◽  
Tony Wen-Hann Sheu
Keyword(s):  
Mathematical Model ◽  
Reproduction Number ◽  
Control Measures ◽  
Disease Spread ◽  
Contact Tracing ◽  
Imported Cases ◽  
Quantitative Results ◽  
High Level ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Case isolation and contact tracing are two essential parts of control measures to prevent the spread of COVID-19, however, additional interventions, such as mask wearing, are required. Taiwan successfully contained local COVID-19 transmission after the initial imported cases in the country in early 2020 after applying the above-mentioned interventions. In order to explain the containment of the disease spread in Taiwan and understand the efficiency of different non-pharmaceutical interventions, a mathematical model has been developed. A stochastic model was implemented in order to estimate the effectiveness of mask wearing together with case isolation and contact tracing. We investigated different approaches towards mask usage, estimated the effect of the interventions on the basic reproduction number (R0), and simulated the possibility of controlling the outbreak. With the assumption that non-medical and medical masks have 20% and 50% efficiency, respectively, case isolation works on 100%, 70% of all people wear medical masks, and R0 = 2.5, there is almost 80% probability of outbreak control with 60% contact tracing, whereas for non-medical masks the highest probability is only about 20%. With a large proportion of infectiousness before the onset of symptoms (40%) and the presence of asymptomatic cases, the investigated interventions (isolation of cases, contact tracing, and mask wearing by all people), implemented on a high level, can help to control the disease spread. Superspreading events have also been included in our model in order to estimate their impact on the outbreak and to understand how restrictions on gathering and social distancing can help to control the outbreak. The obtained quantitative results are in agreement with the empirical COVID-19 data in Taiwan.

scholarly journals A deterministic epidemic model for the emergence of COVID-19 in China

2020 ◽  
Author(s):  
Meng Wang ◽  
Jingtao Qi
Keyword(s):  
Prevention And Control ◽  
Reproduction Number ◽  
Chinese Mainland ◽  
Hubei Province ◽  
Control Measures ◽  
The Novel ◽  
Health Authorities ◽  
Prevention And Control Measures ◽  
Novel Coronavirus ◽  
And Control

AbstractCoronavirus disease (COVID-19) broke out in Wuhan, Hubei province, China, in December 2019 and soon after Chinese health authorities took unprecedented prevention and control measures to curb the spreading of the novel coronavirus-related pneumonia. We develop a mathematical model based on daily updates of reported cases to study the evolution of the epidemic. With the model, on 95% confidence level, we estimate the basic reproduction number, R0 = 2.82 ± 0.11, time between March 19 and March 21 when the effective reproduction number becoming less than one, the epidemic ending after April 2 and the total number of confirmed cases approaching 14408 ± 429 on the Chinese mainland excluding Hubei province.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Relations between carbon removal rates, biofilm size and density of a novel anaerobic reactor: the inverse turbulent bed

2000 ◽  
Vol 41 (4-5) ◽  
pp. 253-260 ◽  
Author(s):  
P. Buffière ◽  
R. Moletta
Keyword(s):  
Removal Rate ◽  
Removal Rates ◽  
Carbon Removal ◽  
Biofilm Growth ◽  
Loading Rates ◽  
Biomass Activity ◽  
High Turbulence ◽  
The One ◽  
High Level ◽  
Very High

An anaerobic inverse turbulent bed, in which the biogas only ensures fluidisation of floating carrier particles, was investigated for carbon removal kinetics and for biofilm growth and detachment. The range of operation of the reactor was kept within 5 and 30 kgCOD· m−3· d−1, with Hydraulic Retention Times between 0.28 and 1 day. The carbon removal efficiency remained between 70 and 85%. Biofilm size were rather low (between 5 and 30 μm) while biofilm density reached very high values (over 80 kgVS· m−3). The biofilm size and density varied with increasing carbon removal rates with opposite trends; as biofilm size increases, its density decreases. On the one hand, biomass activity within the reactor was kept at a high level, (between 0.23 and 0.75 kgTOC· kgVS· d−1, i.e. between 0.6 and 1.85 kgCOD·kgVS · d−1).This result indicates that high turbulence and shear may favour growth of thin, dense and active biofilms. It is thus an interesting tool for biomass control. On the other hand, volatile solid detachment increases quasi linearly with carbon removal rate and the total amount of solid in the reactor levels off at high OLR. This means that detachment could be a limit of the process at higher organic loading rates.

scholarly journals Harm Reduction for Adolescents and Young Adults During the COVID-19 Pandemic: A Case Study of Community Care in Reach

2021 ◽  
pp. 003335492199939
Author(s):  
Elizabeth Noyes ◽  
Ellis Yeo ◽  
Megan Yerton ◽  
Isabel Plakas ◽  
Susan Keyes ◽  
...  
Keyword(s):  
Young Adults ◽  
Harm Reduction ◽  
Community Care ◽  
Control Measures ◽  
Adolescents And Young Adults ◽  
People Who Use Drugs ◽  
Staff Members ◽  
Infection Control Measures ◽  
Semistructured Interviews ◽  
High Level

The coronavirus disease 2019 (COVID-19) pandemic has challenged the ability of harm reduction programs to provide vital services to adolescents, young adults, and people who use drugs, thereby increasing the risk of overdose, infection, withdrawal, and other complications of drug use. To evaluate the effect of the COVID-19 pandemic on harm reduction services for adolescents and young adults in Boston, we conducted a quantitative assessment of the Community Care in Reach (CCIR) youth pilot program to determine gaps in services created by its closure during the peak of the pandemic (March 19–June 21, 2020). We also conducted semistructured interviews with staff members at 6 harm reduction programs in Boston from April 27 through May 4, 2020, to identify gaps in harm reduction services, changes in substance use practices and patterns of engagement with people who use drugs, and how harm reduction programs adapted to pandemic conditions. During the pandemic, harm reduction programs struggled to maintain staffing, supplies, infection control measures, and regular connection with their participants. During the 3-month suspension of CCIR mobile van services, CCIR missed an estimated 363 contacts, 169 units of naloxone distributed, and 402 syringes distributed. Based on our findings, we propose the following recommendations for sustaining harm reduction services during times of crisis: pursuing high-level policy changes to eliminate political barriers to care and fund harm reduction efforts; enabling and empowering harm reduction programs to innovatively and safely distribute vital resources and build community during a crisis; and providing comprehensive support to people to minimize drug-related harms.

scholarly journals Analysis and prediction of the coronavirus disease epidemic in China based on an individual-based model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zuiyuan Guo ◽  
Dan Xiao
Keyword(s):  
Reproduction Number ◽  
Control Measures ◽  
Cumulative Number ◽  
Individual Based Model ◽  
Whole Process ◽  
Coordinate Descent Algorithm ◽  
Initial Stage ◽  
The Government ◽  
Prevention And Control Measures ◽  
And Control

AbstractWe established a stochastic individual-based model and simulated the whole process of occurrence, development, and control of the coronavirus disease epidemic and the infectors and patients leaving Hubei Province before the traffic was closed in China. Additionally, the basic reproduction number (R0) and number of infectors and patients who left Hubei were estimated using the coordinate descent algorithm. The median R0 at the initial stage of the epidemic was 4.97 (95% confidence interval [CI] 4.82–5.17). Before the traffic lockdown was implemented in Hubei, 2000 (95% CI 1982–2030) infectors and patients had left Hubei and traveled throughout the country. The model estimated that if the government had taken prevention and control measures 1 day later, the cumulative number of laboratory-confirmed patients in the whole country would have increased by 32.1%. If the lockdown of Hubei was imposed 1 day in advance, the cumulative number of laboratory-confirmed patients in other provinces would have decreased by 7.7%. The stochastic model could fit the officially issued data well and simulate the evolution process of the epidemic. The intervention measurements nationwide have effectively curbed the human-to-human transmission of severe acute respiratory syndrome coronavirus 2.

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