An Updated Trend Analysis Representing the Outbreak of Novel Coronavirus (2019-nCoV) in 16 Cities of Hubei Province, China Using Logistic S-Curve Model

2020 ◽  
Author(s):  
Muhammad Fawad ◽  
Sumaira Mubarik ◽  
Saima Shakil Malik ◽  
Yangyang Hao ◽  
Chuanhua Yu ◽  
...  
Keyword(s):  
Trend Analysis ◽  
Hubei Province ◽  
Curve Model ◽  
S Curve ◽  
Novel Coronavirus

scholarly journals Epidemiological Development of Novel Coronavirus Pneumonia in China and Its Forecast

2020 ◽  
Author(s):  
Shanshan Wu ◽  
Panpan Sun ◽  
Ruiling Li ◽  
Liang Zhao ◽  
Yanli Wang ◽  
...  
Keyword(s):  
Infection Intensity ◽  
Hubei Province ◽  
Henan Province ◽  
Peak Time ◽  
Republic Of China ◽  
Useful Knowledge ◽  
S Curve ◽  
The Government ◽  
Epidemic Development ◽  
Novel Coronavirus

ABSTRACTBACKGROUND AND OBJECTIVEThe novel coronavirus (SARS-Cov-2) infected coronavirus disease 2019 (COVID-19) was broken out in Wuhan and Hubei province for more than a month. It severely threats people’s health of thousands in Chin and even other countries. In order to prevent its wide spread, it is necessary to understand the development of the epidemic with precise mathematical language.METHODSThe various data of novel coronavirus pneumonia were collected from the official websites of the National Health Committee of the People’s Republic of China. According to epidemic and administrative division, three groups were divided to analyze the data, Hubei Province (including Wuhan), nationwide without Hubei and Henan Province. With classic SIR models, the fitting epidemiological curves of incidence have made, and basic reproduction number (R0) was also calculated as well. Therefore the disease’s infection intensity, peak time and the epidemiological end time can be deduced.RESULTS(1) Wuhan was the origin place of the epidemic, then it spread to Hubei province quickly. The patients in Hubei had increased rapidly with exponential rise. According to data in Hubei province, the fitting parabolas were made, and some with 51,673 cases. R0 curve shows with S-curve, at early breakout, R0 was as high as 6.27, then it decrease gradually. It is expected to approach to zero in early May; (2) In the group of nationwide without Hubei, the patient cases were much lower than Hubei, but its epidemiological fitting curve also shows a parabola as Hubei. The peak will arrive around February 10 with 9,145 cases. At beginning, R0 was as high as 2.44, then it decreases gradually and approach to zero in the end of March. (3) In Henan Province, the incidence stays very low, the parabolic fitting curve is similar to the nationwide without Hubei. The epidemic is expected to reach the peak on around February 12 and end in early April.CONCLUSIONThe epidemic development in all three groups shows parabolic curves. Their incidences are expected to reach their peaks on February 18 in Hubei, on February 10 in other areas of China. The epidemic will end in early May in Hubei, and in early April in other areas of China. Our study may provide useful knowledge for the government to make prevention and treatment policies.

Current updates on COVID-19 - A review

2020 ◽  
Vol 10 (1) ◽  
pp. 1-8
Author(s):  
Ghotekar D S ◽  
Vishal N Kushare ◽  
Sagar V Ghotekar
Keyword(s):  
World Health Organization ◽  
Common Cold ◽  
Respiratory Diseases ◽  
Gastrointestinal Diseases ◽  
Hubei Province ◽  
World Health ◽  
The World ◽  
The Common ◽  
Novel Coronavirus ◽  
Health Organization

Coronaviruses are a family of viruses that cause illness such as respiratory diseases or gastrointestinal diseases. Respiratory diseases can range from the common cold to more severe diseases. A novel coronavirus outbreak was first documented in Wuhan, Hubei Province, China in December 2019. The World Health Organization (WHO) has declared the coronavirus disease 2019 (COVID-19) a pandemic. A global coordinated effort is needed to stop the further spread of the virus. A novel coronavirus (nCoV) is a new strain that has not been identified in humans previously. Once scientists determine exactly what coronavirus it is, they give it a name (as in the case of COVID-19, the virus causing it is SARS-CoV-2).

Curve Estimation Modeling for Predictions of the Novel Coronavirus (Covid-19) Epidemic in Nigeria

2020 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Christian Ebere Enyoh ◽  
Andrew Wirnkor Verla ◽  
Chidi Edbert Duru ◽  
Emmanuel Chinedu Enyoh ◽  
Budi Setiawan
Keyword(s):  
State Governments ◽  
The Novel ◽  
Curve Estimation ◽  
Exponential Models ◽  
Inverse Models ◽  
Forward Prediction ◽  
S Curve ◽  
Novel Coronavirus ◽  
Best Fit ◽  
Official Data

Based on the official Nigeria Centre for Disease Control (NCDC) data, the current research paper modeled the confirmed cases of the novel coronavirus disease 2019 (COVID-19) in Nigeria. Ten different curve regression models including linear, logarithmic, inverse, quadratic, cubic, compound, power, S-curve, growth, and exponential were used to fit the obtained official data. The cubic (R2 = 0.999) model gave the best fit for the entire country. However, the growth and exponential had the lowest standard error of estimate (0.958) and thus may best be used. The equations for these models were e0.78897+0.0944x and 2.2011e0.0944x respectively. In terms of confirmed cases in individual State, quadratic, cubic, compound, growth, power and exponential models generally best describe the official data for many states except for the state of Kogi which is best fitted with S-curve and inverse models.  The error between the model and the official data curve is quite small especially for compound, power, growth and exponential models. The computed models will help to realized forward prediction and backward inference of the epidemic situation in Nigeria, and the relevant analysis help Federal and State governments to make vital decisions on how to manage the lockdown in the country.

The Risk of Death in 2019 Novel Coronavirus Disease (COVID-19) in Hubei Province

2020 ◽  
Author(s):  
Vincent Yi Fong Su ◽  
Yao-Hsu Yang ◽  
Kuang-Yao Yang ◽  
Kun-Ta Chou ◽  
Wei-Juin Su ◽  
...  
Keyword(s):  
Hubei Province ◽  
Risk Of Death ◽  
Novel Coronavirus

scholarly journals Evolving epidemiology of novel coronavirus diseases 2019 and possible interruption of local transmission outside Hubei Province in China: a descriptive and modeling study

2020 ◽  
Author(s):  
Juanjuan Zhang ◽  
Maria Litvinova ◽  
Wei Wang ◽  
Yan Wang ◽  
Xiaowei Deng ◽  
...  
Keyword(s):  
Age Groups ◽  
Mainland China ◽  
The Elderly ◽  
Hubei Province ◽  
Epidemic Threshold ◽  
Epidemic Dynamics ◽  
Serial Interval ◽  
The Mean ◽  
Novel Coronavirus ◽  
Local Transmission

AbstractBackgroundThe COVID-19 epidemic originated in Wuhan City of Hubei Province in December 2019 and has spread throughout China. Understanding the fast evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy.MethodsWe collected individual information on 8,579 laboratory-confirmed cases from official publically sources reported outside Hubei in mainland China, as of February 17, 2020. We estimated the temporal variation of the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level.ResultsThe median age of the cases was 44 years, with an increasing of cases in younger age groups and the elderly as the epidemic progressed. The delay from symptom onset to hospital admission decreased from 4.4 days (95%CI: 0.0-14.0) until January 27 to 2.6 days (0.0-9.0) from January 28 to February 17. The mean incubation period was estimated at 5.2 days (1.8-12.4) and the mean serial interval at 5.1 days (1.3-11.6). The epidemic dynamics in provinces outside Hubei was highly variable, but consistently included a mix of case importations and local transmission. We estimate that the epidemic was self-sustained for less than three weeks with Rt reaching peaks between 1.40 (1.04-1.85) in Shenzhen City of Guangdong Province and 2.17 (1.69-2.76) in Shandong Province. In all the analyzed locations (n=10) Rt was estimated to be below the epidemic threshold since the end of January.ConclusionOur findings suggest that the strict containment measures and movement restrictions in place may contribute to the interruption of local COVID-19 transmission outside Hubei Province. The shorter serial interval estimated here implies that transmissibility is not as high as initial estimates suggested.

scholarly journals COVID-19 in a Designated Infectious Diseases HospitalOutside Hubei Province,China

2020 ◽  
Author(s):  
Qingxian Cai ◽  
Deliang Huang ◽  
Pengcheng Ou ◽  
Hong Yu ◽  
Zhibin Zhu ◽  
...  
Keyword(s):  
Hubei Province ◽  
Initial Symptom ◽  
Wuhan City ◽  
Hospital System ◽  
Risk Of Progression ◽  
New Type ◽  
Coronavirus Infection ◽  
Novel Coronavirus ◽  
Underlying Diseases ◽  
Overall Mortality

AbstractBackgroundA new type of novel coronavirus infection (COVID-19) occurred in Wuhan, Hubei Province. Previous investigations reported patients in Wuhan city often progressed into severe or critical and had a high mortality rate.The clinical characteristics of affected patients outside the epicenter of Hubei province are less well understood.MethodsAll confirmed COVID-19 case treated in the Third People’s Hospital of Shenzhen,from January 11, 2020 to February 6, 2020, were included in this study. We analyzed the epidemiological and clinical features of these cases to better inform patient management in normal hospital settings.ResultsAmong the 298 confirmed cases, 233(81.5%) had been to Hubei while 42(14%) had not clear epidemiological history. Only 192(64%) cases presented with fever as initial symptom. The lymphocyte count decreased in 38% patients after admission. The number (percent) of cases classified as non-severe and severe was 240(80.6%) and 58(19.4%) respectively. Thirty-two patients (10.7%) needed ICU care. Compared to the non-severe cases, severe cases were associated with older age, underlying diseases, as well as higher levels of CRP, IL-6 and ESR. The median (IRQ) duration of positive viral test were 14(10-19). Slower clearance of virus was associated with higher risk of progression to severe clinical condition. As of February 14, 2020, 66(22.1%) patients were discharged and the overall mortality rate remains 0.ConclusionsIn a designated hospital outside the Hubei Province, COVID-19 patients were mainly characterized by mild symptoms and could be effectively manage by properly using the existing hospital system.

scholarly journals COVID-19 Pandemic: A Global Disaster

2021 ◽  
Vol 1 (1) ◽  
pp. 7-8
Author(s):  
Solomon Arigwe Joseph ◽  
Abuhuraira Ado Musa ◽  
Faisal Muhammad ◽  
Tijjani Muhammad Ahmad
Keyword(s):  
Public Health ◽  
World Health Organization ◽  
Signs And Symptoms ◽  
Viral Disease ◽  
Hubei Province ◽  
Public Health Emergency ◽  
World Health ◽  
Novel Coronavirus ◽  
Health Organization

People began to become ill in late December 2019 in Wuhan, Hubei Province, China, and the illness was revealed to be a kind of pneumonia with unusual signs and symptoms. It was eventually discovered as a novel coronavirus, a virus that causes widespread sickness in animals and birds. World Health Organization (WHO) named this new viral disease coronavirus disease 2019 (COVID-19) and declared a Public Health Emergency of International Concern in January 2020.

Coronavirus Disease 2019 (COVID-19)

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Wasil Khan
Keyword(s):  
Hubei Province ◽  
Animal Pathogens ◽  
Novel Coronavirus

Coronaviruses are important human and animal pathogens. At the end of 2019, a novel coronavirus was identified as the cause of a cluster of pneumonia cases in Wuhan, a city in the Hubei Province of China.

S-curve Model of Relationship Between Energy Consumption and Economic Development

2014 ◽  
Vol 24 (1) ◽  
pp. 53-64 ◽  
Author(s):  
Anjian Wang ◽  
Gaoshang Wang ◽  
Qishen Chen ◽  
Wenjia Yu ◽  
Kun Yan ◽  
...  
Keyword(s):  
Economic Development ◽  
Energy Consumption ◽  
Curve Model ◽  
S Curve
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