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 Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

2021 ◽  
Author(s):  
Mohamed LOUNIS ◽  
Babu Malavika
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Polynomial Regression ◽  
Logistic Growth ◽  
The Novel ◽  
Logistic Growth Model ◽  
Polynomial Regression Models ◽  
Using Data ◽  
Novel Coronavirus ◽  
Polynomial Regression Model

Abstract The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

Coronavirus: Towards controlling of the pandemic - Indian scenario

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 462-468
Author(s):  
Latika kothari ◽  
Sanskruti Wadatkar ◽  
Roshni Taori ◽  
Pavan Bajaj ◽  
Diksha Agrawal
Keyword(s):  
Control Measures ◽  
The Novel ◽  
Medical Treatments ◽  
Health Crisis ◽  
The Public ◽  
Infection Control Measures ◽  
The Social ◽  
Asking Questions ◽  
The Government ◽  
Novel Coronavirus

Coronavirus disease 2019 (COVID-19) is a communicable infection caused by the novel coronavirus resulting in severe acute respiratory syndrome coronavirus 2 (SARS-CoV). It was recognized to be a health crisis for the general population of international concern on 30th January 2020 and conceded as a pandemic on 11th March 2020. India is taking various measures to fight this invisible enemy by adopting different strategies and policies. To stop the COVID-19 from spreading, the Home Affairs Ministry and the health ministry, of India, has issued the nCoV 19 guidelines on travel. Screening for COVID-19 by asking questions about any symptoms, recent travel history, and exposure. India has been trying to get testing kits available. The government of India has enforced various laws like the social distancing, Janata curfew, strict lockdowns, screening door to door to control the spread of novel coronavirus. In this pandemic, innovative medical treatments are being explored, and a proper vaccine is being hunted to deal with the situation. Infection control measures are necessary to prevent the virus from further spreading and to help control the current situation. Thus, this review illustrates and explains the criteria provided by the government of India to the awareness of the public to prevent the spread of COVID-19.

scholarly journals Risk Assessment of Novel Coronavirus COVID-19 Outbreaks Outside China

2020 ◽  
Vol 9 (2) ◽  
pp. 571 ◽  
Author(s):  
Péter Boldog ◽  
Tamás Tekeli ◽  
Zsolt Vizi ◽  
Attila Dénes ◽  
Ferenc A. Bartha ◽  
...  
Keyword(s):  
Control Measure ◽  
Mainland China ◽  
Destination Country ◽  
Control Measures ◽  
Cumulative Number ◽  
Travel Restrictions ◽  
Imported Cases ◽  
Major Outbreak ◽  
Novel Coronavirus ◽  
Local Reproduction

We developed a computational tool to assess the risks of novel coronavirus outbreaks outside of China. We estimate the dependence of the risk of a major outbreak in a country from imported cases on key parameters such as: (i) the evolution of the cumulative number of cases in mainland China outside the closed areas; (ii) the connectivity of the destination country with China, including baseline travel frequencies, the effect of travel restrictions, and the efficacy of entry screening at destination; and (iii) the efficacy of control measures in the destination country (expressed by the local reproduction number R loc ). We found that in countries with low connectivity to China but with relatively high R loc , the most beneficial control measure to reduce the risk of outbreaks is a further reduction in their importation number either by entry screening or travel restrictions. Countries with high connectivity but low R loc benefit the most from policies that further reduce R loc . Countries in the middle should consider a combination of such policies. Risk assessments were illustrated for selected groups of countries from America, Asia, and Europe. We investigated how their risks depend on those parameters, and how the risk is increasing in time as the number of cases in China is growing.

scholarly journals Stepping up infection control measures in ophthalmology during the novel coronavirus outbreak: an experience from Hong Kong

2020 ◽  
Vol 258 (5) ◽  
pp. 1049-1055 ◽  
Author(s):  
Tracy H. T. Lai ◽  
Emily W. H. Tang ◽  
Sandy K. Y. Chau ◽  
Kitty S. C. Fung ◽  
Kenneth K. W. Li
Keyword(s):  
Hong Kong ◽  
Infection Control ◽  
Control Measures ◽  
The Novel ◽  
Infection Control Measures ◽  
Novel Coronavirus

scholarly journals Modeling the Novel Coronavirus (SARS-CoV-2) Outbreak in Sicily, Italy

2020 ◽  
Vol 17 (14) ◽  
pp. 4964 ◽  
Author(s):  
Andrea Maugeri ◽  
Martina Barchitta ◽  
Sebastiano Battiato ◽  
Antonella Agodi
Keyword(s):  
Transmission Rate ◽  
Control Measures ◽  
Transmission Dynamics ◽  
The Novel ◽  
Epidemic Spread ◽  
Transmission Rates ◽  
Contact Precautions ◽  
Travel Restrictions ◽  
Novel Coronavirus ◽  
Reported Data

Italy was the first country in Europe which imposed control measures of travel restrictions, quarantine and contact precautions to tackle the epidemic spread of the novel coronavirus (SARS-CoV-2) in all its regions. While such efforts are still ongoing, uncertainties regarding SARS-CoV-2 transmissibility and ascertainment of cases make it difficult to evaluate the effectiveness of restrictions. Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to assess SARS-CoV-2 transmission dynamics, working on the number of reported patients in intensive care unit (ICU) and deaths in Sicily (Italy), from 24 February to 13 April. Overall, we obtained a good fit between estimated and reported data, with a fraction of unreported SARS-CoV-2 cases (18.4%; 95%CI = 0–34.0%) before 10 March lockdown. Interestingly, we estimated that transmission rate in the community was reduced by 32% (95%CI = 23–42%) after the first set of restrictions, and by 80% (95%CI = 70–89%) after those adopted on 23 March. Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Moreover, our findings suggested that transmission rates were reduced after the adoption of control measures. However, we cannot evaluate whether part of this reduction might be attributable to other unmeasured factors, and hence further research and more accurate data are needed to understand the extent to which restrictions contributed to the epidemic control.

scholarly journals COMPARATIVE ANALYSIS OF COVID-19 EPIDEMIC IN HUBEI PROVINCE AND REST PARTS OF MAINLAND CHINA

2020 ◽  
pp. 1-3
Author(s):  
Youfu Ke ◽  
Zemin Chen ◽  
Bo Peng ◽  
Hoyan Wong ◽  
Yunkeung Wong ◽  
...  
Keyword(s):  
Growth Rates ◽  
Mainland China ◽  
Diagnosis And Treatment ◽  
The Novel ◽  
Fatality Rate ◽  
Treatment Standards ◽  
Daily Observation ◽  
Novel Coronavirus ◽  
Pneumonia Diagnosis ◽  
Close Contacts

Background: China 's national-level anti-COVID-19 campaign has been going on for a month. With the development of the epidemic, it is found that COVID-19 severity in Hubei province (Hubei) is different from the rest of mainland China (Rest of China). It is necessary to compare the two areas, summarize experiences and lessons, analyze the epidemic trend and further point out the direction for the campaign. Methods: Prevent, quarantine and treat the disease according to The Novel Coronavirus Infected Pneumonia Diagnosis and Treatment Standards. Collect the numbers of total close contacts, daily observation cases, daily suspected cases, total conrmed cases, daily severe cases, total deaths from January 20 to February 19, input them into SPSS 25 and Microsoft ofce 2019 excel for data processing, statistical analysis and drawing. Findings: Total conrmed cases in Hubei account for 83.2% of the country. Daily suspected cases growth rates for both areas have become negative since February 9. Daily observation cases in Rest of China reached highest point on February 5 as opposed to February 13 in Hubei, and total close contacts growth rates for the last three days are declining steadily to 1.9% and 3.8% respectively. Total conrmed cases growth rate has hit the lowest levels in Rest of China at 0.34% by comparison with 0.57% in Hubei. Mean fatality rate and mean percentage of severe cases for the last three days in Rest of China are 0.67% and 5.83% in contrast to 3.12% and 18.2% in Hubei. There have been very signicant differences in fatality rate and percentage of severe cases existing in the two areas since January 23 and 24 respectively (P<0.01). Interpretation: Hubei is the main epidemic area. COVID-19 has low fatality rate and high transmissibility. Cutting off the source of infection is pivotal in containing COVID-19 outbreak and has a guiding effect on prevention and control of pandemic worldwide. The Novel Coronavirus Infected Pneumonia Diagnosis and Treatment Standards has played an important role in helping medical staff across the country to ght the epidemic. Coordinating national medical resources to support disaster areas, making full use of the existing facilities to isolate and quarantine, providing timely and accurate treatment can reduce fatality rate. Further efforts are needed to develop highly effective Chinese medicines, western drugs and vaccines in order to eradicate the virus or prevent the epidemic from continuing.

scholarly journals A data-driven assessment of early travel restrictions related to the spreading of the novel COVID-19 within mainland China

2020 ◽  
Author(s):  
Alberto Aleta ◽  
Qitong Hu ◽  
Jiachen Ye ◽  
Peng Ji ◽  
Yamir Moreno
Keyword(s):  
Mainland China ◽  
Behavioral Changes ◽  
The Novel ◽  
Metapopulation Model ◽  
Short Term ◽  
Effective Measure ◽  
Mobility Data ◽  
Health Authorities ◽  
Travel Restrictions ◽  
Novel Coronavirus

Two months after it was firstly reported, the novel coronavirus disease COVID-19 has already spread worldwide. However, the vast majority of reported infections have occurred in China. To assess the effect of early travel restrictions adopted by the health authorities in China, we have implemented an epidemic metapopulation model that is fed with mobility data corresponding to 2019 and 2020. This allows to compare two radically different scenarios, one with no travel restrictions and another in which mobility is reduced by a travel ban. Our findings indicate that i) travel restrictions are an effective measure in the short term, however, ii) they are ineffective when it comes to completely eliminate the disease. The latter is due to the impossibility of removing the risk of seeding the disease to other regions. Our study also highlights the importance of developing more realistic models of behavioral changes when a disease outbreak is unfolding.

scholarly journals Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling

2021 ◽  
Author(s):  
Miguel López ◽  
Alberto Peinado ◽  
Andrés Ortiz
Keyword(s):  
Growth Model ◽  
Logistic Growth ◽  
Data Series ◽  
The Novel ◽  
Short Term ◽  
Growth Trend ◽  
Global Pandemic ◽  
First Case ◽  
Logistic Growth Model ◽  
Novel Coronavirus

AbstractSince the first case reported of SARS-CoV-2 the end of December 2019 in China, the number of cases quickly climbed following an exponential growth trend, demonstrating that a global pandemic is possible. As of December 3, 2020, the total number of cases reported are around 65,527,000 contagions worldwide, and 1,524,000 deaths affecting 218 countries and territories. In this scenario, Spain is one of the countries that has suffered in a hard way, the ongoing epidemic caused by the novel coronavirus SARS-CoV-2, namely COVID-19 disease. In this paper, we present the utilization of phenomenological epidemic models to characterize the two first outbreak waves of COVID-19 in Spain. The study is driven using a two-step phenomenological epidemic approach. First, we use a simple generalized growth model to fit the main parameters at the early epidemic phase; later, we apply our previous finding over a logistic growth model to that characterize both waves completely. The results show that even in the absence of accurate data series, it is possible to characterize the curves of case incidence, and even construct short-term forecast in the near time horizon.

scholarly journals Being aware of the neurological manifestations of COVID-19 infection

2021 ◽  
Vol 40 (3) ◽  
pp. 187-189
Author(s):  
Basak Atalay
Keyword(s):  
Respiratory Tract ◽  
Sore Throat ◽  
Strong Evidence ◽  
Initial Presentation ◽  
The Novel ◽  
Neurologic Manifestations ◽  
Neurological Manifestations ◽  
Impaired Consciousness ◽  
Atypical Symptoms ◽  
Novel Coronavirus

The novel coronavirus disease- 2019 (COVID-19) is first reported from China, and unfortunately, no longer restricted, and spreads in all geographies.(1) The main symptoms of COVID-19 infection are respiratory tract related. Infected patients usually have symptoms such as fever, sore throat, and fatigue, and they may have atypical symptoms like headache, seizure, anosmia, stroke, and even impaired consciousness. Strong evidence of an association between COVID-19 infection and neurological manifestations has been reported. Besides, the neurologic manifestations may be the initial presentation of COVID-19 infection.

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