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 Characterizing two outbreak waves of COVID-19 in Spain using phenomenological epidemic modelling

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253004
Author(s):  
Miguel López ◽  
Alberto Peinado ◽  
Andrés Ortiz
Keyword(s):  
Growth Model ◽  
Total Duration ◽  
Logistic Growth ◽  
Data Series ◽  
The Novel ◽  
Growth Trend ◽  
Global Pandemic ◽  
First Case ◽  
Logistic Growth Model ◽  
Novel Coronavirus

Since 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 construct a short-term forecast of 60 days in the near time horizon, in relation to the expected total duration of the pandemic.

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.

scholarly journals Outbreak analysis with a logistic growth model shows COVID-19 suppression dynamics in China

2020 ◽  
Author(s):  
Yi Zou ◽  
Stephen Pan ◽  
Peng Zhao ◽  
Lei Han ◽  
Xiaoxiang Wang ◽  
...  
Keyword(s):  
Growth Model ◽  
Mainland China ◽  
Logistic Growth ◽  
Complete Suppression ◽  
Virus Growth ◽  
Rate Of Increase ◽  
Decline Curves ◽  
Major Outbreak ◽  
Logistic Growth Model ◽  
Novel Coronavirus

AbstractChina reported a major outbreak of a novel coronavirus, SARS-CoV2, from mid-January till mid-March 2020. The number of cases outside China is now growing fast, while in mainland China the virus outbreak is largely under control. We review the epidemic virus growth and decline curves in China using a phenomenological logistic growth model to summarize the outbreak dynamics using three parameters that characterize the epidemic’s timing, rate and peak. During the initial phase, the number of virus cases doubled every 2.7 (range 2.2 - 4.4) days. The rate of increase in the number of reported cases peaked approximately 10 days after suppression measures were started on 23-25 January 2020. The peak in the number of reported sick cases occurred on average 18 days after the start of measures. From the time of starting measures till the peak, the number of cases increased by a factor 39 in the province Hubei, and by a factor 9.5 for all of China (range: 6.2-20.4 in the other provinces). Complete suppression took up to 2 months (range: 23-57d.), during which period severe restrictions, social distancing measures, testing and isolation of cases were in place. The suppression of the disease in China has been successful, demonstrating that suppression is a viable strategy to contain SARS-CoV2.

scholarly journals Reconstructing and Forecasting the COVID-19 Epidemic in the US Using a 5-Parameter Logistic Growth Model

2020 ◽  
Author(s):  
Ding-Geng Chen ◽  
Xinguang Chen ◽  
Jenny Ke Chen
Keyword(s):  
Growth Rate ◽  
Growth Model ◽  
Early Period ◽  
Logistic Growth ◽  
Daily Growth ◽  
First Case ◽  
The Us ◽  
Logistic Growth Model ◽  
Using Data ◽  
Public Policy Decisions

Abstract Background: Many studies have modeled and predicted the epidemic of COVID-19 in the US using data that starts from the first reported cases. However, because of the shortage of test services to detect the infected, this approach is subject to error due to under-detection in the early period of the epidemic. We attempted a new approach to overcome this limitation and to provide data supporting the public policy decisions against the life-threatening COVID-19 epidemic.Methods: Documented data by CDC were used, including daily new and cumulative cases of confirmed COVID-19 in the US from January 22 to April 6, 2020. A 5-parameter logistic growth model was used to reconstruct the epidemic. Instead of all data in the whole study period, we fitted data in a 2-week window from March 21 to April 4 (approximately one incubation period) during which massive testing services were in position. With parameters obtained from the modeling, we reconstructed and predicted the epidemic and evaluated the under-detection.Results: The data fit the model satisfactorily. The estimated daily growth rate was 16.8% (95% CI: 15.95%, 17.76%) overall, with 4 consecutive days having a doubling growth rate. Based on the modeling result, the tipping point for new cases to decline will be on April 7 th , 2020, with 32,860 new cases. By the end of the epidemic, a total of 792,548 (95% CI: 789,162-795,934) will be infected. Based on the model, a total of 12,029 cases were not detected from the first case from January 22 to April 4.Conclusions: Study findings suggest the usage of a 5-parameter logistic growth model with reliable data that comes from a specified window period, where governmental interventions are appropriately implemented. In addition to informing decision-making, this model adds one tool for use to capture the underlying COVID-19 epidemic caused by a novel pathogen.

scholarly journals Generalized logistic growth modeling of the COVID-19 outbreak: comparing the dynamics in the 29 provinces in China and in the rest of the world

2020 ◽  
Vol 101 (3) ◽  
pp. 1561-1581 ◽  
Author(s):  
Ke Wu ◽  
Didier Darcet ◽  
Qian Wang ◽  
Didier Sornette
Keyword(s):  
Growth Model ◽  
Western Europe ◽  
Control Measures ◽  
Logistic Growth ◽  
Global Pandemic ◽  
The World ◽  
Containment Measures ◽  
Logistic Growth Model ◽  
The Impact ◽  
Scenario Projections

Abstract Started in Wuhan, China, the COVID-19 has been spreading all over the world. We calibrate the logistic growth model, the generalized logistic growth model, the generalized Richards model and the generalized growth model to the reported number of infected cases for the whole of China, 29 provinces in China, and 33 countries and regions that have been or are undergoing major outbreaks. We dissect the development of the epidemics in China and the impact of the drastic control measures both at the aggregate level and within each province. We quantitatively document four phases of the outbreak in China with a detailed analysis on the heterogeneous situations across provinces. The extreme containment measures implemented by China were very effective with some instructive variations across provinces. Borrowing from the experience of China, we made scenario projections on the development of the outbreak in other countries. We identified that outbreaks in 14 countries (mostly in western Europe) have ended, while resurgences of cases have been identified in several among them. The modeling results clearly show longer after-peak trajectories in western countries, in contrast to most provinces in China where the after-peak trajectory is characterized by a much faster decay. We identified three groups of countries in different level of outbreak progress, and provide informative implications for the current global pandemic.

scholarly journals The Greek Response to COVID-19: A True Success Story from an IBD Perspective

2020 ◽  
Vol 26 (8) ◽  
pp. 1144-1148 ◽  
Author(s):  
Giorgos Bamias ◽  
Styliani Lagou ◽  
Michalis Gizis ◽  
George Karampekos ◽  
Konstantinos G Kyriakoulis ◽  
...  
Keyword(s):  
Home Delivery ◽  
The Novel ◽  
National Health Care ◽  
Global Pandemic ◽  
First Case ◽  
Strict Compliance ◽  
Inflammatory Bowel ◽  
Primary Focus ◽  
Novel Coronavirus ◽  
Of Greece

Abstract Background After the first case of infection with the novel coronavirus, SARS-CoV-2, in China, an outbreak rapidly spread, finally evolving into a global pandemic. The new disease was named coronavirus disease 2019 (COVID-19) and by May 10, 2020, it has affected more than 4 million people worldwide and caused more than 270,000 deaths. Methods We describe the Greek experience regarding the response to COVID-19, with particular focus on 2 COVID-19 reference hospitals in the metropolitan area of Athens, the capital of Greece. Results The first case of SARS-CoV-2 infection in Greece was reported on February 26, 2020, and prompted a decisive response from the Greek government. The primary focus was containment of virus spread, considering shortage of ICU beds. A general lockdown was implemented early on, and the national Health Care System underwent massive re-structuring. Our 2 gastrointestinal (GI) centers, which provide care for more than 1500 inflammatory bowel disease (IBD) patients, are located in hospitals that were transformed to COVID-19 reference centers. To maintain sufficient care for our patients, while also contributing to the fight against COVID-19, we undertook specific measures. These included provision of telemedicine services, electronic prescriptions and home delivery of medications, isolation of infusion units and IBD clinics in COVID-free zones of the hospitals, in addition to limiting endoscopies to emergencies only. Such practices allowed us to avoid interruption of appropriate therapies for IBD patients. In fact, within the SECURE-IBD database, there have been only 4 Greek IBD patients, to date, who have been reported as positive for SARS-CoV-2. Conclusion Timely application of preventive measures and strict compliance to guidelines limited the spread of COVID-19 in Greece and minimally impacted our IBD community, without interfering with therapeutic management.

scholarly journals A Novel 2-Stage Fractional Runge–Kutta Method for a Time-Fractional Logistic Growth Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Sarmad Arshad ◽  
Dumitru Baleanu ◽  
Muhammad Bilal Riaz ◽  
Muhammad Abbas
Keyword(s):  
Growth Model ◽  
Euler Method ◽  
Logistic Growth ◽  
The Novel ◽  
Kutta Method ◽  
Runge Kutta Method ◽  
Novel Method ◽  
Fractional Differential ◽  
Logistic Growth Model ◽  
Runge Kutta

In this paper, the fractional Euler method has been studied, and the derivation of the novel 2-stage fractional Runge–Kutta (FRK) method has been presented. The proposed fractional numerical method has been implemented to find the solution of fractional differential equations. The proposed novel method will be helpful to derive the higher-order family of fractional Runge–Kutta methods. The nonlinear fractional Logistic Growth Model is solved and analyzed. The numerical results and graphs of the examples demonstrate the effectiveness of the method.

scholarly journals Discrete SIR modelling using empirical infection data shows that SARS-CoV-2 infection provides short-term immunity

2020 ◽  
Author(s):  
Andrew McMahon ◽  
Nicole C. Robb
Keyword(s):  
Transmission Dynamics ◽  
Economic Damage ◽  
The Novel ◽  
Anecdotal Evidence ◽  
Serological Testing ◽  
Short Term ◽  
Testing Strategies ◽  
Global Pandemic ◽  
Subsequent Exposure ◽  
Novel Coronavirus

Background: The novel coronavirus SARS-CoV-2, which causes the COVID-19 disease, has resulted in a global pandemic. Since its emergence in December 2019, the virus has infected millions of people, caused the deaths of hundreds of thousands and resulted in incalculable social and economic damage. Understanding the infectivity and transmission dynamics of the virus is essential for understanding how best to reduce mortality whilst ensuring minimal social restrictions to the lives of the general population. Anecdotal evidence is available, but detailed studies have not yet revealed whether infection with the virus results in immunity. Objective: The objective of the study was to use mathematical modelling to investigate the reinfection frequency of COVID-19. Methods: We have used the SIR (Susceptible, Infected, Recovered) framework and random processing based on empirical SARS-CoV-2 infection and fatality data from different regions to calculate the number of reinfections that would be expected to occur if no immunity to the disease occurred. Results: Our model predicts that cases of reinfection should have been observed by now if primary SARS-CoV-2 infection did not protect from subsequent exposure in the short term, however, no such cases have been documented. Conclusions: This work concludes that infection with the SARS-CoV-2 virus provides short-term immunity to reinfection and therefore provides a useful insight for serological testing strategies, lockdown easing and vaccine design.

scholarly journals Forecasting Novel Corona Positive Cases in India using Truncated Information: A Mathematical Approach

2020 ◽  
Author(s):  
Brijesh P. Singh
Keyword(s):  
United States ◽  
Growth Model ◽  
United States Of America ◽  
Initial Phase ◽  
The United States ◽  
The Other ◽  
Logistic Growth ◽  
Mathematical Approach ◽  
First Case ◽  
Logistic Growth Model

AbstractNovel corona virus is declared as pandemic and India is struggling to control this from a massive attack of death and destruction, similar to the other countries like China, Europe, and the United States of America. India reported 2545 cases novel corona confirmed cases as of April 2, 2020 and out of which 191 cases were reported recovered and 72 deaths occurred. The first case of novel corona is reported in India on January 30, 2020. The growth in the initial phase is following exponential. In this study an attempt has been made to model the spread of novel corona infection. For this purpose logistic growth model with minor modification is used and the model is applied on truncated information on novel corona confirmed cases in India. The result is very exiting that till date predicted number of confirmed corona positive cases is very close to observed on. The time of point of inflexion is found in the end of the April, 2020 means after that the increasing growth will start decline and there will be no new case in India by the end of July, 2020.

scholarly journals Modeling and Simulation: A study on predicting the outbreak of COVID-19 in Saudi Arabia

2021 ◽  
Author(s):  
Ahmed Msmali ◽  
Zico Mutum ◽  
Idir Mechai ◽  
Abdullah Ahmadini
Keyword(s):  
Saudi Arabia ◽  
Logistic Growth ◽  
Special Focus ◽  
The Novel ◽  
Final Phase ◽  
The World ◽  
Logistic Growth Model ◽  
Novel Coronavirus ◽  
Epidemiological Trends

AbstractThe novel coronavirus (Covid-19) infection has resulted in an ongoing pandemic affecting health system and economy of more than 200 countries around the world. Mathematical models are used to predict the biological and epidemiological trends of an epidemic and develop methods for controlling it. In this work, we use mathematical model perspective to study the role of behavior change in slowing the spread of the COVID-19 disease in Saudi Arabia. The real-time updated data from 1st May 2020 to 8th January 2021 is collected from Saudi Ministry of Health, aiming to provide dynamic behaviors of the pandemic in Saudi Arabia. During this period, it has infected 297,205 people, resulting in 6124 deaths with the mortality rate 2.06 %. There is weak positive relationship between the spread of the infection and mortality (R2 =0.412). We use Susceptible-Exposed-Infection-Recovered (SEIR) mode, the logistic growth model and with special focus on the exposed, infection and recovery individuals to simulate the final phase of the outbreak. The results indicate that social distancing, good hygienic conditions, and travel limitation are the crucial measures to prevent further spreading of the epidemic.

Sign in / Sign up

Export Citation Format

Share Document