Comparison of some forecasting methods for COVID-19

Appadu, Appanah Rao; Kelil, Abey Sherif; Tijani, Yusuf O.

doi:10.1016/j.aej.2020.11.011

Cited by 22 publications

(20 citation statements)

References 22 publications

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“…In Appadu et al. ( 2021 ), an iterative method based on Euler’s method and cubic spline interpolation is studied to forecast values from June 01, 2020, using the data from February 15, 2020, to May 31, 2020. In Nesteruk ( 2020a ), to predict the infected cases on February 10, 2020, in Mainland China, a mathematical model is used.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

Selvakumar

Lokesh

2021

Soft Comput

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The World Health Organization (WHO) on December 31, 2019, was informed of several cases of respiratory diseases of unknown origin in the city of Wuhan in the Chinese Province of Hubei, the clinical manifestations of which were similar to those of viral pneumonia and manifested as fever, cough, and shortness of breath. And, the disease caused by the virus is named the new coronavirus disease 2019 and it will be abbreviated as 2019-nCoV and COVID-19. As of January 30, 2020, the WHO classified this epidemic as a global health emergency (Chung et al. in Radiology 295(1):202–207, 2020). It is an international real-life problem. Due to deaths, globally everyone is under fear. Now, it is the responsibility of researchers to give hope to the people. In this article, we aim to better protect people and general pandemic preparedness by predicting the lifetime of the disease-causing virus using three mathematical models. This article deals with a complex real-life problem people face all over the world, an international real-life problem. The main focus is on the USA due to large infection and death due to coronavirus and thereby the life of every individual is uncertain. The death counts of the USA from February 29 to April 22, 2020, are used in this article as a data set. The death counts of the USA are fitted by the solutions of three mathematical models and a solution to an international problem is achieved. Based on the death rate, the lifetime of the coronavirus COVID-19 is predicted as 1464.76 days from February 29, 2020. That is, after March 2024 there will be no death in the USA due to COVID-19 if everyone follows the guidelines of WHO and the advice of healthcare workers. People and government can get prepared for this situation and many lives can be saved. It is the contribution of soft computing. Finally, this article suggests several steps to control the spread and severity of the disease. The research work, the lifetime prediction presented in this article is entirely new and differs from all other articles in the literature.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

Selvakumar

Lokesh

2021

Soft Comput

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“…In the absence of a vaccine, the effects of taxation and the risk of easing social distancing are discussed. In [8] an iterative method based on the Euler method and cubic spline interpolation is investigated to predict values from the begining of June 01, 2020, using data from February 15, 2020, to May 31, 2020. In [56] to predict cases infected in mainland China on February 10, 2020, a mathematical model is used.…”

Section: Literature Review Of Mathematical Modelsmentioning

confidence: 99%

“…2. By taking r = p in the model function (8) and adding a parameter m, we get a model with seven parameters and consider it as Model-B.…”

Section: Parameter-dependent Mathematical Model For the New Covid-19 Virusmentioning

confidence: 99%

The Prediction of the Lifetime of the COVID-19 Virus using Parameter-dependent New Mathematical Models

2021

Preprint

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This article is about a complex real-world human medical problem that people all over the world face, a major international public Health problem due to the new coronavirus disease 2019(COVID-19), a highly communicable infectious disease between humans. Spreads rapidly among humans of both sexes of all ages, in large masses in the cyclical manner(seasonally) causing disease in susceptible human Hosts affecting most of the organs in humans mainly lungs resulting in Severe Acute Respiratory Syndrome resulting in mass acute deaths. Acute deaths are more common with Comorbidities like Diabetes mellitus, Ischaemic heart disease, Liver disease, Kidney disease, Gut, etc. Now it is the major emergency international pandemic public health medical disease. On the face of the earth, there are large masses of infection and mass acute deaths due to COVID-19 virus infection and so the life of every individual is uncertain at any time. Because of the mass acute deaths from the COVID-19 virus infection, everyone in the world is scared. From now on, it is the responsibility of the researchers of all nations to bring hope to people. In this article, by predicting the lifetime of disease-causing virus, hope to the people is given, to better protect all people and speed up the immediate general pandemic preparedness within the lifespan of the virus. To accelerate actions to save people's lives, mathematical models will help make public health decisions and reduce mortality using the resources available during this time of the COVID-19 pandemic. In this article, to better protect people from disease preparedness for the virus and a general pandemic by predicting the lifetime of the disease-causing coronavirus, three new mathematical models which are dependent on parameters are proposed. The parameters in the model function model uncertainty of death due to the present international real-life problem caused by different strains of the COVID-19 virus. The first model is a model with six parameters and the second and third models are models with seven parameters respectively. These three models are the generalization of the three models of Phem . The errors due to the models of this article are minimized from the errors due to the models of Phem. These three models can predict the acute death count outside the data period and can predict the lifetime. To illustrate the applicability of the models a big data set of size 54 days starting from February 29, 2020, to April 22, 2020, of acute death counts of USA( United States of America) is considered. The main focus is on the USA due to the significant large mass of infection and large mass of acute death from the COVID-19 virus. As a result, everyone's life is uncertain about death at any time. Since it is a major international public health-related medical problem in humans, with an accuracy of 95% of confidence the results using three models are erected. The large mass of acute deaths due to the number of COVID-19 virus infections in the USA are fitted by the model functions of three mathematical models and a solution is found to an international problem. Based on the acute death rate, the lifetime of the COVID-19 virus is estimated to be 1484.76198616309920 days from the first day of acute death, February 29, 2020. In other words, there will be no mass acute deaths from the COVID-19 virus in the USA after April 2024 if the nation follows the guidelines of the WHO(World Health Organization) and the recommendations of the pathogen. And when the people and the government are very well prepared for this crisis then the spread of infection can be prevented, the people and government can be saved from the economic crisis, and many lives can be saved from mass acute deaths. A comparative study of all models is presented for different measures of errors. The acute death count of the USA outside the date of the data set of 54 days is predicted using three models. The data set misses some counts during the collection of data and it is identified. From the ratio of standard deviation and average acute deaths, it is predicted that the total acute death counts during 54 days will be 62,969. Using the standard deviation around the line of regression it is shown that in the data set a large count is missing during the collection of data of USA. Using the coefficient of determination it is predicted that the Model-C, provides 100% of fitness with the given data set and only 0.0% variation. All three models are suitable to fit the data set of acute death counts of the USA, but Model-C is the best and optimal among the three models. Tt is predicted from Model-A, Model-B, and Model-C the total acute death counts during 54 days will be 66537, 67085, and 68523 respectively. Since Model-C is the best and optimal model, the predicted total acute death counts during 54 days will be 68523. Finally, this article suggests various steps to help control the spread and severity of the new disease. The prediction of the lifetime and data count missing in the data set presented in this research article is entirely new and differs totally from all other articles in the literature. To accelerate actions to save people's lives, mathematical models will help make public health decisions and reduce mortality using the resources available during this time of the COVID-19 pandemic.

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“…Based on WHO statistics, since its detection until December 25, 2020, the total number of confirmed cases worldwide is 80,133,093 and the number of deaths is 1,755,653. The considerable and continuous rise in the daily infected cases number all over the world is worrying, and many researchers are currently developing various mathematical and machine learning models to predict the future progress of this pandemic [1] , [2] , [3] , [4] , [5] , [6] , [7] . However, only few studies have discussed the possibility of the second surge of this SARS pandemic.…”

Section: Introductionmentioning

confidence: 99%

A multi-stage SEIR model to predict the potential of a new COVID-19 wave in KSA after lifting all travel restrictions

Khedher¹,

Kolsi²,

Alsaif³

2021

Alexandria Engineering Journal

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The complete lifting of travel restrictions to KSA takes place after 3rd of January 2021.There are fears that KSA will confront a new COVID-19 wave, especially when the most of countries that resumed the international flights are suffering now from the second surge. Fortunately, more than one Covid-19 Vaccine have been rolled out. However, herd immunity could be reached only through widespread vaccination. COVID-19 vaccines need more time to be properly protective, especially in front of people refusing to get vaccinated. A modified multi-stage SEIR model, with distinct reproductive numbers corresponding to before and after lockdown is employed to predict the potential of a new pandemic wave. First, the two-stage model employed to find the best fitting for the reproductive numbers. Then, the model is extended to three-stage one to investigate the relaxation. However, the modified model detects a second wave in early stage from 28th May to 17th June 2020 before even succeeding controlling the first outbreak. Subsequently, the four-stage SEIR model is used to predict the end of the second wave. Moreover, the model is employed to test the potential of a new pandemic surge after the international flights are resumed.

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Comparison of some forecasting methods for COVID-19

Cited by 22 publications

References 22 publications

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

RETRACTED ARTICLE: The prediction of the lifetime of the new coronavirus in the USA using mathematical models

The Prediction of the Lifetime of the COVID-19 Virus using Parameter-dependent New Mathematical Models

A multi-stage SEIR model to predict the potential of a new COVID-19 wave in KSA after lifting all travel restrictions

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