An SIR epidemic model for COVID-19 spread with fuzzy parameter: the case of Indonesia

Abdy, Muhammad; Side, Sumiati; Annas, Suwardi; Nur, Wahyuddin; Sanusi, Wahidah

doi:10.1186/s13662-021-03263-6

Cited by 60 publications

(53 citation statements)

References 32 publications

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“…Many research studies were conducted to provide a transmission dynamic model for COVID-19. In 5 , an SIR (susceptible-infected-recovered) epidemic model for COVID-19 spread in Indonesia with fuzzy parameter was constructed by taking into account the factors of vaccination, treatment, health protocols, and the virus-load. In 6 , a conceptual mathematical model on the transmission dynamics of COVID-19 between the frontliners and the general public was formulated by considering an SEIR (susceptible–exposed–infected–recovered) compartment model where a huge number of parameters is required (up to 23 parameters).…”

Section: Literature Reviewmentioning

confidence: 99%

A particle swarm optimization approach for predicting the number of COVID-19 deaths

Haouari

Mhiri

2021

Sci Rep

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The rapid spread of the COVID-19 pandemic has raised huge concerns about the prospect of a major health disaster that would result in a huge number of deaths. This anxiety was largely fueled by the fact that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the disease, was so far unknown, and therefore an accurate prediction of the number of deaths was particularly difficult. However, this prediction is of the utmost importance for public health authorities to make the most reliable decisions and establish the necessary precautions to protect people’s lives. In this paper, we present an approach for predicting the number of deaths from COVID-19. This approach requires modeling the number of infected cases using a generalized logistic function and using this function for inferring the number of deaths. An estimate of the parameters of the proposed model is obtained using a Particle Swarm Optimization algorithm (PSO) that requires iteratively solving a quadratic programming problem. In addition to the total number of deaths and number of infected cases, the model enables the estimation of the infection fatality rate (IFR). Furthermore, using some mild assumptions, we derive estimates of the number of active cases. The proposed approach was empirically assessed on official data provided by the State of Qatar. The results of our computational study show a good accuracy of the predicted number of deaths.

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Section: Literature Reviewmentioning

confidence: 99%

A particle swarm optimization approach for predicting the number of COVID-19 deaths

Haouari

Mhiri

2021

Sci Rep

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“…The comparison between Iran and Pakistan is done by [20] . A case of the COVID-19 pandemic in Indonesia has been discussed in [21] .…”

Section: Introductionmentioning

confidence: 99%

The current COVID-19 pandemic in China: An overview and corona data analysis

Wang¹,

Ahmad²,

Alanzi³

et al. 2022

Alexandria Engineering Journal

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At the end of December 2019, the Wuhan Municipal Health Commission, revealed several cases of pneumonia of unknown etiology. Later, this etiology was called the coronavirus disease 2019 (COVID-19). COVID-19 disease is rapidly spreading around the globe, affected millions of people, compelling governments to take serious actions. Due to this deadly disease, a number of deaths have been occurred and still increasing exponentially. In the practice and application of big data sciences, it is always of interest to provide the best description of the data. In this present article, the event background, symptoms, and preventions from COVID-19 are discussed. The steps were taken by the Chinese government to control the COVID-19 has also been discussed. Up to date, details, and data of daily discovered cases, total discovered cases, daily deaths, and total deaths around the world are presented. Moreover, a new statistical distribution is introduced to provide the best characterization of the survival times of the patients affected by the COVID-19 in China. By analyzing the survival times of the COVID-19 patient’s data, it is showed that the new model provides a closer fit to COVID-19 events.

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“…The comparison between the two neighbour countries Iran and Pakistan has appeared in [16]. A case of the COVID-19 pandemic in Indonesia has been discussed in [17]. For more information, reader can refer to [18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China

et al. 2021

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Over the past few months, the spread of the current COVID-19 epidemic has caused tremendous damage worldwide, and unstable many countries economically. Detailed scientific analysis of this event is currently underway to come. However, it is very important to have the right facts and figures to take all possible actions that are needed to avoid COVID-19. In the practice and application of big data sciences, it is always of interest to provide the best description of the data under consideration. The recent studies have shown the potential of statistical distributions in modeling data in applied sciences, especially in medical science. In this article, we continue to carry this area of research, and introduce a new statistical model called the arcsine modified Weibull distribution. The proposed model is introduced using the modified Weibull distribution with the arcsine-X approach which is based on the trigonometric strategy. The maximum likelihood estimators of the parameters of the new model are obtained and the performance these estimators are assessed by conducting a Monte Carlo simulation study. Finally, the effectiveness and utility of the arcsine modified Weibull distribution are demonstrated by modeling COVID-19 patients data. The data set represents the survival times of fifty-three patients taken from a hospital in China. The practical application shows that the proposed model out-classed the competitive models and can be chosen as a good candidate distribution for modeling COVID-19, and other related data sets.

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An SIR epidemic model for COVID-19 spread with fuzzy parameter: the case of Indonesia

Cited by 60 publications

References 32 publications

A particle swarm optimization approach for predicting the number of COVID-19 deaths

A particle swarm optimization approach for predicting the number of COVID-19 deaths

The current COVID-19 pandemic in China: An overview and corona data analysis

Modeling the survival times of the COVID-19 patients with a new statistical model: A case study from China

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