A predictive analytics model for COVID-19 pandemic using artificial neural networks

Kuvvetli, Yusuf; Deveci, Muhammet; Paksoy, Turan; Garg, Harish

doi:10.1016/j.dajour.2021.100007

Cited by 32 publications

(20 citation statements)

References 44 publications

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“…Spatiotemporal COVID-19 modeling involving prevalence and mortality using neural networks [ 20 ], Short term prediction of COVID-19 cased for the Brazilian prospectives [ 21 ]. An analytical model for COVID-19 pandemic with the help of artificial neural networks [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Numerical treatment on the new fractional-order SIDARTHE COVID-19 pandemic differential model via neural networks

2022

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In this study, modeling the COVID-19 pandemic via a novel fractional-order SIDARTHE (FO-SIDARTHE) differential system is presented. The purpose of this research seemed to be to show the consequences and relevance of the fractional-order (FO) COVID-19 SIDARTHE differential system, as well as FO required conditions underlying four control measures, called SI, SD, SA, and SR. The FO-SIDARTHE system incorporates eight phases of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatening (T), healed (H), and extinct (E). Our objective of all these investigations is to use fractional derivatives to increase the accuracy of the SIDARTHE system. A FO-SIDARTHE system has yet to be disclosed, nor has it yet been treated using the strength of stochastic solvers. Stochastic solvers based on the Levenberg–Marquardt backpropagation methodology (L-MB) and neural networks (NNs), specifically L-MBNNs, are being used to analyze a FO-SIDARTHE problem. Three cases having varied values under the same fractional order are being presented to resolve the FO-SIDARTHE system. The statistics employed to provide numerical solutions toward the FO-SIDARTHE system are classified as obeys: 72% toward training, 18% in testing, and 10% for authorization. To establish the accuracy of such L-MBNNs utilizing Adams–Bashforth–Moulton, the numerical findings were compared with the reference solutions.

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Section: Introductionmentioning

confidence: 99%

Numerical treatment on the new fractional-order SIDARTHE COVID-19 pandemic differential model via neural networks

2022

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“…Sajadi et al ( 2020 ) investigate the effects of parameters such as temperature and humidity on the potential spreading to COVID19. Kuvvetli et al ( 2021 ) propose different artificial neural network and deep learning algorithms to predict the spread of COVID19 for different countries. Rest and Hirsch ( 2021 ) consider a home-healthcare scheduling problem during pandemic by proposing a decision support system.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A goal programming model for two-stage COVID19 test sampling centers location-allocation problem

Kuvvetli

2022

Cent Eur J Oper Res

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The COVID19 virus, which first appeared in Wuhan, China, and has become a pandemic in a short time, has threatened the health system in many countries and put it into a bottleneck. Simultaneously, the second wave's expectation spread it necessary to plan the health services correctly. In this study, a location-allocation problem in the two-echelon system, which considers different test sampling alternatives, is examined to obtain test sampling centers' location-allocation. The problem is modeled as a goal programming model to create a network that tests samples at a minimum total distance, establishes a minimum number of test sampling centers, and reaches the distance of PCR test laboratories at minimum total distances. The proposed model is applied as a case study for the two cities located in Turkey, and the obtained locations and inventory levels of each location are presented. Besides, different scenarios are examined to understand the structure of the model. As a result, only testing in hospitals will increase the risk of contamination. Since testing at all points will not be possible administratively, it will be ensured that the most appropriate location-allocation decisions are taken by considering all the proposed model's objectives.

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“…Artificial neural networks (ANN) were used for prediction. ANN, which are widely used in machine learning and are inspired by the information processing technique of the human brain, enable the understanding of the relationships between dependent and independent variables affecting the system when the mathematical model of the system cannot be created (Borghi et al 2021 ; Kuvvetli et al 2021 ; Khojasteh et al 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…In conclusion, they adopted for the generalized regression neural network, given the fact that it can be trained accurately with a rather small dataset. Kuvvetli et al ( 2021 ), have designed an artificial neural network model that will predict the daily number of cases and deaths caused by the Covid-19 pandemic in a generalized manner. Reddy et al (in press), in turn, proposed a neural network-based prediction model for the number of Covid-19 cases in India.…”

Section: Introductionmentioning

confidence: 99%

Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels

Keskin

Doğruparmak

Ergun

2022

Environ Sci Pollut Res

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The dilemma between health concerns and the economy is apparent in the context of strategic decision making during the pandemic. In particular, estimating the patient numbers and achieving an informed management of the dilemma are crucial in terms of the strategic decisions to be taken. The Covid-19 pandemic presents an important case in this context. Sustaining the efforts to cope with and to put an end to this pandemic requires investigation of the spread and infection mechanisms of the disease, and the factors which facilitate its spread. Covid-19 symptoms culminating in respiratory failure are known to cause death. Since air quality is one of the most significant factors in the progression of lung and respiratory diseases, it is aimed to estimate the number of Covid-19 patients corresponding to the pollutant parameters (PM10, PM2.5, SO 2 , NO X , NO 2 , CO, O 3 ) after determining the relationship between air pollutant parameters and Covid-19 patient numbers in Turkey. For this purpose, artificial neural network was used to estimate the number of Covid-19 patients corresponding to air pollutant parameters in Turkey. To obtain highest accuracy levels in terms of network architecture structure, various network structures were tested. The optimal performance level was developed with 15 neurons combined with one hidden layer, which achieved a network performance level as high as 0.97342. It was concluded that Covid-19 disease is affected from air pollutant parameters and the number of patients can be estimated depending on these parameters by this study. Since it is known that the struggle against the pandemic should be handled in all aspects, the result of the study will contribute to the establishment of environmental decisions and precautions.

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A predictive analytics model for COVID-19 pandemic using artificial neural networks

Cited by 32 publications

References 44 publications

Numerical treatment on the new fractional-order SIDARTHE COVID-19 pandemic differential model via neural networks

Numerical treatment on the new fractional-order SIDARTHE COVID-19 pandemic differential model via neural networks

A goal programming model for two-stage COVID19 test sampling centers location-allocation problem

Estimation of COVID-19 patient numbers using artificial neural networks based on air pollutant concentration levels

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