Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach

Long, Jie; Furati, Khaled M.

doi:10.1080/00207160.2021.1929942

Cited by 31 publications

(31 citation statements)

References 33 publications

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“…Thus, the output layer processes the net output from the last hidden layer to produce the desired outcome. If the task is a classification task, then the output layer will produce discrete outcomes but if the task is a regression task, then the output layer will produce a continuous-valued outcome [2,22]. The mathematical formula that transforms the data from one layer to the other is defined as follows [23]:…”

Section: Feedforward Neural Network (Fnn)mentioning

confidence: 99%

“…Different loss functions and optimizers such as Adam or gradient descent method [2] are used to build and train the network to get the output. The difference between the network's output and the actual data is computed as the error.…”

Section: Feedforward Neural Network (Fnn)mentioning

confidence: 99%

“…The spread of SARS-CoV-2 virus began in Wuhan, China December, 2019 [1]. The virus spread across the globe quickly, affecting many lives and causing deaths, thereby making the World Health Organization (WHO) [1] to declare it a pandemic and naming it COVID-19 [1,2]. Studies on mitigation measures such as social distancing, quarantining, and government shut-downs for controlling the disease show the need for an effective vaccination program.…”

Section: Introductionmentioning

confidence: 99%

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Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy

Torku

Furati

2021

Epidemiologia

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Vaccination strategies to lessen the impact of the spread of a disease are fundamental to public health authorities and policy makers. The socio-economic benefit of full return to normalcy is the core of such strategies. In this paper, a COVID-19 vaccination model with efficacy rate is developed and analyzed. The epidemiological parameters of the model are learned via a feed-forward neural network. A hybrid approach that combines residual neural network with variants of recurrent neural network is implemented and analyzed for reliable and accurate prediction of daily cases. The error metrics and a k-fold cross validation with random splitting reveal that a particular type of hybrid approach called residual neural network with gated recurrent unit is the best hybrid neural network architecture. The data-driven simulations confirm the fact that the vaccination rate with higher efficacy lowers the infectiousness and basic reproduction number. As a study case, COVID-19 data for the state of Tennessee in USA is used.

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Section: Feedforward Neural Network (Fnn)mentioning

confidence: 99%

Section: Feedforward Neural Network (Fnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy

Torku

Furati

2021

Epidemiologia

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“…The model allowed the prediction of the next day’s infected cases and deaths associated with Covid-19 worldwide on a cumulative basis, using the infected case and death counts of the preceding days. Long et al ( 2021 ) adapted a variant of physics-informed neural network for an effort to identify the time-varying parameters of the Susceptible-Infectious-Recovered-Deceased model pertaining to the spread of Covid-19. To do so, they used daily reported case numbers.…”

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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“…PINN has been used to solve system of ordinary differential equations [29] and system of fractional differential equations [30]. In [31], an algorithm that combines PINN together with LSTM is presented to solve an epidemiological model and identify weekly and daily time-varying parameters.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Deep-Learning Algorithm for Asymptomatic COVID-19 Model with Varying Mitigation Measures and Transmission Rate

Olumoyin

Furati

2021

Epidemiologia

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Epidemiological models with constant parameters may not capture satisfactory infection patterns in the presence of pharmaceutical and non-pharmaceutical mitigation measures during a pandemic, since infectiousness is a function of time. In this paper, an Epidemiology-Informed Neural Network algorithm is introduced to learn the time-varying transmission rate for the COVID-19 pandemic in the presence of various mitigation scenarios. There are asymptomatic infectives, mostly unreported, and the proposed algorithm learns the proportion of the total infective individuals that are asymptomatic infectives. Using cumulative and daily reported cases of the symptomatic infectives, we simulate the impact of non-pharmaceutical mitigation measures such as early detection of infectives, contact tracing, and social distancing on the basic reproduction number. We demonstrate the effectiveness of vaccination on the transmission of COVID-19. The accuracy of the proposed algorithm is demonstrated using error metrics in the data-driven simulation for COVID-19 data of Italy, South Korea, the United Kingdom, and the United States.

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Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach

Cited by 31 publications

References 33 publications

Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy

Deep-Data-Driven Neural Networks for COVID-19 Vaccine Efficacy

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

Data-Driven Deep-Learning Algorithm for Asymptomatic COVID-19 Model with Varying Mitigation Measures and Transmission Rate

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