Assessment of worldwide COVID-19 transmission landscape for predicting its upcoming severity along with a clinical update for its prevention

Choudhuri, Adhyeta; Khatun, M. Mahfuza; Bandyopadhyay, Arnab; Dasgupta, U.; Chowdhury, Sufia; Biswas, Anirban; Samanta, Indrani; Das, Dipanjana; Sinha, Debolina; Ghatak, A.; Das, Rajarshi; Ghosh, S.; Saha, Chinmay; Kundu, Sharbadeb

doi:10.22541/au.160347794.45407178/v1

Cited by 2 publications

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“…Since no vaccine has been created for infectious diseases, such as COVID-19, vaccinations cannot be used to treat them. A limited supply of vaccines controlling the spread of COVID-19 requires a focus on early detection and prevention (18). COVID-19 many prediction models are currently available, including the logistic regression analysis LSTM, ARIMA, and TBATS models (19).…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Zhao¹,

Sun

Liu

et al. 2022

Front. Public Health

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A major emphasis is the dissemination of COVID-19 across the country's many regions and provinces. Using the present COVID-19 pandemic as a guide, the researchers suggest a hybrid model architecture for analyzing and optimizing COVID-19 data during the complete country. The analysis of COVID-19's exploration and death rate uses an ARIMA model with susceptible-infectious-removed and susceptible-exposed-infectious-removed (SEIR) models. The logistic model's failure to forecast the number of confirmed diagnoses and the snags of the SEIR model's too many tuning parameters are both addressed by a hybrid model method. Logistic regression (LR), Autoregressive Integrated Moving Average Model (ARIMA), support vector regression (SVR), multilayer perceptron (MLP), Recurrent Neural Networks (RNN), Gate Recurrent Unit (GRU), and long short-term memory (LSTM) are utilized for the same purpose. Root mean square error, mean absolute error, and mean absolute percentage error are used to show these models. New COVID-19 cases, the number of quarantines, mortality rates, and the deployment of public self-protection measures to reduce the epidemic are all outlined in the study's findings. Government officials can use the findings to guide future illness prevention and control choices.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Zhao¹,

Sun

Liu

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…The amount of vaccine availability is limited. So, predicting and preventing is the top priority to tackle the spread of Covid-19 [ 4 ]. A new type of coronavirus caused the 2019 coronavirus disease.…”

Section: Introductionmentioning

confidence: 99%

A hybrid approach to forecast the COVID-19 epidemic trend

Nawaz

Bhatti

et al. 2021

PLoS ONE

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Studying the progress and trend of the novel coronavirus pneumonia (COVID-19) transmission mode will help effectively curb its spread. Some commonly used infectious disease prediction models are introduced. The hybrid model is proposed, which overcomes the disadvantages of the logistic model’s inability to predict the number of confirmed diagnoses and the drawbacks of too many tuning parameters of the SEIR (Susceptible, Exposed, Infectious, Recovered) model. The realization and superiority of the prediction of the proposed model are proven through experiments. At the same time, the influence of different initial values of the parameters that need to be debugged on the hybrid model is further studied, and the mean error is used to quantify the prediction effect. By forecasting epidemic size and peak time and simulating the effects of public health interventions, this paper aims to clarify the transmission dynamics of COVID-19 and recommend operation suggestions to slow down the epidemic. It is suggested that the quick detection of cases, sufficient implementation of quarantine and public self-protection behaviours are critical to slow down the epidemic.

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Assessment of worldwide COVID-19 transmission landscape for predicting its upcoming severity along with a clinical update for its prevention

Cited by 2 publications

References 0 publications

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Prediction of COVID-19 Data Using Hybrid Modeling Approaches

A hybrid approach to forecast the COVID-19 epidemic trend

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