On the necessity of proper quarantine without lock down for 2019-nCoV in the absence of vaccine

Sahoo, Prasanta; Mondal, Himadri Shekhar; Hammouch, Zakia; Abdeljawad, Thabet; Mishra, Dwaipayan; Reza, Motahar

doi:10.1016/j.rinp.2021.104063

Cited by 23 publications

(11 citation statements)

References 21 publications

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“… [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] .…”

Section: Uncited Referencesunclassified

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

et al. 2021

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The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

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Section: Uncited Referencesunclassified

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

et al. 2021

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“…In general, different stochastic computations [7,8] and numerical methods [9][10][11][12][13][14] are exploited to assess the various aspects of the COVID-19 outbreak. In this sense, Katoch et al used the autoregressive integrated moving average (ARIMA) model to forecast the COVID-19 dynamics in India [15].…”

Section: Sars-cov-2 (Covid-19mentioning

confidence: 99%

Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries

Khoojine

Shadabfar

Hosseini

et al. 2021

Entropy

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Predicting the way diseases spread in different societies has been thus far documented as one of the most important tools for control strategies and policy-making during a pandemic. This study is to propose a network autoregressive (NAR) model to forecast the number of total currently infected cases with coronavirus disease 2019 (COVID-19) in Iran until the end of December 2021 in view of the disease interactions within the neighboring countries in the region. For this purpose, the COVID-19 data were initially collected for seven regional nations, including Iran, Turkey, Iraq, Azerbaijan, Armenia, Afghanistan, and Pakistan. Thenceforth, a network was established over these countries, and the correlation of the disease data was calculated. Upon introducing the main structure of the NAR model, a mathematical platform was subsequently provided to further incorporate the correlation matrix into the prediction process. In addition, the maximum likelihood estimation (MLE) was utilized to determine the model parameters and optimize the forecasting accuracy. Thereafter, the number of infected cases up to December 2021 in Iran was predicted by importing the correlation matrix into the NAR model formed to observe the impact of the disease interactions in the neighboring countries. In addition, the autoregressive integrated moving average (ARIMA) was used as a benchmark to compare and validate the NAR model outcomes. The results reveal that COVID-19 data in Iran have passed the fifth peak and continue on a downward trend to bring the number of total currently infected cases below 480,000 by the end of 2021. Additionally, 20%, 50%, 80% and 95% quantiles are provided along with the point estimation to model the uncertainty in the forecast.

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“…Finally, the prediction efficiencies of the models applied in this study are satisfactory, despite the fact that the models can be categorized as simple because they are based on elementary mathematical equations that describe growth curves and a computational model based on artificial intelligence. However, the results obtained can be compared in efficiency with complex mathematical models, such as: Fractional Multi-Order Model [32] , [33] , SQIR epidemic model [34] , non-linear epidemic model [35] , among others. Therefore, we recommend the application of both simple and complex models to compare the prediction efficiency and to provide accurate information on the dynamics of the disease.…”

Section: Covid-19 Recovered Casesmentioning

confidence: 99%

Predictive models for COVID-19 cases, deaths and recoveries in Algeria

Lounis¹,

Torrealba-Rodriguez²,

Conde-Gutiérrez³

2021

Results in Physics

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This study was conducted to predict the number of COVID-19 cases, deaths and recoveries using reported data by the Algerian Ministry of health from February 25, 2020 to January 10, 2021. Four models were compared including Gompertz model, logistic model, Bertalanffy model and inverse artificial neural network (ANNi). Results showed that all the models showed a good fit between the predicted and the real data (R 2 >0.97). In this study, we demonstrate that obtaining a good fit of real data is not directly related to a good prediction efficiency with future data. In predicting cases, the logistic model obtained the best precision with an error of 0.92% compared to the rest of the models studied. In deaths, the Gompertz model stood out with a minimum error of 1.14%. Finally, the ANNi model reached an error of 1.16% in the prediction of recovered cases in Algeria. .

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On the necessity of proper quarantine without lock down for 2019-nCoV in the absence of vaccine

Cited by 23 publications

References 21 publications

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods

Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries

Predictive models for COVID-19 cases, deaths and recoveries in Algeria

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