COVID-19 in Iran: Forecasting Pandemic Using Deep Learning

Kafieh, Rahele; Arian, Roya; Saeedizadeh, Narges; Amini, Zahra; Serej, Nasim Dadashi; Minaee, Shervin; Yadav, Sunil Kumar; Vaezi, Atefeh; Rezaei, Nima; Javanmard, Shaghayegh Haghjooy

doi:10.1155/2021/6927985

Cited by 48 publications

(29 citation statements)

References 27 publications

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“…The results indicated that with a root mean square error of 12.3, the Holt-winter model can be a good predictor of COVID-19 deaths cases in Iran. Kafieh et al (2021) provided a similar study concerning COVID-19 trend forecasting in Iran as well as other countries. The results demonstrated that with a root mean square error of 458.12, the multivariate long short-term memory is the optimal choice.…”

Section: Literature Surveymentioning

confidence: 93%

Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

Shaibani

Emamgholipour

Moazeni

2021

Preprint

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As an ongoing public health menace, the novel coronavirus pandemic has challenged the world. With several mutations and a high transmission rate, the virus is able to infect individuals in an exponential manner. At the same time, Iran is confronted with multiple wave peaks and the health care system is facing a major challenge. In consequence, developing a robust forecasting methodology can assist health authorities for effective planning. In that regard, with the help of Artificial Neural Network-Artificial Bee Colony (ANN-ABC) and Artificial Neural Network- Firefly Algorithm (ANN-FA) as two robust hybrid artificial intelligence-based models, the current study intends to select the optimal model with the maximum accuracy rate. To do so, first a sample of COVID-19 confirmed cases in Iran ranging from 19 February 2020 to 25 July 2021 is compiled. 75% (25%) of total observation is randomly allocated as training (testing) data. Afterwards, an ANN models is trained with Levenberg Marquardt algorithm. Accordingly, based on R-squared and root-mean-square error criteria, the optimal number of hidden neurons is computed as 17. The proposed ANN model is employed to develop ANN-ABC and ANN-FA models for achieving the maximum accuracy rate. According to ANN-ABC, the R- squared values of the optimal model are 0.9884 and 0.9885 at train and test stages, correspondingly. In respect to ANN-FA, for the selected model, the R-squared ranged from 0.9954 to 0.9940 at the train and test phases, respectively. The results indicated that both hybrid ANN-ABC and ANN-FA are the robust predictor of COVID-19 new cases in Iran. Additionally, with a slight difference, the ANN-FA model outperformed ANN-ABC algorithm.

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Section: Literature Surveymentioning

confidence: 93%

Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

Shaibani

Emamgholipour

Moazeni

2021

Preprint

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“…Additionally, In Kafieh et al (2021) [ 25 ], the main objective was to predict outbreaks in nine countries - Iran, Germany, Italy, Japan, Japan, Korea, Switzerland, Spain, China, and the United States of America. Data between 22 January and 30 July 2020 were used for training and the period 1–31 August 2020 for testing.…”

Section: Methodsmentioning

confidence: 99%

Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves

Kolozsvári¹,

Bérczes²,

Hajdú³

et al. 2021

Informatics in Medicine Unlocked

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Objectives The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. Methods We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data. Results We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low. Conclusion Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.

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“…Nevertheless, the infection rate fell to 65% after implementing global travel restrictions [2]. COVID-19 also spread to different parts of Asia, and the number of infections was very high in Turkey, India, Indonesia, and Iran [3][4][5][6]. The middle to low-income countries in Asia and the Asia-Pacific regions were the worst affected [6].…”

Section: Introductionmentioning

confidence: 99%

“…COVID-19 also spread to different parts of Asia, and the number of infections was very high in Turkey, India, Indonesia, and Iran [3][4][5][6]. The middle to low-income countries in Asia and the Asia-Pacific regions were the worst affected [6]. However, some success stories in controlling the spread of the disease are from small Asian countries such as Taiwan, Vietnam, and South Korea [7].…”

Section: Introductionmentioning

confidence: 99%

Asian-Origin Approved COVID-19 Vaccines and Current Status of COVID-19 Vaccination Program in Asia: A Critical Analysis

et al. 2021

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COVID-19 vaccination has started throughout the globe. The vaccination program has also begun in most Asian countries. This paper analyzed the Asian-origin COVID-19 vaccines and vaccination program status in Asia till March 2021 under three sections. In the first section, we mapped the approved vaccines that originated from Asia, their technological platforms, collaborations during vaccine development, and regulatory approval from other countries. We found that a total of eight Asian COVID-19 vaccines originated and got approval from three countries: China, India, and Russia. In the second section, we critically evaluated the recent progress of COVID-19 vaccination programs. We analyzed the overall vaccination status across the Asian region. We also calculated the cumulative COVID-19 vaccine doses administered in different Asian countries, vaccine rolling in 7-day average in various Asian countries, and COVID-19 vaccine per day doses administrated in several Asian countries. We found that China and India vaccinated the maximum number of people. Finally, we evaluated the factors affecting the COVID-19 vaccination program in Asia, such as vaccine hesitancy, basic reproduction numbers (R0) and vaccination campaigns, and the cost of the vaccines. Our analysis will assist the implementation of the COVID-19 vaccination program successfully in Asia.

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COVID-19 in Iran: Forecasting Pandemic Using Deep Learning

Cited by 48 publications

References 27 publications

Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves

Asian-Origin Approved COVID-19 Vaccines and Current Status of COVID-19 Vaccination Program in Asia: A Critical Analysis

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