CoronaTracker: World-wide COVID-19 Outbreak Data Analysis and Prediction

Hamzah, Fairoza Amira Binti; Lau, Cher Han; Nazri, Hafeez; Ligot, Dominic Vincent; Lee, Guanhua; Tan, Cheng Liang; Shaib, Mohammad Khursani Bin Mohd; Zaidon, Ummi Hasanah Binti; Abdullah, Adina; Chung, Ming Hong; Ong, Chin Hwee; Chew, Pei Ying

doi:10.2471/blt.20.255695

Cited by 262 publications

(205 citation statements)

References 11 publications

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“…COVID-19 as an infectious disease that remains ambiguous; an accurate prediction can only be obtained when the pandemic ends ( Hamzah, et al, 2020 ). The pandemic is substantially influenced by each country’s policy and social responsibility ( Anderson, Heesterbeek, Klinkenberg, & Hollingsworth, 2020 ).…”

Section: Authors’ Claims and Discussionmentioning

confidence: 99%

“…The situational reports of the World Health Organisation’s ( WHO ) statistics have indicated that the number of confirmed cases exceeds 37,109,851 , and the number of deaths exceeds 1,070,355 worldwide ( WHO, 2020 ). Accurate insights into COVID-19 can only be obtained when the pandemic ends as literature and statistics are proliferating, and keeping data updated is nearly impossible ( Hamzah, et al, 2020 ). On 28 February 2020 , the WHO launched emergency protocols in all medical and public health systems because of the severity and risks of COVID-19 ( Epidemiol, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Alamoodi

Zaidan

et al. 2021

Expert Systems with Applications

287

152

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Highlights Understanding Sentiment Analysis Role and Opinion Mining In Covid-19 and Other Infectious Diseases. Literature’s Categorization for Sentiment Analysis and Infectious Disease. Academic Challenges and Motivations of Sentiment Analysis with Infectious Diseases. Different Applications for Mitigating Infectious Diseases by Sentiment Analysis.

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Section: Authors’ Claims and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Alamoodi

Zaidan

et al. 2021

Expert Systems with Applications

287

152

View full text Add to dashboard Cite

show abstract

“…Each neuron has a set of weights where each one is multiplied by one input into the neuron, these are then summed to form the output from the neuron after it has been fed through the activation function. Deep neural networks often require big data and huge numbers of training data to be able to give performing results [9].…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

“…In fact, our goal here is to reuse the models already built and trained on china series times dataset to predict COVID-19 outbreak in Tunisia. Our motivation is to increase the size of training data as DNN often requires huge numbers of training data [9]. So in this augmented prediction, China data are used as training data and Tunisia data are considers as Test data.…”

Section: Augmented Prediction: From China To Tunisiamentioning

confidence: 99%

WITHDRAWN: Deep Ensemble Learning Method to Forecast COVID-19 Outbreak

Nb¹,

Kandara²,

Nbb³

2020

Preprint

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Due to the continuous spread of the novel coronavirus (COVID-19) worldwide, it is urgent to develop accurate decision-aided methods to support healthcare policymakers to control and early detect COVID-19 outbreak especially in the data science era. In this context, our main goal is to build a generic and accurate method that can predict daily conrmed cases which helps stake-holders to make and review their epidemic response plans. This method takes advantage of the complementarity of DNN (Deep Neuronal Networks), LSTM (Long Short Term Memory) and CNN (Convolutional Neuronal Networks) where their forecasted values represent the inputs of stacked ensemble meta-learners that will generate the nal outbreak predictions. To the best of our knowledge, this is the rst time that deep ensemble learning is used to deal with this issue. The proposed method is validated on three experimental scenarios, Tunisia case study, China case study and the third one is based on China data and models to predict Tunisia COVID-19 outbreak. Experiment results indicate that, compared with individual learners, the stacked-DNN meta-learner, whose input are forecasted values of DNN, LSTM and CNN, achieved the best accurate results in terms of accuracy as well as RMSE for the three scenarios. In conclusion, our ndings demonstrate that i) deep ensemble learning may be used as an accurate decision support tool for improving COVID-19 outbreak forecasting, ii) it is possible to reuse China learners and meat-learners to make prediction of the epidemic trend for other countries when preventive and control measures are comparable.

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“…Since COVID-19 was first detected on 25 January 2020 in Malaysia, over 6,000 people have been infected by the virus, and a total 113 deaths has been reported as of 18 May 2020 [3]. In order to curb this pandemic, Malaysia has instituted several non-pharmaceutical intervention (NPI) strategies including the Movement Control Order (MCO) which was first implemented on 18 March 2020.…”

Section: Introductionmentioning

confidence: 99%

Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models

Singh

Sundram

Rajendran

et al. 2020

J Infect Dev Ctries

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Introduction: The novel coronavirus infection has become a global threat affecting almost every country in the world. As a result, it has become important to understand the disease trends in order to mitigate its effects. The aim of this study is firstly to develop a prediction model for daily confirmed COVID-19 cases based on several covariates, and secondly, to select the best prediction model based on a subset of these covariates. Methodology: This study was conducted using daily confirmed cases of COVID-19 collected from the official Ministry of Health, Malaysia (MOH) and John Hopkins University websites. An Autoregressive Integrated Moving Average (ARIMA) model was fitted to the training data of observed cases from 22 January to 31 March 2020, and subsequently validated using data on cases from 1 April to 17 April 2020. The ARIMA model satisfactorily forecasted the daily confirmed COVID-19 cases from 18 April 2020 to 1 May 2020 (the testing phase). Results: The ARIMA (0,1,0) model produced the best fit to the observed data with a Mean Absolute Percentage Error (MAPE) value of 16.01 and a Bayes Information Criteria (BIC) value of 4.170. The forecasted values showed a downward trend of COVID-19 cases until 1 May 2020. Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. Conclusions: This study finds that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Malaysia.

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CoronaTracker: World-wide COVID-19 Outbreak Data Analysis and Prediction

Abstract: DISCLAIMER This paper was submitted to the Bulletin of the World Health Organization and was posted to the COVID-19 open site, according to the protocol for public health emergencies for international concern as described in Vasee Moorthy et al.

Cited by 262 publications

References 11 publications

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

WITHDRAWN: Deep Ensemble Learning Method to Forecast COVID-19 Outbreak

Forecasting daily confirmed COVID-19 cases in Malaysia using ARIMA models

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