Development of new hybrid model of discrete wavelet decomposition and autoregressive integrated moving average (ARIMA) models in application to one month forecast the casualties cases of COVID-19

Singh, Sarbjit; Parmar, Kulwinder Singh; Kumar, Jagdish; Makkhan, Sidhu Jitendra Singh

doi:10.1016/j.chaos.2020.109866

Cited by 131 publications

(115 citation statements)

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“…Besides, recent studies have demonstrated that some hybrid models can also provide a close approximation to the temporal patterns of the COVID-19 outbreak. 50,51 For example, Singh et al proposed a new hybrid model by combining wavelet decomposition and ARIMA model to make forecasting for the total death cases of COVID-19 in Italy, Spain, France, the UK, and the USA. 51 Chakraborty et al developed a novel hybrid ARIMA-wavelet-based forecasting (WBF) model to make real-time forecasts and risk assessment for the morbidity cases of COVID-19 in India, Canada, France, South Korea, and the UK.…”

Section: Discussionmentioning

confidence: 99%

“…50,51 For example, Singh et al proposed a new hybrid model by combining wavelet decomposition and ARIMA model to make forecasting for the total death cases of COVID-19 in Italy, Spain, France, the UK, and the USA. 51 Chakraborty et al developed a novel hybrid ARIMA-wavelet-based forecasting (WBF) model to make real-time forecasts and risk assessment for the morbidity cases of COVID-19 in India, Canada, France, South Korea, and the UK. 50 Therefore, future work is supposed to compare the reliability level in forecasting the epidemiological indicators of the COVID-19 outbreak between the ETS model and the mentioned hybrid techniques.…”

Section: Discussionmentioning

confidence: 99%

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<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

Wang¹,

Xu²,

Yao³

et al. 2020

IDR

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The aim of this study is to apply the advanced error-trend-seasonal (ETS) framework to forecast the prevalence and mortality series of COVID-19 in the USA, the UK, Russia, and India, and the predictive performance of the ETS framework was compared with the most frequently used autoregressive integrated moving average (ARIMA) model. Materials and Methods: The prevalence and mortality data of COVID-19 in the USA, the UK, Russia, and India between 20 February 2020 and 15 May 2020 were extracted from the WHO website. Then, the data subsamples between 20 February 2020 and 3 May 2020 were treated as the training horizon, and the others were used as the testing horizon to construct the ARIMA models and the ETS models. Results: Based on the model evaluation criteria, the ARIMA (0,2,1) and ETS (M,MD,N), sparse coefficient ARIMA (0,2,(1,6)) and ETS (A,AD,M), ARIMA (1,1,1) and ETS (A,MD, A), together with ARIMA (2,2,1) and ETS (A,M,A) specifications were identified as the preferred ARIMA and ETS models for the prevalence data in the USA, the UK, Russia, and India, respectively; the ARIMA (0,2,1) and ETS (M,A,M), ARIMA (0,2,1) and ETS (M,A, N), ARIMA (0,2,1) and ETS (A,A,N), coupled with ARIMA (0,2,2) and ETS (M,M,N) specifications were selected as the optimal ARIMA and ETS models for the mortality data in these four countries, respectively. Among these best-fitting models, the ETS models produced smaller forecasting error rates than the ARIMA models in all the datasets. Conclusion: The ETS framework can be used to nowcast and forecast the long-term temporal trends of the COVID-19 prevalence and mortality in the USA, the UK, Russia, and India, and which provides a notable performance improvement over the most frequently used ARIMA model. Our findings can aid governments as a reference to prepare for and respond to the COVID-19 pandemic both in restricting the transmission of the disease and in lowering the disease-related deaths in the upcoming days.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

<p>Estimating the Prevalence and Mortality of Coronavirus Disease 2019 (COVID-19) in the USA, the UK, Russia, and India</p>

Wang¹,

Xu²,

Yao³

et al. 2020

IDR

View full text Add to dashboard Cite

show abstract

“…It was also recommended to extend the lockdown period in case of sharply increase in the number of confirmed cases and deaths. The incorporation between discrete wavelet decomposition and ARIMA to forecast deaths due to COVID-19 in France, Spain, Italy, United Kingdom, and USA was presented by ( Singh et al, 2020 ). The results obtained by the proposed hybrid model reduced the forecasting errors by about 50 % compared with standalone ARIMA model.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia

Elsheikh

Saba

Elaziz

et al. 2021

Process Safety and Environmental Protection

118

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“…This global emergency brings with it important and yet unanswered questions related to the dynamic behavior of the contagion and its mitigation and control strategies. As a result, measures carried out to contain the contagion such as social distancing, quarantine, and complete lock-down of areas have been studied [5] , [6] , [7] , [8] , [9] , [10] .…”

Section: Introductionmentioning

confidence: 99%