Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA)

Balah, Belkacem; Djeddou, Messaoud

doi:10.1101/2020.05.03.20089615

Cited by 6 publications

(5 citation statements)

References 10 publications

(11 reference statements)

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“…Although researchers like [9,10,11] have used some of these techniques to model and forecast COVID-19 cases in other countries, the selection of the appropriate method was not exhaustive.…”

Section: Boldface = Minimum Error Metricmentioning

confidence: 99%

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Forecasting COVID-19 Confirmed Cases in Ghana: A Model Selection Approach

Twumasi-Ankrah

Owusu

Appiah

et al. 2021

PoS

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This study seeks to determine an appropriate statistical technique for forecasting the cumulated confirm cases of Coronavirus in Ghana. Cumulated daily data spanning from March 12, 2020, to August 04, 2020, was retrieved from the Center for Systems Science and Engineering at Johns Hopkins University. Four statistical forecasting techniques: Autoregressive Integrated Moving Average, Artificial Neural Network, Exponential smoothing and Autoregressive Fractional Integrated Moving Average were fitted to the COVID-19 series. Their respective forecast accuracy measures were compared to select the appropriate technique for forecasting the COVID-19 cases. Our findings revealed that the ARFIMA technique was a suitable statistical model for predicting COVID-19 cases in Ghana. The "best" model for forecasting is ARFIMA (2, 0.49, 4) which passed all the needed diagnostic tests. An unequal weight was estimated to derive a combined model for all four forecasting techniques. A 149-cumulated daily forecast from the "best" model and the combined model revealed that the number of confirmed COVID-19 cases would increase slightly until the end of this year.

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“…Although researchers like [9,10,11] have used some of these techniques to model and forecast COVID-19 cases in other countries, the selection of the appropriate method was not exhaustive.…”

Section: Boldface = Minimum Error Metricmentioning

confidence: 99%

“…Some studies have modelled and forecasted the COVID-19 pandemic using the time series analysis methods [7,8,9,10,11]. Although all countries deal with the same SARS-CoV-2, predicting future outbreaks seems to differ based on cases' unique pattern.…”

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 Confirmed Cases in Ghana: A Model Selection Approach

Twumasi-Ankrah

Owusu

Appiah

et al. 2021

PoS

View full text Add to dashboard Cite

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“…The order of ARFIMA is ( , , ), is autoregressive order ( ), is the moving average order ( ), and ( ) is order of fractional differentiation. Among authors who applied time series to forecast cases of COVID-19 was [23]. The author used ARFIMA to model cases of COVID-19 in Algeria.…”

Section: The Time Series Modelmentioning

confidence: 99%

Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models

Adesina

Onanaye

Okewole

et al. 2020

ARJOM

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The emergence of global pandemic known as COVID-19 has impacted significantly on human lives and measures have been taken by government all over the world to minimize the rate of spread of the virus, one of which is by enforcing lockdown. In this study, Autoregressive fractionally integrated moving average (ARFIMA) Models was used to model and forecast what the daily new cases of COVID-19 would have been ten days after the lockdown was eased in Nigeria and compare to the actual new cases for the period when the lockdown was eased. The proposed model ARFIMA model was compared with ARIMA (1, 0, 0), and ARIMA (1, 0, 1) and found to outperform the classical ARIMA models based on AIC and BIC values. The results show that the rate of spread of COVID-19 would have been significantly less if the strict lockdown had continued. ARFIMA model was further used to model what new cases of COVID-19 would be ten days ahead starting from 31st of August 2020. Therefore, this study recommends that government should further enforce measures to reduce the spread of the virus if business must continue as usual.

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“…Multiple models have been achieved in Algeria to forecast the epidemic or to evaluate the preventive implemented measure with different approaches including local, regional and global studies (Balah and Djeddou, 2020;Belkacem, 2020;Ben Hassen et al, 2020;Bentout et al, 2020;Boudrioua and Boudrioua, 2020;Daw and El Bouzebdei, 2020;Hamidouche, 2020;Moussaoui and Auger, 2020;Nail et al, 2020;Rahmani, 2020;Rezki, 2020;Rouabah et al, 2020;Zhao et al, 2020). These studies used the most common SIR (Boudrioua and Boudrioua, 2020;Rezki, 2020) and SEIR (Belkacem, 2020;Bentout et al, 2020;Moussaoui and Auger, 2020;Peng et al, 2020;Rouabah et al, 2020) models or other mathematical models such as ARFIMA (Autoregressive Fractionally Integrated Moving Average Models) (Balah and Djeddou, 2020) and the Alg-COVID-19 Model (Rahmani, 2020).…”

Section: Introductionmentioning

confidence: 99%

Application of a Generalized SEIR Model for COVID-19 in Algeria

Lounis

Azevedo

2021

EUR J SUSTAIN DEV RE

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The novel coronavirus disease 2019 reported in Wuhan is continuing to impress the world by its fast spread and the number of affected persons attracting an unprecedented attention.In this article, the classical SEIR model and a generalized SEIR model called SEIRDP were applied to predict the evolution of COVID-19 in Algeria for a future period of 100 days using official reported data from early April to early August, 2020. Initial evaluation showed that the two models had a net correspondence with the reported data during this period for cumulative infected cases but the number of cumulative deaths was underestimated with the classical SEIR model. Model prediction with the SEIRDP concluded that the number of cumulative infected cases will increase in the next days reaching a number of about 60 k in middle November with a median of about 300 daily cases. Also, the number of estimated deaths will be around 2k. These results suggest that the COVID-19 is ongoing to infect more persons which may push national authorities to carefully act in the probable leaving of containment.

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Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA)

Abstract: In this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks ahead. In the present study, public health database from Algeria health ministry has been used to build an ARFIMA model and used to forecast COVID-19 new cases in Algeria until May 11, 2020.

Cited by 6 publications

References 10 publications

Forecasting COVID-19 Confirmed Cases in Ghana: A Model Selection Approach

Forecasting COVID-19 Confirmed Cases in Ghana: A Model Selection Approach

Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models

Application of a Generalized SEIR Model for COVID-19 in Algeria

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