A note on Covid-19 Statistics, Strange trend and Forecasting of Total Cases in the most Infected African Countries: An ARIMA and Fuzzy Time Series Approaches

Fatih, Chellai; Hamimes, Ahmed; Mishra, Pradeep Kumar

doi:10.16929/ajas/2020.967.250

Cited by 4 publications

(4 citation statements)

References 19 publications

(21 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ababsa et al examined the spread of COVID-19 and the effect of climatic factors in Algeria [ 18 ]. Fatih et al investigated the transmission of the virus in Algeria, Egypt, and South Africa [ 19 ]. Kadi et al studied the association between population density and the spread of COVID-19 in Algerian cities [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian Modeling of COVID-19 to Classify the Infection and Death Rates in a Specific Duration: The Case of Algerian Provinces

Aouissi

Hamimes

Ababsa

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

COVID-19 causes acute respiratory illness in humans. The direct consequence of the spread of the virus is the need to find appropriate and effective solutions to reduce its spread. Similar to other countries, the pandemic has spread in Algeria, with noticeable variation in mortality and infection rates between regions. We aimed to estimate the proportion of people who died or became infected with SARS-CoV-2 in each provinces using a Bayesian approach. The estimation parameters were determined using a binomial distribution along with an a priori distribution, and the results had a high degree of accuracy. The Bayesian model was applied during the third wave (1 January–15 August 2021), in all Algerian’s provinces. For spatial analysis of duration, geographical maps were used. Our findings show that Tissemsilt, Ain Defla, Illizi, El Taref, and Ghardaia (Mean = 0.001) are the least affected provinces in terms of COVID-19 mortality. The results also indicate that Tizi Ouzou (Mean = 0.0694), Boumerdes (Mean = 0.0520), Annaba (Mean = 0.0483), Tipaza (Mean = 0.0524), and Tebessa (Mean = 0.0264) are more susceptible to infection, as they were ranked in terms of the level of corona infections among the 48 provinces of the country. Their susceptibility seems mainly due to the population density in these provinces. Additionally, it was observed that northeast Algeria, where the population is concentrated, has the highest infection rate. Factors affecting mortality due to COVID-19 do not necessarily depend on the spread of the pandemic. The proposed Bayesian model resulted in being useful for monitoring the pandemic to estimate and compare the risks between provinces. This statistical inference can provide a reasonable basis for describing future pandemics in other world geographical areas.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Bayesian modeling also captures a priori information to rank provinces of a country based on the epidemic spread and death rates [ 18 , 19 , 20 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Modeling of COVID-19 to Classify the Infection and Death Rates in a Specific Duration: The Case of Algerian Provinces

Aouissi

Hamimes

Ababsa

et al. 2022

IJERPH

Self Cite

View full text Add to dashboard Cite

show abstract

“…So, there is need of study of FTS forecasting for COVID-19 predictions. Some studies are already available in the literature to predict COVID-19 cases using FTS forecasting [19] , [20] . We can experiment with more hyperparameters of FTS to improve the forecasting results.…”

Section: Introductionmentioning

confidence: 99%

Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19

Kumar

Susan

2021

Applied Soft Computing

View full text Add to dashboard Cite

Major hyperparameters which affect fuzzy time series (FTS) forecasting are the number of partitions, length of partition intervals in the universe of discourse, and the fuzzy order. There are very few studies which have considered an integrated solution to optimize all the hyperparameters. In this paper, we strive to achieve optimum values of all three hyperparameters for fuzzy time series forecasting of the COVID-19 pandemic using the Particle Swarm Optimization (PSO) algorithm. We specifically propose two techniques, namely nested FTS-PSO and exhaustive search FTS-PSO for determining the optimal interval length, as an augmentation to the FTS-PSO model that optimizes the interval length and the fuzzy order. Nested PSO has two PSO loops: (i) the inner PSO optimizes the combination of fuzzy order and boundaries of intervals for a given number of partitions defined by the outer loop, and the resultant cost is fed back to the outer PSO; (ii) the outer PSO optimizes the number of partitions to reduce the cost while meeting the defined constraint. Exhaustive search FTS-PSO also has two loops where the inner loop is similar to nested FTS-PSO while the outer loop iterates over a pre-defined search space of number of partitions. We analyze the effectiveness of the two approaches by comparing with ARIMA, FbProphet, and the state-of-the-art FTS and FTS-PSO models. We adopt COVID-19 highly affected 10 countries worldwide to perform forecasting of coronavirus confirmed cases. We consider two phases of COVID-19 spread, one from the year 2020 and another from 2021. Our study provides an analytical aspect of the COVID-19 pandemic, and aims to achieve optimal number and length of intervals along with fuzzy order for FTS forecasting of COVID-19. The results prove that the exhaustive search FTS-PSO outperformed all the methods whereas nested FTS-PSO performed moderately well.

show abstract

“…For malaria modeling in Afghanistan, Anwar et al (2016) have also dealt with ARIMA models to make prediction of the trend and incidence. Recently Fatih et al (2020) applied ARIMA models to explain and predict the number of COVID-19 cases in South Africa, Algeria and Egypt. For more details on ARIMA models, the interested reader is refereed to Brockwell and Davis (2002), Box and Jenkins (1970), Box and Pierce (1970) and references therein.…”

Section: Introductionmentioning

confidence: 99%

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

Diop¹,

Ndao²,

Seck³

et al. 2021

ajas

View full text Add to dashboard Cite

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

show abstract

A note on Covid-19 Statistics, Strange trend and Forecasting of Total Cases in the most Infected African Countries: An ARIMA and Fuzzy Time Series Approaches

Cited by 4 publications

References 19 publications

Bayesian Modeling of COVID-19 to Classify the Infection and Death Rates in a Specific Duration: The Case of Algerian Provinces

Bayesian Modeling of COVID-19 to Classify the Infection and Death Rates in a Specific Duration: The Case of Algerian Provinces

Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

Contact Info

Product

Resources

About