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

2020 ◽  
Author(s):  
Belkacem Balah ◽  
Messaoud Djeddou
Keyword(s):  
Long Memory ◽  
Moving Average ◽  
Prediction Method ◽  
Decision Support Tool ◽  
Upward Trend ◽  
Support Tool ◽  
Moving Average Models ◽  
Auto Regressive ◽  
Health Database ◽  
Arfima Model

AbstractIn 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.BackgroundThe aim of this study is first to find the best prediction method among the two techniques used and type of memory, either short or long, of the model constructed for the daily confirmed cases in Algeria, then make forecasts of the confirmed cases in the fifteen next days.MethodsThis study was conducted based on daily new cases of COVID-19 that were collected from the official website of Algerian Ministry of Health from March 1, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARFIMA) model was used to predict the trend of confirmed cases. The evaluation of the fractional differentiation parameter (d) is carried out using OxMetrics 6 software.ResultsThe ARFIMA model (0, 0.431779, 0) build for Algeria, has a long memory and an upward trend over the next fifteen days and which coincides with the holy month of Ramadhan.ConclusionsThe forecasted results obtained by the proposed ARFIMA model can be used as a decision support tool to manage medical efforts and facilities against the COVID-19 pandemic crisis.

A Decision Support Tool (DST) for Inventory Management

2019 ◽  
Vol 11 (2) ◽  
pp. 27-47
Author(s):  
Okure Udo Obot ◽  
Uduak David George ◽  
Victoria Sunday Umana
Keyword(s):  
Decision Support ◽  
Inventory Management ◽  
Moving Average ◽  
Real Life ◽  
Decision Support Tool ◽  
Unit Price ◽  
Public Power ◽  
Support Tool ◽  
Eoq Model ◽  
Real Life Situation

Loss of customer goodwill is one of the greatest losses a business organization can incur. One reason for such a loss is stock outage. In an attempt to solve this problem, an overstock could result. Overstock comes with an increase in the holding and carrying cost. It is an attempt to solve these twin problems that an economic order quantity (EOQ) model was developed. Information on fifteen items comprised of 10 non-seasonal and 5 seasonal items was collected from a supermarket in Ikot Ekpene town, Nigeria. The information includes the quantity of daily sales, the unit price, the lead time and the number of times an item is ordered in a month. Based on this information, a simple moving average and y-trend method of forecasting were used to forecast the sales quantity for the following month for the non-seasonal and seasonal items. The forecast value was used to compute the EOQ for each of the items. Different scenarios were created to simulate the fuzzy logic EOQ after which the result of the conventional method, EOQ method, and fuzzy EOQ methods were obtained and compared. It was revealed that if the EOQ method is adopted, savings of 43% of holding and carrying cost would be made. From the scenarios of a fuzzy EOQ, a savings of 35.65% was recorded. It was however observed that in a real-life situation, the savings on a fuzzy EOQ is likely to be higher than that of an EOQ considering the incessant public power outages and the increase in transportation fares due to the high cost of fuel and the bad state of roads in Nigeria. To this end, a Decision Support Tool (DST) was developed to help the supermarket manage its inventory based on daily predictions. The DST incorporates a filter engine to take care of some emotional and cognitive incidences within the environment.

scholarly journals Excess cases of influenza and the coronavirus epidemic in Catalonia: a time-series analysis of primary-care electronic medical records covering over 6 million people

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e039369 ◽  
Author(s):  
Ermengol Coma Redon ◽  
Nuria Mora ◽  
Albert Prats-Uribe ◽  
Francesc Fina Avilés ◽  
Daniel Prieto-Alhambra ◽  
...  
Keyword(s):  
Public Health ◽  
Primary Care ◽  
Time Series ◽  
Moving Average ◽  
Epidemic Curve ◽  
Imported Case ◽  
Moving Average Models ◽  
Community Transmission ◽  
Auto Regressive ◽  
Primary Care Practices

ObjectivesThere is uncertainty about when the first cases of COVID-19 appeared in Spain. We aimed to determine whether influenza diagnoses masked early COVID-19 cases and estimate numbers of undetected COVID-19 cases.DesignTime-series study of influenza and COVID-19 cases, 2010–2020.SettingPrimary care, Catalonia, Spain.ParticipantsPeople registered in primary-care practices, covering >6 million people and >85% of the population.Main outcome measuresWeekly new cases of influenza and COVID-19 clinically diagnosed in primary care.AnalysesDaily counts of both cases were computed using the total cases recorded over the previous 7 days to avoid weekly effects. Epidemic curves were characterised for the 2010–2011 to 2019–2020 influenza seasons. Influenza seasons with a similar epidemic curve and peak case number as the 2019–2020 season were used to model expected case numbers with Auto Regressive Integrated Moving Average models, overall and stratified by age. Daily excess influenza cases were defined as the number of observed minus expected cases.ResultsFour influenza season curves (2011–2012, 2012–2013, 2013–2014 and 2016–2017) were used to estimate the number of expected cases of influenza in 2019–2020. Between 4 February 2020 and 20 March 2020, 8017 (95% CI: 1841 to 14 718) excess influenza cases were identified. This excess was highest in the 15–64 age group.ConclusionsCOVID-19 cases may have been present in the Catalan population when the first imported case was reported on 25 February 2020. COVID-19 carriers may have been misclassified as influenza diagnoses in primary care, boosting community transmission before public health measures were taken. The use of clinical codes could misrepresent the true occurrence of the disease. Serological or PCR testing should be used to confirm these findings. In future, this surveillance of excess influenza could help detect new outbreaks of COVID-19 or other influenza-like pathogens, to initiate early public health responses.

Aviation Accident Prediction Based on Auto-Regressive Integrating Moving Average Method

2013 ◽  
Vol 739 ◽  
pp. 754-758
Author(s):  
Xu Sheng Gan ◽  
Jing Shun Duanmu ◽  
Jian Guo Gao
Keyword(s):  
Decision Making ◽  
Average Method ◽  
Moving Average ◽  
Prediction Method ◽  
Aviation Safety ◽  
Good Prediction ◽  
Accident Prediction ◽  
Satisfactory Model ◽  
Calculation Process ◽  
Auto Regressive

In order to reinforce the efficiency and initiative of accident prevention for reducing accident loss, the surest way is to strengthen the prediction. In view of difficulties in aviation accident prediction, a prediction method based on Auto-Regressive Integrating Moving Average (ARIMA) is proposed. In this method, a trial model is first identified for an accident time series, and then the diagnosis is executed with the necessary adjustments. The calculation process including identification, estimation and diagnosis is repeated until obtain a satisfactory model. The example shows that, ARIMA has a good prediction for aviation accident and provides an important technique support for decision-making of aviation safety.

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