scholarly journals Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins Modeling Procedure

2020 ◽  
Author(s):  
Rauf Rauf Ibrahim ◽  
Hannah Oluwakemi Oladipo
Keyword(s):  
Task Force ◽  
Moving Average ◽  
Arima Model ◽  
Time Frame ◽  
R Software ◽  
Test Parameter ◽  
Autoregressive Integrated Moving Average ◽  
Stationarity Test ◽  
Parameter Test ◽  
Insight Into

AbstractObjectiveThis study is focused on the analysis of the spread of Covid-19 in Nigeria, applying statistical models and available data from the NCDC. We present an insight into the spread of Covid-19 in Nigeria in order to establish a suitable prediction model, which can be applied as a decision-supportive tool for assigning health interventions and mitigating the spread of the Covid-19 infection.MethodologyDaily spread data from February 27 to April 26, 2020, were collected to construct the autoregressive integrated moving average (ARIMA) model using the R software. Stability analysis and stationarity test, parameter test, and model diagnostic were also carried out. Finally, the fitting, selection and prediction accuracy of the ARIMA model was evaluated using the AICc model selection criteria.ResultsThe ARIMA (1,1,0) model was finally selected among ARIMA models based upon the parameter test and Box–Ljung test. A ten-day forecast was also made from the model, which shows a steep upward trend of the spread of the COVID-19 in Nigeria within the selected time frame.ConclusionFederal Government of Nigeria through the presidential task force can apply the forecasted trend of much more spread to make more informed decisions on the additional measures in place to curb the spread of the virus. Application of the model can also assist in studying the effectiveness of the lockdown on the on the spread of Covid-19 in Nigeria.

scholarly journals A Time Series Model to Forecast Covid-19 Infection rate in Nigeria Using Box-Jenkins Method

2021 ◽  
Vol 4 (1) ◽  
pp. 83-98
Author(s):  
CA Ortese ◽  
TG Ieren ◽  
AJ Tamber
Keyword(s):  
Task Force ◽  
Moving Average ◽  
Unit Root Test ◽  
Future Trend ◽  
Suitable Model ◽  
Test Parameter ◽  
Autoregressive Integrated Moving Average ◽  
Global Pandemic ◽  
Augmented Dickey Fuller ◽  
One Step

Coronavirus declared as a global pandemic by WHO has emerged as the most aggressive disease negatively affecting more than 90% countries of the world. Nigeria, one of the most populated countries in Africa is not an exception. This study focuses on analyzing the intrinsic patterns in the COVID-19 spread in Nigeria using the Box-Jenkins procedure. Data of daily confirmed cases of COVID-19 in Nigeria was retrieved from Nigeria Centre for Disease Control (NCDC) official website from February 27, 2020 to October 31, 2020 to identify the series components, estimate parameters, develop an appropriate stochastic predictive model and use the model to forecast future trend of the deadly virus. The Autoregressive Integrated Moving Average (ARIMA) of order (0,1,1) was identified as the most suitable model based on the analysis of the autocorrelation (ACF), partial autocorrelation functions (PACF) and Akaike Information Correction (AICc) value. R software version 4.0.3 was used to analyze the trend which moothen the series by using 8-point moving average to extract the irregular component as wellas differencing the series one step further to obtain a stationary series. We performed the Augmented Dickey-Fuller Unit root test, parameter estimation and Ljung-Box test to check the proposed model’s conformity to the stationary univariate process. A 85 – day (1st Oct., 2020 – 24th Jan., 2020)forecast shows a gradual decline in the successive number of confirmed cases of infection indicating the effectiveness of the intervention strategies employed by the Task Force to contain the virus. The concerned authorities can apply the forecasted trend to make further informed decisions on the measures to be put in place to reduce diffusion of the deadly virus into the country.

scholarly journals Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Lulu Wang ◽  
Chen Liang ◽  
Wei Wu ◽  
Shengwen Wu ◽  
Jinghua Yang ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Autoregressive Integrated Moving Average ◽  
Incidence Data ◽  
Health Delivery ◽  
Morbidity Data ◽  
Parameter Test ◽  
Model Recognition ◽  
Epidemiological Characteristics

Objective. This study aimed to investigate the specific epidemiological characteristics and epidemic situation of brucellosis in Jinzhou City of China so as to establish a suitable prediction model potentially applied as a decision-supportive tool for reasonably assigning health interventions and health delivery. Methods. Monthly morbidity data from 2004 to 2013 were selected to construct the autoregressive integrated moving average (ARIMA) model using SPSS 13.0 software. Moreover, stability analysis and sequence tranquilization, model recognition, parameter test, and model diagnostic were also carried out. Finally, the fitting and prediction accuracy of the ARIMA model were evaluated using the monthly morbidity data in 2014. Results. A total of 3078 cases affected by brucellosis were reported from January 1998 to December 2015 in Jinzhou City. The incidence of brucellosis had shown a fluctuating growth gradually. Moreover, the ARIMA(1,1,1)(0,1,1)12 model was finally selected among quite a few plausible ARIMA models based upon the parameter test, correlation analysis, and Box–Ljung test. Notably, the incidence from 2005 to 2014 forecasted using this ARIMA model fitted well with the actual incidence data. Notably, the actual morbidity in 2014 fell within the scope of 95% confidence limit of values predicted by the ARIMA(1,1,1)(0,1,1)12 model, with the absolute error between the predicted and the actual values in 2014 ranging from 0.02 to 0.74. Meanwhile, the MAPE was 19.83%. Conclusion. It is suitable to predict the incidence of brucellosis in Jinzhou City of China using the ARIMA(1,1,1)(0,1,1)12 model.

scholarly journals A statistical study of COVID-19 pandemic in Egypt

2021 ◽  
Vol 54 (1) ◽  
pp. 233-244
Author(s):  
Taha Radwan
Keyword(s):  
Linear Relationship ◽  
Statistical Study ◽  
Moving Average ◽  
Arima Model ◽  
Decision Makers ◽  
Accurate Data ◽  
Autoregressive Integrated Moving Average ◽  
The World ◽  
The Future

Abstract The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA) model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

scholarly journals Seawater intrusion vulnerability evaluation and prediction: a case study of Qeshm Island, Iran

2020 ◽  
Author(s):  
Amin Zeynolabedin ◽  
Reza Ghiassi ◽  
Moharram Dolatshahi Pirooz
Keyword(s):  
Seawater Intrusion ◽  
Moving Average ◽  
Arima Model ◽  
Chloride Ion ◽  
Absolute Error ◽  
Ion Concentration ◽  
Qeshm Island ◽  
Autoregressive Integrated Moving Average ◽  
Dangerous Condition

Abstract Seawater intrusion is one of the most serious issues to threaten coastal aquifers. Tourian aquifer, which is selected as the case study, is located in Qeshm Island, Persian Gulf. In this study, first the vulnerability of the region to seawater intrusion is assessed using chloride ion concentration value, then by using the autoregressive integrated moving average (ARIMA) model, the vulnerability of the region is predicted for 14 wells in 2018. The results show that the Tourian aquifer experiences moderate vulnerability and the area affected by seawater intrusion is wide and is in danger of expanding. It is also found that 0.95 km2 of the region is in a state of high vulnerability with Cl concentration being in a dangerous condition. The prediction model shows that ARIMA (2,1,1) is the best model with mean absolute error of 13.3 mg/L and Nash–Sutcliffe value of 0.81. For fitted and predicted data, mean square error is evaluated as 235.3 and 264.3, respectively. The prediction results show that vulnerability is increasing through the years.

scholarly journals Rainfall Forecast of Merauke Using Autoregressive Integrated Moving Average Model

2018 ◽  
Vol 73 ◽  
pp. 12010 ◽  
Author(s):  
Yenni P. Pasaribu ◽  
Hariani Fitrianti ◽  
Dessy Rizki Suryani
Keyword(s):  
Global Climate ◽  
Human Life ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
Crop Failure ◽  
Maximum Rainfall ◽  
Autoregressive Integrated Moving Average ◽  
Agriculture Sector ◽  
Rainfall Forecast

Climate is an important element for human life, one of them is to agriculture sector. Global climate change leads to increased frequency and extreme climatic intensity such as storms, floods, and droughts. Rainfall is climate factor that causes the failure of harvest in Merauke. Therefore, rainfall forecast information is very useful in anticipating the occurrence of extreme events that can lead to crop failure. The purpose of this research is to model rainfall using autoregressive integrated moving average (ARIMA) model. The ARIMA model can be used to predict future events using a set of past data, including predicting rainfall. This research was conducted by collecting secondary data from Agency of Meteorology, Climatology, and Geophysics (BMKG) from 2005 until 2017, then the data was analyzed using R.3.4.2. software. The analysis result showed that ARIMA model (2.0,2) as the right model to predict rainfall in Merauke. The result of forecasting based on ARIMA model (2.0,2) for one period ahead is 179 mm of average rainfall, 46 mm of minimum rainfall, and 295 mm of maximum rainfall. Thus it can be concluded that the intensity of rainfall in Merauke has decreased and there was a seasonal shift from the previous period.

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