scholarly journals An ARIMA Model to Forecast the Spread and the Final Size of COVID-2019 Epidemic in Italy

2020 ◽  
Author(s):  
Gaetano Perone
Keyword(s):  
Inflection Point ◽  
Social Impact ◽  
Moving Average ◽  
Arima Model ◽  
Epidemiological Data ◽  
List Type ◽  
Arima Models ◽  
Final Size ◽  
Autoregressive Integrated Moving Average ◽  
The World

AbstractCoronavirus disease (COVID-2019) is a severe ongoing novel pandemic that is spreading quickly across the world. Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day. In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4, 2020, by using the Italian epidemiological data at national and regional level. The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4, 2020. The main advantage of this model is that it is easy to manage and fit. Moreover, it may give a first understanding of the basic trends, by suggesting the hypothetic epidemic’s inflection point and final size.Highlights❖ARIMA models allow in an easy way to investigate COVID-2019 trends, which are nowadays of huge economic and social impact.❖These data may be used by the health authority to continuously monitor the epidemic and to better allocate the available resources.❖The results suggest that the epidemic spread inflection point, in term of cumulative cases, will be reached at the end of May.❖Further useful and more precise forecasting may be provided by updating these data or applying the model to other regions and countries.

scholarly journals Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Loshini Thiruchelvam ◽  
Sarat Chandra Dass ◽  
Vijanth Sagayan Asirvadam ◽  
Hanita Daud ◽  
Balvinder Singh Gill
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Epidemiological Data ◽  
The State ◽  
Spatial Effects ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Temperature Levels ◽  
Surrounding Areas

AbstractThe state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions’ dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.

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 Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

scholarly journals Using Autoregressive Integrated Moving Average (ARIMA) Technique to Forecast the Production of Kharif Cereals in Odisha (India)

2020 ◽  
pp. 104-113
Author(s):  
Abhiram Dash ◽  
A. Mangaraju ◽  
Pradeep Mishra ◽  
H. Nayak
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Percentage Error ◽  
Arima Models ◽  
Training Set ◽  
Autoregressive Integrated Moving Average ◽  
Future Production ◽  
Best Fit ◽  
Testing Set

Cereals are the most important kharif season crop in Odisha. The present study was carried out to forecast the production of kharif cereals in Odisha by using the forecast values of area and yield of kharif cereals obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1970-71 to 2010-11 are considered as training set data and used for model building and from 2011-12 to 2015-16 are considered as testing set data and used for cross-validation of the selected model on the basis of the absolute percentage error. The ARIMA models are fitted to the stationary data which may be the original data or the differenced data. The different ARIMA models are evaluated on the basis of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) at various lags. The possible ARIMA models are selected on the basis of significant coefficient of autoregressive and moving average components by using the training set data. The best fitted models are then selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under kharif cereals and yield of kharif cereals are ARIMA (1,1,0) without constant and ARIMA (0,1,2) without constant respectively which are successfully cross-validated with the testing set data. The respective best fit ARIMA model has been used to forecast the area and yield of kharif cereals for the years 2016-17, 2017-18 and 2018-19. The forecast values of area shows a decrease, whereas, the forecast values of yield shows an increase. The decrease in area might have been the result of limited availability of area for cereals due to shifting towards non-food grain crops. The forecast values of production of kharif cereals obtained from the forecast values of area and yield of kharif cereals shows an increase which is due to the increase in forecast values of yield. Since there is limited scope for area expansion, the future production of kharif cereals can only be increased by increasing the yield to achieve the goal of food security for the growing population.

scholarly journals Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

scholarly journals An Application of ARIMA Model to Forecast the Dynamics of COVID-19 Epidemic in India

2021 ◽  
pp. 097215092098865
Author(s):  
Rupinder Katoch ◽  
Arpit Sidhu
Keyword(s):  
Temporal Dynamics ◽  
Time Window ◽  
Moving Average ◽  
Arima Model ◽  
National Level ◽  
Epidemiological Data ◽  
Final Size ◽  
The Past ◽  
The Future ◽  
Near Future

The swiftly growing and overwhelming epidemic in India has intensified the question: What will the trend and magnitude of impact of the novel coronavirus disease 2019 (COVID-19) be in India in the near future? To answer the present question, the study requires ample historical data to make an accurate forecast of the blowout of expected confirmed cases. All at once, no prediction can be certain as the past seldom reiterates itself in the future likewise. Besides, forecasts are influenced by a number of factors like reliability of the data and psychological factors like perception and reaction of the people to the hazards arising from the epidemic. The present study presents a simple but powerful and objective, that is, autoregressive integrated moving average (ARIMA) approach, to analyse the temporal dynamics of the COVID-19 outbreak in India in the time window 30 January 2020 to 16 September 2020 and to predict the final size and trend of the epidemic over the period after 16 September 2020 with Indian epidemiological data at national and state levels. With the assumption that the data that have been used are reliable and that the future will continue to track the same outline as in the past, underlying forecasts based on ARIMA model suggest an unending increase in the number of confirmed COVID-19 cases in India in the near future. The present article suggests varying epidemic’s inflection point and final size for underlying states and for the mainland, India. The final size at national level is expected to reach 25,669,294 in the next 230 days, with infection point that can be expected to be projected only on 23 April 2021. The study has enormous potential to plan and make decisions to control the further spread of epidemic in India and provides objective forecasts for the confirmed cases of COVID-19 in the coming days corresponding to the respective COVID periods of the underlying regions.

scholarly journals Predicting the throughput of grain products at the multipurpose terminal at the Port of Cape Town

2016 ◽  
Vol 10 (1) ◽  
Author(s):  
Leila L. Goedhals-Gerber
Keyword(s):  
Supply Chains ◽  
Moving Average ◽  
Arima Model ◽  
Cape Town ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Upper Limits ◽  
Grain Products ◽  
And Performance ◽  
And Storage

Background: Ports provide vital links in the maritime supply chains on which the trading of countries depend, and their efficiency and performance can contribute largely to the international competitiveness of those countries. However, to achieve and maintain such a contribution, port operators need to understand their role in a national economy and the factors that underlie the efficiency of the intermodal link that ports constitute in international supply chains. One such factor is the capacity of specialised cargo terminals.Objectives: This article described a possible technique for forecasting the throughput of grain imports through the bulk grain terminal at the Port of Cape Town. It determined whether the capacity in the bulk grain terminal is sufficient to handle current and forecasted volumes of imported grains or whether the volumes justify expansion or upgrading of the bulk grain terminal in the Port of Cape Town.Method: The Box–Jenkins methodology for autoregressive integrated moving average (ARIMA) models was applied. An ARIMA model – 2 parameter, 1 difference – was selected to do the forecast.Results: The average tonnage of all grains imported through the Port of Cape Town that can be expected in a month is approximately 90 000 tons. The maximum tonnage of all grains imported through the Port of Cape Town that can be expected in a month is approximately 180 000 tons.Conclusion: The analyses show that the demand for imports of grain products at the multipurpose terminal in the Port of Cape Town is not growing substantially. The analyses also identify that the current upper limits of grain imports are within the existing handling and storage capacities of the bulk grain terminal.

scholarly journals Fascioliasis in buffaloes: A 5-year forecast analysis of the disease based on a 15-year survey in Brazil

2019 ◽  
Vol 28 (3) ◽  
pp. 410-415 ◽  
Author(s):  
Izanara Cristine Pritsch ◽  
Emanoelli Cristini Augustinhak Stanula ◽  
Alan dos Anjos ◽  
José Alberto Bertot ◽  
Marcelo Beltrão Molento
Keyword(s):  
High Frequency ◽  
Fasciola Hepatica ◽  
Moving Average ◽  
Arima Model ◽  
Weather Conditions ◽  
Economic Losses ◽  
Arima Models ◽  
Case Scenario ◽  
Autoregressive Integrated Moving Average ◽  
Southern States

Abstract In South America, fascioliasis caused by the trematode Fasciola hepatica is an anthropozoonosis disease associated with significant economic losses and poor animal welfare. The objective of this study was to determine the prevalence of F. hepatica in the liver of buffaloes slaughtered from 2003 to 2017 in Brazil, and to perform a forecast analysis of the disease for the next five years using the Autoregressive Integrated Moving Average (ARIMA) model. Data analysis revealed an incidence of 7,187 cases out of 226,561 individuals. The disease presented a considerable interannual variation (p<0.005). Fasciola hepatica was more prevalent in the southern states of Brazil; Paraná, Rio Grande do Sul, and Santa Catarina, presenting 11.9, 7.7, and 3.2% of infected livers, respectively. The high frequency of liver condemnation in Paraná was influenced by weather conditions. The ARIMA models calculated a constant trend of the disease, depicting an average of its future prevalence. The models also described a worse-case and a positive-case scenario, calculating the effects of intervention measurements. In reality, there is an urgent need for regular diagnostic in the animals (fecal and immune diagnose) and in the environment (intermediate host), in order to avoid the high rates of infection.

scholarly journals Forecasting daily confirmed COVID-19 cases in Algeria using ARIMA models

2020 ◽  
Author(s):  
Messis Abdelaziz ◽  
Adjebli Ahmed ◽  
Ayeche Riad ◽  
Ghidouche Abderrezak ◽  
Ait-Ali Djida
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Arima Models ◽  
Forecast Period ◽  
Autoregressive Integrated Moving Average ◽  
Hospital Reform ◽  
Estimated Parameters ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

ABSTRACTCoronavirus disease has become a worldwide threat affecting almost every country in the world. The aim of this study is to identify the COVID-19 cases (positive, recovery and death) in Algeria using the Double Exponential Smoothing Method and an Autoregressive Integrated Moving Average (ARIMA) model for forecasting the COVID-19 cases.The data for this study were obtained from March 21st, 2020 to November 26th, 2020. The daily Algerian COVID-19 confirmed cases were sourced from The Ministry of Health, Population and Hospital Reform of Algeria. Based on the results of PACF, ACF, and estimated parameters of the ARIMA model in the COVID-19 case in Algeria following the ARIMA model (0,1,1). Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria.

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