Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

scholarly journals Forecasting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models

2020 ◽  
Author(s):  
Tania Dehesh ◽  
H.A. Mardani-Fard ◽  
Paria Dehesh
Keyword(s):  
South Korea ◽  
Prediction Models ◽  
Moving Average ◽  
Arima Model ◽  
Mainland China ◽  
Johns Hopkins University ◽  
Auto Regressive ◽  
Novel Coronavirus ◽  
Global Threat ◽  
Near Future

AbstractBackgroundThe epidemic of a novel coronavirus illness (COVID-19) becomes as a global threat. The aim of this study is first to find the best prediction models for daily confirmed cases in countries with high number of confirmed cases in the world and second to predict confirmed cases with these models in order to have more readiness in healthcare systems.MethodsThis study was conducted based on daily confirmed cases of COVID-19 that were collected from the official website of Johns Hopkins University from January 22th, 2020 to March 1th, 2020. Auto Regressive Integrated Moving Average (ARIMA) model was used to predict the trend of confirmed cases. Stata version 12 were used.ResultsParameters used for ARIMA were (2,1,0) for Mainland China, ARIMA (2,2,2) for Italy, ARIMA(1,0,0) for South Korea, ARIMA (2,3,0) for Iran, and ARIMA(3,1,0) for Thailand. Mainland China and Thailand had almost a stable trend. The trend of South Korea was decreasing and will become stable in near future. Iran and Italy had unstable trends.ConclusionsMainland China and Thailand were successful in haltering COVID-19 epidemic. Investigating their protocol in this control like quarantine should be in the first line of other countries’ program

scholarly journals Predicting the Pandemic COVID-19 Using ARIMA Model

2020 ◽  
Vol 36 (4) ◽  
Author(s):  
Nguyen Quoc Duong ◽  
Le Phuong Thao ◽  
Dinh Thi Nhu Quynh ◽  
Le Thanh Binh ◽  
Cao Thi Ai Loan ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
World Health ◽  
Percentage Error ◽  
The World ◽  
Validation Set ◽  
Short Term Forecasting ◽  
Health Organization ◽  
Global Threat

Coronavirus disease 2019 (COVID-19) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. The main objective of this study is to apply AutoRegressive Integrated Moving Average (ARIMA) model with the objective of monitoring and short-term forecasting the total confirmed new cases per day all over the world. The data are extracted from daily report of World Health Organization from 21st January 2020 to 16th March 2020. Akaike’s Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. To assess the validity of the proposed model, the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) between the observed and fitted of COVID-19 total confirmed new cases was calculated. Finally, we applied “forecast” package in R software and the fitted ARIMA model to predict the infections of COVID-19. We found that the ARIMA (1, 2, 1) model was able to describe and predict the epidemiological trend of the disease of COVID-19. The MAPE and RMSE for the training set and validation set respectively, which we found was reasonable for use in the forecast. Furthermore, the model also provided forecast total confirmed new cases for the following days. ARIMA model applied to COVID-19 confirmed cases data are an important tool for COVID-19 surveillance all over the world. This study shows that accurate forecasting of the COVID-19 trend is possible using an ARIMA model. Unless strict infection management and control are taken, our findings indicate the potential of COVID-19 to cause greater outbreak all over the world.

scholarly journals Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

2020 ◽  
Author(s):  
Lutfi Bayyurt ◽  
Burcu Bayyurt
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Health Crisis ◽  
Public Health Crisis ◽  
New Public Health ◽  
New Public ◽  
Science Community ◽  
Auto Regressive ◽  
Global Threat ◽  
And Control

ABSTRACTAfter the outbreak of severe acute respiratory syndrome (SARS-2002/2003) and middle east respiratory syndrome (MERS-2012/2014) in the world, new public health crisis, called new coronavirus disease (COVID-19), started in China in December 2019 and has spread all over countries. COVID-19 coronavirus has been global threat of the disease and infected humans rapidly. Control of the pandemi is urgently essential, and science community have continued to research treatment agents. Support therapy and intensive care units in hospitals are also efective to overcome of COVID-19. Statistic forecasting models could aid to healthcare system in preventation of COVID-19. This study aimed to compose of forecasting model that could be practical to predict the spread of COVID-19 in Italy, Spain and Turkey. For this purpose, we performed Auto Regressive Integrated Moving Average (ARIMA) model on the European Centre for Disease Prevention and Control COVID-19 data to predict the number of cases and deaths in COVID-19. According to the our results, while number of cases in Italy and Spain is expected to decrease as of July, in Turkey is expected to decline as of September. The number of deaths in Italy and Spain is expected to be the lowest in July. In Turkey, this number is expected to reach the highest in July. In addition, it is thought that if studies in which the sensitivity and validity of this method are tested with more cases, they will contribute to researchers working in this field.

scholarly journals Brief Analysis of the ARIMA model on the COVID-19 in Italy

2020 ◽  
Author(s):  
Guorong Ding ◽  
Xinru Li ◽  
Fan Jiao ◽  
Yang Shen
Keyword(s):  
Inflection Point ◽  
Moving Average ◽  
Arima Model ◽  
Personal Hygiene ◽  
Cumulative Number ◽  
Newly Diagnosed ◽  
Analysis Model ◽  
Auto Regressive ◽  
Global Threat ◽  
The Right

AbstractCoronavirus disease 2019 (COVID-19) has been considered as a global threat infectious disease, and various mathematical models are being used to conduct multiple studies to analyze and predict the evolution of this epidemic. We statistically analyze the epidemic data from February 24 to March 30, 2020 in Italy, and proposes a simple time series analysis model based on the Auto Regressive Integrated Moving Average (ARIMA). The cumulative number of newly diagnosed and newly diagnosed patients in Italy is preprocessed and can be used to predict the spread of the Italian COVID-19 epidemic. The conclusion is that an inflection point is expected to occur in Italy in early April, and some reliable points are put forward for the inflection point of the epidemic: strengthen regional isolation and protection, do a good job of personal hygiene, and quickly treat the team leaders existing medical forces. It is hoped that the “City Closure” decree issued by the Italian government will go in the right direction, because this is the only way to curb the epidemic.

scholarly journals Forecasting the Severity of COVID-19 Pandemic Amidst the Emerging SARS-CoV-2 Variants: Adoption of ARIMA Model

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Cai Li ◽  
Agyemang Kwasi Sampene ◽  
Fredrick Oteng Agyeman ◽  
Brenya Robert ◽  
Abraham Lincoln Ayisi
Keyword(s):  
South Africa ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Model Specification ◽  
World Health ◽  
To Come ◽  
Health Organization ◽  
The Uk ◽  
Death Cases

Currently, the global report of COVID-19 cases is around 110 million, and more than 2.43 million related death cases as of February 18, 2021. Viruses continuously change through mutation; hence, different virus of SARS-CoV-2 has been reported globally. The United Kingdom (UK), South Africa, Brazil, and Nigeria are the countries from which these emerged variants have been notified and now spreading globally. Therefore, these countries have been selected as a research sample for the present study. The datasets analyzed in this study spanned from March 1, 2020, to January 31, 2021, and were obtained from the World Health Organization website. The study used the Autoregressive Integrated Moving Average (ARIMA) model to forecast coronavirus incidence in the UK, South Africa, Brazil, and Nigeria. ARIMA models with minimum Akaike Information Criterion Correction (AICc) and statistically significant parameters were chosen as the best models in this research. Accordingly, for the new confirmed cases, ARIMA (3,1,14), ARIMA (0,1,11), ARIMA (1,0,10), and ARIMA (1,1,14) models were chosen for the UK, South Africa, Brazil, and Nigeria, respectively. Also, the model specification for the confirmed death cases was ARIMA (3,0,4), ARIMA (0,1,4), ARIMA (1,0,7), and ARIMA (Brown); models were selected for the UK, South Africa, Brazil, and Nigeria, respectively. The results of the ARIMA model forecasting showed that if the required measures are not taken by the respective governments and health practitioners in the days to come, the magnitude of the coronavirus pandemic is expected to increase in the study’s selected countries.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Growth, instability and forecast of marine products export from India

2016 ◽  
Vol 63 (4) ◽  
Author(s):  
Apu Das ◽  
Nalini Ranjan Kumar ◽  
Prathvi Rani
Keyword(s):  
Moving Average ◽  
Export Performance ◽  
Arima Model ◽  
South East Asia ◽  
Arima Models ◽  
Policy Makers ◽  
Increasing Trend ◽  
Marine Products ◽  
Auto Regressive ◽  
Instability Indices

This paper analysed growth and instability in export of marine products from India with an attempt to forecast the total export quantity of marine products from the country. The compound growth rates and instability indices of marine products export from India were estimated for major importing countries viz., Japan, USA, European Union, South-east Asia and Middle East; as more than 80% of the marine products export from India destines to these markets. The study revealed high compound growth rate and low instability in case of selected countries. The study also revealed that India’s marine products export concentrated mainly to those countries, which were falling in less desirable or least desirable category which has affected export performance of the country. Forecast of India’s marine products export was done by fitting univariate Auto Regressive Integrated Moving Average (ARIMA) models. ARIMA (1, 1, 0) was found suitable for modelling marine products export from India. The results of ARIMA model indicated increasing trend in export of Indian marine products. This calls for serious attention by policy makers to identify competitive and stable market destinations for marine products export which could help in harnessing the potential of marine products export from India.

Data Analysis and Forecasting of COVID-19 Pandemic in Kuwait Based on Daily Observation and Basic Reproduction Number Dynamics

2021 ◽  
Author(s):  
Kayode Oshinubi ◽  
◽  
Fahimah Al-Awadhi ◽  
Mustapha Rachdi ◽  
Jacques Demongeot ◽  
...  
Keyword(s):  
Basic Reproduction Number ◽  
Reproduction Number ◽  
Moving Average ◽  
Arima Model ◽  
Exponential Model ◽  
Spreading Rate ◽  
Support Vector ◽  
Global Threat ◽  
Log Linear ◽  
Second Wave

Coronavirus (COVID-19) has continued to be a global threat to public health. When the coronavirus pandemic began early in 2020, experts wondered if there would be waves of cases, a pattern seen in other virus pandemics. The overall pattern so far has been one of increasing cases of COVID-19 followed by a decline, and we observed a second wave of increased cases and yet we are still exploring this pandemic. Hence, updating the prediction model for the new cases of COVID-19 for different waves is essential to monitor the spreading of the virus and control the disease. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modelling approach for predicting new cases of coronavirus (COVID-19). We propose a deterministic method to predict the basic reproduction number Ro of first and second wave transition of COVID-19 cases in Kuwait and also to forecast the daily new cases and deaths of the pandemic in the country. Forecasting has been done using ARIMA model, Exponential smoothing model, Holt’s method, Prophet forecasting model and machine learning models like log-linear, polynomial and support vector regressions. The results presented aligned with other methods used to predict Ro in first and second waves and the forecasting clearly shows the trend of the pandemic in Kuwait. The deterministic prediction of Ro is a good forecasting tool available during the exponential phase of the contagion, which shows an increasing trend during the beginning of the first and second waves of the pandemic in Kuwait. The results show that support vector regression has achieved the best performance for prediction while a simple exponential model without trend gives good optimal results for forecasting of Kuwait COVID-19 data.

Predicting Daily Confirmed COVID-19 Cases in India

2021 ◽  
pp. 34-51
Author(s):  
Sudip Singh
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Family Welfare ◽  
Mean Square ◽  
Univariate Time Series ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

scholarly journals Analysis and Forecast of Mining Fatalities in Cherat Coal Field, Pakistan

2021 ◽  
Vol 11 (4) ◽  
pp. 22-26
Author(s):  
Sajid Khan ◽  
Kausar Sultan Shah ◽  
Naeem Abbas ◽  
Abdur Rahman ◽  
Naseer Muhammad Khan
Keyword(s):  
Management System ◽  
Safety Management ◽  
Moving Average ◽  
Arima Model ◽  
Coal Field ◽  
Safety Management System ◽  
Term Forecast ◽  
Auto Regressive ◽  
Mineral Production

Mineral exploitation contributes to the economic growth of developing countries. Managing mineral production brought a more disturbing environment linked to workers' causalities due to scarcities in the safety management system. One of the barriers to attaining an adequate safety management system is the unavailability of future information relating to accidents causing fatalities. Policymakers always try to manage the safety system after each accident. Therefore, a precise forecast of the number of workers fatalities can provide significant observation to strengthen the safety management system. This study involves forecasting the number of mining workers fatalities in Cherat coal mines by using Auto-Regressive Integrating Moving Average Method (ARIMA) model. Workers' fatalities information was collected over the period of 1994 to 2018 from Mine Workers Federation, Inspectorate of Mines and Minerals and company records to evaluate the long-term forecast. Various diagnostic tests were used to obtain an optimistic model. The results show that ARIMA (0, 1, 2) was the most appropriate model for workers fatalities. Based on this model, casualties from 2019 to 2025 have been forecasted. The results suggest that policymakers should take systematic consideration by evaluating possible risks associated with an increased number of fatalities and develop a safe and effective working platform.

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