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 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.

scholarly journals Indo-Pak Comparison and Forecasting of Covid-19 Epidemic: A Secondary Data Analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 184-188
Author(s):  
Osama Ajaz ◽  
Muhammad Irfan ◽  
Ayesha Siddiqa ◽  
Muhammad Amjad
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Current Trend ◽  
Secondary Data ◽  
World Health ◽  
Data Set ◽  
Related Case ◽  
The World ◽  
Novel Coronavirus ◽  
Health Organization

Background: The world has historically faced and recovered from many pandemics. The most recent global pandemic that the whole world is facing is Novel Coronavirus – Covid-19. The objective of current study is to compare and forecast COVID-19 trends for Pakistan and India. Methods: The data set for this research is obtained from the World Health Organization (WHO) online repository (https://covid19.who.int/). The time period we have considered since the first corona related case and death were observed in both countries. This research paper analyzes corona related cases and deaths in Pakistan and India till 28th February 2021, a total of 578,797 cases in Pakistan and 11,096,731 cases in India has been confirmed including 128,37 and 1,570,51 deaths respectively. The Auto-Regressive Integrated Moving Average (ARIMA) model is used to forecast the variables cumulative cases and deaths. It is simple to use and more predictive than any other regression model. Results: Based on the current trend, the forecast graph reveals that the number of cumulative corona cases could reach 999,767 in Pakistan and 16,481,122 in India up to 31st December 2021. Conclusion: This research found that corona related cumulative cases and deaths are on the rise in both countries. The pandemic situation in India is worse than in Pakistan nevertheless both countries are at high risk. There is a sudden increasing pattern in the number of corona related cases in both countries. Both governments must impose effective policies to control this pandemic.

scholarly journals ARIMA modelling of predicting COVID-19 infections

2020 ◽  
Author(s):  
W.Regis Anne ◽  
S.Carolin Jeeva
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
World Health ◽  
Director General ◽  
Global Pandemic ◽  
Novel Coronavirus ◽  
Health Organization ◽  
Short Term Prediction

AbstractThe World Health Organization (WHO) Director-General, Dr. Tedros Adhanom Ghebreyesus on March 11, 2020 declared the novel coronavirus (COVID-19) outbreak a global pandemic [4] the reason being the number of cases outside China increased 13-fold and the number of countries with cases increased threefold. In this paper a time series model to predict short-term prediction of the transmission of the exponentially growing COVID-19 time series is modelled and studied. Auto Regressive Integrated Moving Average (ARIMA) model prediction is performed on the number of cumulative cases over a time period and is validated over Akaike information criterion (AIC) statistics.

scholarly journals Testing the Accuracy of the ARIMA Models in Forecasting the Spreading of COVID-19 and the Associated Mortality Rate

Medicina ◽  
2020 ◽  
Vol 56 (11) ◽  
pp. 566
Author(s):  
Ovidiu-Dumitru Ilie ◽  
Alin Ciobica ◽  
Bogdan Doroftei
Keyword(s):  
Moving Average ◽  
Developed Countries ◽  
World Health ◽  
Percentage Error ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
The World ◽  
The Developed Countries ◽  
Health Organization ◽  
June July

Background and objectives: The current pandemic of SARS-CoV-2 has not only changed, but also affected the lives of tens of millions of people around the world in these last nine to ten months. Although the situation is stable to some extent within the developed countries, approximately one million have already died as a consequence of the unique symptomatology that these people displayed. Thus, the need to develop an effective strategy for monitoring, restricting, but especially for predicting the evolution of COVID-19 is urgent, especially in middle-class countries such as Romania. Material and Methods: Therefore, autoregressive integrated moving average (ARIMA) models have been created, aiming to predict the epidemiological course of COVID-19 in Romania by using two statistical software (STATGRAPHICS Centurion (v.18.1.13) and IBM SPSS (v.20.0.0)). To increase the accuracy, we collected data between the established interval (1 March, 31 August) from the official website of the Romanian Government and the World Health Organization. Results: Several ARIMA models were generated from which ARIMA (1,2,1), ARIMA (3,2,2), ARIMA (3,1,3), ARIMA (3,2,2), ARIMA (3,1,3), ARIMA (2,2,2) and ARIMA (1,2,1) were considered the best models. For this, we took into account the lowest value of mean absolute percentage error (MAPE) for March, April, May, June, July, and August (MAPEMarch = 9.3225, MAPEApril = 0.975287, MAPEMay = 0.227675, MAPEJune = 0.161412, MAPEJuly = 0.243285, MAPEAugust = 0.163873, MAPEMarch – August = 2.29175 for STATGRAPHICS Centurion (v.18.1.13) and MAPEMarch = 57.505, MAPEApril = 1.152, MAPEMay = 0.259, MAPEJune = 0.185, MAPEJuly = 0.307, MAPEAugust = 0.194, and MAPEMarch – August = 6.013 for IBM SPSS (v.20.0.0) respectively. Conclusions: This study demonstrates that ARIMA is a useful statistical model for making predictions and provides an idea of the epidemiological status of the country of interest.

scholarly journals ARIMA-based forecasting of the dynamics of confirmed Covid-19 cases for selected European countries

Equilibrium ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 181-204
Author(s):  
Tadeusz Kufel
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Essential Element ◽  
Value Added ◽  
World Health ◽  
Global Pandemic ◽  
The World ◽  
Auto Regressive ◽  
Health Organization ◽  
Virus Distribution

Research background: On 11 March 2020, the Covid-19 epidemic was identified by the World Health Organization (WHO) as a global pandemic. The rapid increase in the scale of the epidemic has led to the introduction of non-pharmaceutical countermeasures. Forecast of the Covid-19 prevalence is an essential element in the actions undertaken by authorities. Purpose of the article: The article aims to assess the usefulness of the Auto-regressive Integrated Moving Average (ARIMA) model for predicting the dynamics of Covid-19 incidence at different stages of the epidemic, from the first phase of growth, to the maximum daily incidence, until the phase of the epidemic's extinction. Methods: ARIMA(p,d,q) models are used to predict the dynamics of virus distribution in many diseases. Model estimates, forecasts, and the accuracy of forecasts are presented in this paper. Findings & Value added: Using the ARIMA(1,2,0) model for forecasting the dynamics of Covid-19 cases in each stage of the epidemic is a way of evaluating the implemented non-pharmaceutical countermeasures on the dynamics of the epidemic.

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.

ARIMA model for forecasting COVID-19 Death cases in India (Preprint)

2020 ◽  
Author(s):  
Jeya Sutha M
Keyword(s):  
Arima Model ◽  
Public Health Emergency ◽  
World Health ◽  
The People ◽  
The World ◽  
The Status ◽  
The Government ◽  
Health Organization ◽  
Spread Of Infection ◽  
Death Cases

UNSTRUCTURED COVID-19, the disease caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a highly contagious disease. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of July 25, 2020; 15,947,292 laboratory-confirmed and 642,814 deaths have been reported globally. India has reported 1,338,928 confirmed cases and 31,412 deaths till date. This paper presents different aspects of COVID-19, visualization of the spread of infection and presents the ARIMA model for forecasting the status of COVID-19 death cases in the next 50 days in order to take necessary precaution by the Government to save the people.

Emerging and Reemerging Infectious Diseases - Past, Present and Future

2007 ◽  
Vol 2 (2) ◽  
pp. 66-70 ◽  
Author(s):  
Yoshifumi Takeda ◽  
Keyword(s):  
Infectious Diseases ◽  
Emerging Infectious Diseases ◽  
World Health ◽  
Control Law ◽  
Global Response ◽  
The Us ◽  
The World ◽  
Us Government ◽  
Health Organization ◽  
Global Threat

The global threat of new infectious diseases first became widely recognized in the 1990s. The US government published a report on emerging and reemerging infectious diseases followed by the World Health Organization (WHO), which adopted the slogan "Emerging Infectious Diseases: Global Alert, Global Response" in 1997. Typical examples of the more than 30 infectious diseases emerging since 1970s are HIV/AIDS, Vibrio cholerae O139 infection, enterohemorrhagic Escherichia coli infection, severe acute respiratory syndrome (SARS), and avian influenza. The New Infectious Diseases Control Law enacted in Japan in 1999 was to control these emerging infectious diseases and the already existing ones.

scholarly journals Counterfeit drugs as a global threat to health

2011 ◽  
Vol 64 (5-6) ◽  
pp. 285-290 ◽  
Author(s):  
Svetlana Golocorbin-Kon ◽  
Momir Mikov
Keyword(s):  
World Health Organization ◽  
Health Professionals ◽  
World Health ◽  
Sources Of Information ◽  
Counterfeit Drugs ◽  
Counterfeit Medicines ◽  
The World ◽  
Health Organization ◽  
Global Threat ◽  
Many Sources

According to the World Health Organization, counterfeit medicines are medicines that are mislabeled deliberately and fraudulently regarding their identity and/or source. All kinds of medicines have been counterfeited, both branded and generic ones. Counterfeit medicines may include products containing correct or wrong ingredients; without active or with insufficiently or over-active ingredients, or with fake packaging. Many sources of information have been explored, including reports from the national medicine regulatory authorities, pharmaceutical companies and literature data. Since the time counterfeit drugs first appeared, they have become more sophisticated and more difficult to be detected. The World Health Organization estimate is that up to 1% of medicines available in the developed world are likely to be counterfeit. This figure rises to 10% globally, although in some developing countries it is 50%. The World Health Organization estimate is that 50% of medicines available via the internet are counterfeit. The knowledge about counterfeit drugs should be used to educate students of pharmacy and medicine, health professionals and patients. The most important players in campaign against counterfeit medicines are health professionals. Pharmacists and doctors should stay vigilant and report suspicious products, and consider counterfeits as a possible cause of adverse reactions or therapeutic failure. Patients should inform their pharmacists and doctors if they suspect any irregularity concerning their medication, if they experience side effects or a decrease in beneficial effect. The crucial step in the prevention of counterfeit medicines is to get supplied from reliable sources, i.e. licensed pharmacies.

scholarly journals Forecasting road traffic accident deaths in India using seasonal autoregressive integrated moving average model

2018 ◽  
Vol 5 (9) ◽  
pp. 3962
Author(s):  
Manikandan M. ◽  
Vishnu Prasad R. ◽  
Amit Kumar Mishra ◽  
Rajesh Kumar Konduru ◽  
Newtonraj A.
Keyword(s):  
Traffic Accidents ◽  
Time Series Data ◽  
Road Traffic ◽  
Moving Average ◽  
Arima Model ◽  
World Health ◽  
Series Data ◽  
Road Traffic Accidents ◽  
Increasing Trend ◽  
Health Organization

Background: As per World Health Organization (WHO) report 1.24 million people die each year as a result of road traffic accidents (RTA) globally. A vast majority of 20-50 million people suffer from non-fatal injuries, many of them ultimately end in disability. Forecasting RTA deaths could help in planning the intervention at the right time in an effective way.Methods: An attempt was made to forecast the RTA deaths in India with seasonal auto regressive integrated moving average (SARIMA) model. ARIMA model is one of the common methods which are used for forecasting variables as the method is very easy and requires only long time series data. The method of selection of appropriate ARIMA model has been explained in detail. Month wise RTA deaths for previous years data was collected from Govt. of India website. Data for 12 years (2001 to 2012) was extracted and appropriate ARIMA model was selected. Using the validated ARIMA model the RTA deaths are forecasted for 8 years (2013-2020).Results: The appropriate SARIMA (1,0,0) (2,1,0) 12 model was selected based on minimal AIC and BIC values. The forecasted RTA deaths show increasing trend overtime.Conclusions: There is an increasing trend in the forecasted numbers of road traffic accidental deaths and it also shows seasonality of RTA deaths with more number of accidents during the month of April and May in every years. It is recommended that the policy makers and transport authority should pay more attention to road traffic accidents and plan some effective intervention to reduce the burden of RTA deaths.

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