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

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.

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Predicting the Pandemic COVID-19 Using ARIMA Model

VNU Journal of Science Mathematics - Physics ◽

10.25073/2588-1124/vnumap.4492 ◽

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.

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ARIMA modelling of predicting COVID-19 infections

10.1101/2020.04.18.20070631 ◽

2020 ◽

Cited By ~ 4

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.

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

Endocrine Metabolic & Immune Disorders - Drug Targets ◽

10.2174/1871530320666201029143122 ◽

2020 ◽

Vol 20 ◽

Cited By ~ 1

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.

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

10.2196/preprints.24107 ◽

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.

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

International Journal of Community Medicine and Public Health ◽

10.18203/2394-6040.ijcmph20183579 ◽

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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Modelling monthly influenza cases in Malaysia

PLoS ONE ◽

10.1371/journal.pone.0254137 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254137

Author(s):

Muhammad Adam Norrulashikin ◽

Fadhilah Yusof ◽

Nur Hanani Mohd Hanafiah ◽

Siti Mariam Norrulashikin

Keyword(s):

Hybrid Model ◽

Moving Average ◽

Arima Model ◽

World Health ◽

Healthcare Sector ◽

Support Vector ◽

Test Dataset ◽

Autoregressive Integrated Moving Average ◽

Health Organization ◽

Nonlinear Components

The increasing trend in the number new cases of influenza every year as reported by WHO is concerning, especially in Malaysia. To date, there is no local research under healthcare sector that implements the time series forecasting methods to predict future disease outbreak in Malaysia, specifically influenza. Addressing the problem could increase awareness of the disease and could help healthcare workers to be more prepared in preventing the widespread of the disease. This paper intends to perform a hybrid ARIMA-SVR approach in forecasting monthly influenza cases in Malaysia. Autoregressive Integrated Moving Average (ARIMA) model (using Box-Jenkins method) and Support Vector Regression (SVR) model were used to capture the linear and nonlinear components in the monthly influenza cases, respectively. It was forecasted that the performance of the hybrid model would improve. The data from World Health Organization (WHO) websites consisting of weekly Influenza Serology A cases in Malaysia from the year 2006 until 2019 have been used for this study. The data were recategorized into monthly data. The findings of the study showed that the monthly influenza cases could be efficiently forecasted using three comparator models as all models outperformed the benchmark model (Naïve model). However, SVR with linear kernel produced the lowest values of RMSE and MAE for the test dataset suggesting the best performance out of the other comparators. This suggested that SVR has the potential to produce more consistent results in forecasting future values when compared with ARIMA and the ARIMA-SVR hybrid model.

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Tuberculosis forecasting and temporal trends by sex and age in a high endemic city in northeastern Brazil: where were we before the Covid-19 pandemic?

BMC Infectious Diseases ◽

10.1186/s12879-021-06978-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Hamilton Leandro Pinto de Andrade ◽

Dulce Gomes ◽

Antônio Carlos Vieira Ramos ◽

Luiz Henrique Arroyo ◽

Marcelino Santos-Neto ◽

...

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Temporal Trends ◽

Northeastern Brazil ◽

World Health ◽

Age Group ◽

Health Organization ◽

The City ◽

Series Study

Abstract Background The aim of this study was to describe the temporal trend of tuberculosis cases according to sex and age group and evidence the level of disease before the Covid-19 pandemic in a TB high endemic city. Methods This was a time series study carried out in a city in northeast Brazil. The population was composed of cases of tuberculosis, excluding those with HIV-positive status, reported between the years 2002 and 2018. An exploratory analysis of the monthly rates of tuberculosis detection, smoothed according to sex and age group, was performed. Subsequently, the progression of the trend and prediction of the disease were also characterized according to these aspects. For the trends forecast, the seasonal autoregressive linear integrated moving average (ARIMA) model and the usual Box-Jenkins method were used to choose the most appropriate models. Results A total of 1620 cases of tuberculosis were reported, with an incidence of 49.7 cases per 100,000 inhabitants in men and 34.0 per 100,000 in women. Regarding the incidence for both sexes, there was a decreasing trend, which was similar for age. Evidence resulting from the application of the time series shows a decreasing trend in the years 2002–2018, with a trend of stability. Conclusions The study evidenced a decreasing trend in tuberculosis, even before the Covid-19 pandemic, for both sex and age; however, in a step really slow from that recommended by the World Health Organization. According to the results, the disease would have achieved a level of stability in the city next years, however it might have been aggravated by the pandemic. These findings are relevant to evidence the serious behavior and trends of TB in a high endemic scenario considering a context prior to the Covid-19 pandemic.

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

Pakistan Journal of Public Health ◽

10.32413/pjph.v11i3.829 ◽

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.

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COVID 19: Real-time Forecasts of confirmed cases, active cases, and health infrastructure requirements for India and its states using the ARIMA model

10.1101/2020.05.17.20104588 ◽

2020 ◽

Cited By ~ 1

Author(s):

Rishabh Tyagi ◽

Mahadev Bramhankar ◽

Mohit Pandey ◽

M Kishore

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Arima Model ◽

Public Health Care ◽

World Health ◽

Series Data ◽

Health Infrastructure ◽

Future Demands ◽

Health Organization

AbstractBackgroundCOVID-19 is an emerging infectious disease which has been declared a Pandemic by the World Health Organization (WHO) on 11th March 2020. The Indian public health care system is already overstretched, and this pandemic is making things even worse. That is why forecasting cases for India is necessary to meet the future demands of the health infrastructure caused due to COVID-19.ObjectiveOur study forecasts the confirmed and active cases for COVID-19 until July mid, using time series Autoregressive Integrated Moving Average (ARIMA) model. Additionally, we estimated the number of isolation beds, Intensive Care Unit (ICU) beds and ventilators required for the growing number of COVID-19 patients.MethodsWe used ARIMA model for forecasting confirmed and active cases till the 15th July. We used time-series data of COVID-19 cases in India from 14th March to 22nd May. We estimated the requirements for ICU beds as 10%, ventilators as 5% and isolation beds as 85% of the active cases forecasted using the ARIMA model.ResultsOur forecasts indicate that India will have an estimated 7,47,772 confirmed cases (95% CI: 493943, 1001601) and 296,472 active cases (95% CI:196820, 396125) by 15th July. While Maharashtra will be the most affected state, having the highest number of active and confirmed cases, Punjab is expected to have an estimated 115 active cases by 15th July. India needs to prepare 2,52,001 isolation beds (95% CI: 167297, 336706), 29,647 ICU beds (95% CI: 19682, 39612), and 14,824 ventilator beds (95% CI: 9841, 19806).ConclusionOur forecasts show an alarming situation for India, and Maharashtra in particular. The actual numbers can go higher than our estimated numbers as India has a limited testing facility and coverage.

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Tuberculosis Forecasting and Temporal Trends by Sex and Age in a High Endemic City in Northeast Brazil: Where Were we Before the Covid-19 Pandemic?

10.21203/rs.3.rs-693429/v1 ◽

2021 ◽

Author(s):

Hamilton Leandro Pinto de Andrade ◽

Dulce Gomes ◽

Antônio Carlos Vieira Ramos ◽

Luiz Henrique Arroyo ◽

Marcelino Santos Neto ◽

...

Keyword(s):

Time Series ◽

Moving Average ◽

Arima Model ◽

Temporal Trends ◽

World Health ◽

Hiv Positive ◽

Northeast Brazil ◽

Age Group ◽

Health Organization ◽

Series Study

Abstract Background: The aim of this study was to describe the temporal trend of tuberculosis cases according to sex and age group and evidence the level of disease before the Covid-19 pandemic in a city in northeast Brazil. Methods: This was a time series study carried out in a city in northeast Brazil. The population was composed of cases of tuberculosis, excluding those with HIV-positive status, reported between the years 2002 and 2018. An exploratory analysis of the monthly rates of tuberculosis detection, smoothed according to sex and age group, was performed. Subsequently, the progression of the trend and prediction of the disease were also characterized according to these aspects. For the trends forecast, the seasonal autoregressive linear integrated moving average (ARIMA) model and the usual Box-Jenkins method were used to choose the most appropriate models. Results: A total of 1,620 cases of tuberculosis were reported, with an incidence of 49.7 cases per 100,000 inhabitants in men and 34.0 per 100,000 in women. Regarding the incidence for both sexes, there was a decreasing trend, which was similar for age. Evidence resulting from the application of the time series shows a decreasing trend in the years 2002–2018, with a trend of stability. Conclusion: The study demonstrated a decreasing trend in tuberculosis, even before the Covid-19 pandemic, for both sex and age; however, in a step really slow that recommended by the World Health Organization. According to the results, the disease would have achieved a level of stability had it not been for the Covid-19 pandemic. The results are relevant to evidence the problem of TB that transcends its aspects prior to the Covid-19 pandemic.

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