ARIMA modelling of predicting COVID-19 infections

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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

Computational and Mathematical Methods in Medicine ◽

10.1155/2022/3163854 ◽

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.

Download Full-text

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.

Download Full-text

To Predict Epidemic Situation: Application of COVID-19 Pneumonia Diffusion Data (Preprint)

10.2196/preprints.19518 ◽

2020 ◽

Author(s):

Zhenguo Wu

Keyword(s):

Current Situation ◽

Current Data ◽

World Health ◽

Director General ◽

Average Value ◽

Global Pandemic ◽

Current Outbreak ◽

Epidemic Development ◽

Novel Coronavirus ◽

Health Organization

BACKGROUND In December 2019, a number of cases of pneumonia with unknown causes were found in some hospitals in Wuhan, Hubei province, China. On 11 March 2020, the director-general of the world health organization (WHO), Tedros Adhanom Ghebreyesus, announced that based on the assessment, WHO believes that the current outbreak of COVID-19 can be called a global pandemic. By early April 2020, there were more than one million confirmed cases worldwide. OBJECTIVE To evaluate novel coronavirus pneumonia cases by establishing the mathematical model of the number of confirmed cases daily, and to assess the current situation and development of the epidemic situation, so as to provide a digital basis for decision-making. METHODS The number of newly confirmed covid-19 cases per day was taken as the research object, and the seven-day average value (M) and the sequential value (R) of M were calculated to study the occurrence and development of covid-19 epidemic through the analysis of charts and data. RESULTS M reflected the current situation of epidemic development; R reflected the current level of infection and the trend of epidemic development. CONCLUSIONS The current data can be used to evaluate the number of people who have been infected, and when R < 1, the peak of epidemic can be predicted.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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

Equilibrium ◽

10.24136/eq.2020.009 ◽

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.

Download Full-text

Progression and Trends in Virus from Influenza A to COVID-19: An Overview of Recent Studies

Viruses ◽

10.3390/v13061145 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1145

Author(s):

Hakimeh Baghaei Daemi ◽

Muhammad Fakhar-e-Alam Kulyar ◽

Xinlin He ◽

Chengfei Li ◽

Morteza Karimpour ◽

...

Keyword(s):

Influenza A ◽

H1n1 Virus ◽

World Health ◽

H1n1 Pandemic ◽

Global Pandemic ◽

First Case ◽

Influenza A H1n1 ◽

The World ◽

Novel Coronavirus ◽

Health Organization

Influenza is a highly known contagious viral infection that has been responsible for the death of many people in history with pandemics. These pandemics have been occurring every 10 to 30 years in the last century. The most recent global pandemic prior to COVID-19 was the 2009 influenza A (H1N1) pandemic. A decade ago, the H1N1 virus caused 12,500 deaths in just 19 months globally. Now, again, the world has been challenged with another pandemic. Since December 2019, the first case of a novel coronavirus (COVID-19) infection was detected in Wuhan. This infection has risen rapidly throughout the world; even the World Health Organization (WHO) announced COVID-19 as a worldwide emergency to ensure human health and public safety. This review article aims to discuss important issues relating to COVID-19, including clinical, epidemiological, and pathological features of COVID-19 and recent progress in diagnosis and treatment approaches for the COVID-19 infection. We also highlight key similarities and differences between COVID-19 and influenza A to ensure the theoretical and practical details of COVID-19.

Download Full-text

Comprehensive physiotherapy management in covid-19 – a narrative review

Scientia Medica ◽

10.15448/1980-6108.2020.1.38030 ◽

2020 ◽

Vol 30 (1) ◽

pp. 38030 ◽

Cited By ~ 1

Author(s):

Deivendran Kalirathinam ◽

Raj Guruchandran ◽

Prabhakar Subramani

Keyword(s):

Narrative Review ◽

World Health ◽

Evidence Based ◽

The United Kingdom ◽

Global Pandemic ◽

Related Quality ◽

Health Related ◽

Novel Coronavirus ◽

Health Organization ◽

The Uk

The 2019 novel coronavirus officially named as coronavirus disease 2019 (COVID-19) pandemic by the World Health Organization, has spread to more than 180 countries. The ongoing global pandemic of severe acute respiratory syndrome coronavirus, which causes COVID-19, spread to the United Kingdom (UK) in January 2020. Transmission within the UK was confirmed in February, leading to an epidemic with a rapid increase in cases in March. As on April 25- 2020, there have been 148,377 confirmed cases of COVID-19 in the UK and 20,319 people with confirmed infection have died. Survival of critically ill patients is frequently associated with significant functional impairment and reduced health-related quality of life. Early physiotherapy and community rehabilitation of COVID-19 patients has recently been identified as an essential therapeutic tool and has become a crucial evidence-based component in the management of these patients. This comprehensive narrative review aims to describe recent progress in the application of physiotherapy management in COVID 19 patients. Assessment and evidence- based treatment of these patients should include prevention, reduction of adverse consequences in immobilization, and long-term impairment sequelae. A variety of techniques and modalities of early physiotherapy in intensive care unit are suggested by clinical research. They should be applied according to the stage of the disease, comorbidities, and patient’s level of cooperation.

Download Full-text