Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

Bayyurt, Lütfi; Bayyurt, Burcu

doi:10.1101/2020.04.17.20069237

Cited by 24 publications

(18 citation statements)

References 11 publications

(16 reference statements)

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“…Chintalapud et al (17) applied AIRMA model to forecast the confirmed infected and recovered COVID-19 cases in Italy after sixty days of lockdown. Bayyurt et al (18) has applied AIRIMA model to forecast COVID-19 cases and deaths in Italy, Spain and Turkey. Tandon et a (19) has developed an ARIMA model to forecast the COVID-19 cases in India.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modeling of the COVID-19 prevalence in Saudi Arabia

Elhassan

Gaafar

2020

Preprint

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The swift precautionary and preventive measures and regulations that were adopted by the Saudi authority has ameliorated the exponential escalation of the SARS-CoV-2 virus spread, decreased the fatality rate and critical cases of COVID-19. Understanding the trend of COVID-19 is crucial to establishing the appropriate precautionary measures to mitigate the epidemic spread. The aim of this paper was to modifying and enhancing the mathematical modeling to guide health authority and assist in an early assessment of the epidemic outbreak and can be utilised to monitor non-pharmaceutical interventions (NPIs). Both ARIMA model and Logistic growth model were developed to study the trend and to provide short and long-term forecasting of the prevalence of COVID-19 cases and dynamics. The data analyzed in this study covered the period between 2nd March and 21st June 2020. Two different scenarios were developed to predict the epidemic fluctuating trends and dynamics. The first scenario covered the period between 2nd March and 28th May when the first peak was observed and immediately declined. The analysis projected that the COVID-19 epidemic to reach a peak by 17th May with a total number of 58,534 infected cases and to end on the 4th August, if lockdown were not interrupted and folks followed the recommended personal and social safety guidelines. The second scenario was simulated because of the sudden sharp spike witnessed in the trend of the new confirmed cases on the last week of May and continue to escalate till the time of current writing-21st June. In the 2nd scenario, the analysis estimated the epidemic to peak on 15th June with a total number of 146,004 infected cases and to end on 29th September, 2020 with a final size of 209,607 (185,757 to 244,310) infected cases, assuming that the NPIs will be maintained while new normal life is resumed carefully. ARIMA and Logistic growth models showed excellent performance in projecting the epidemic prevalence, trends and dynamics at different phases. In conclusion, the analysis presented in this paper will assist policy-makers and health care authorities to evaluate the effect of the NPIs applied and to size the resources needed to manage different phases and cope with the final size of the epidemic estimates and to impose extra precautions.

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Section: Introductionmentioning

confidence: 99%

Mathematical modeling of the COVID-19 prevalence in Saudi Arabia

Elhassan

Gaafar

2020

Preprint

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“…Various types of time series forecast of the COVID-19 epidemic have also been made, using ARIMA models [ 4 , 19 , 20 ], exponential smoothing [ 21 ], or autoregression in cases; for example, the study by Johndron et al [ 22 ] postulates daily deaths as a lagged function of earlier new cases. Applications of phenomenological models to COVID-19 incidence forecasts include the studies in [ 23 , 24 ].…”

Section: Related Research and Research Gapsmentioning

confidence: 99%

Mid-Epidemic Forecasts of COVID-19 Cases and Deaths: A Bivariate Model Applied to the UK

Congdon

2021

Interdisciplinary Perspectives on Infectious Diseases

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Background. The evolution of the COVID-19 epidemic has been accompanied by efforts to provide comparable international data on new cases and deaths. There is also accumulating evidence on the epidemiological parameters underlying COVID-19. Hence, there is potential for epidemic models providing mid-term forecasts of the epidemic trajectory using such information. The effectiveness of lockdown or lockdown relaxation can also be assessed by modelling later epidemic stages, possibly using a multiphase epidemic model. Methods. Commonly applied methods to analyse epidemic trajectories or make forecasts include phenomenological growth models (e.g., the Richards family of densities) and variants of the susceptible-infected-recovered (SIR) compartment model. Here, we focus on a practical forecasting approach, applied to interim UK COVID data, using a bivariate Reynolds model (for cases and deaths), with implementation based on Bayesian inference. We show the utility of informative priors in developing and estimating the model and compare error densities (Poisson-gamma, Poisson-lognormal, and Poisson-log-Student) for overdispersed data on new cases and deaths. We use cross validation to assess medium-term forecasts. We also consider the longer-term postlockdown epidemic profile to assess epidemic containment, using a two-phase model. Results. Fit to interim mid-epidemic data show better fit to training data and better cross-validation performance for a Poisson-log-Student model. Estimation of longer-term epidemic data after lockdown relaxation, characterised by protracted slow downturn and then upturn in cases, casts doubt on effective containment. Conclusions. Many applications of phenomenological models have been to complete epidemics. However, evaluation of such models based simply on their fit to observed data may give only a partial picture, and cross validation against actual trends is also valuable. Similarly, it may be preferable to model incidence rather than cumulative data, although this raises questions about suitable error densities for modelling often erratic fluctuations. Hence, there may be utility in evaluating alternative error assumptions.

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“…Lutfi and Burcu [ 17 ] performed Auto-Regressive Integrated Moving Average (ARIMA) model on the European Centre for Disease Prevention and Control (ECDC) COVID-19 data to predict the number of confirmed cases and deaths of COVID-19. The limitation of this particular study is that a limited number of countries were considered.…”

Section: Related Workmentioning

confidence: 99%

ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound

Malki

Atlam

Ewis

et al. 2020

Neural Comput & Applic

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Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public’s concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios.

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Forecasting of COVID-19 Cases and Deaths Using ARIMA Models

Cited by 24 publications

References 11 publications

Mathematical modeling of the COVID-19 prevalence in Saudi Arabia

Mathematical modeling of the COVID-19 prevalence in Saudi Arabia

Mid-Epidemic Forecasts of COVID-19 Cases and Deaths: A Bivariate Model Applied to the UK

ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound

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