Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections

Ala’raj, Maher; Majdalawieh, Munir; Nizamuddin, Nishara

doi:10.1016/j.idm.2020.11.007

Cited by 64 publications

(51 citation statements)

References 42 publications

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“…Ala’raj et al [ 2 ] utilized a dynamic hybrid model based on a modified susceptible–exposed–infected–recovered–dead (SEIRD) model with ARIMA corrections of the residuals. They provided long-term forecasts for infected, recovered, and deceased people using a US COVID-19 dataset, and their model had a remarkable ability to make accurate predictions.…”

Section: Related Literaturementioning

confidence: 99%

Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

Perone

2021

Eur J Health Econ

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The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic’s second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box–Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020–October 13, 2020 were extracted from the website of the Italian Ministry of Health (www.salute.gov.it). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities’ decision-making, especially in the short-term.

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Section: Related Literaturementioning

confidence: 99%

Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

Perone

2021

Eur J Health Econ

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“…Thus any dynamic process can be forecasted following the steps: 1) integrate the time series from your time-stamped data, 2) forecast the time series using some Koopman-based method, 3) optimize dictionaries, hyper-parameters. Hence, some interesting and data-intensive research fields like cybersecurity (for instance, web credibility [13], risky behaviors in private cyber-activity [11], models of cyberattack detection [2], and dynamic monitoring systems) or infectious models (like [1], [3], and [12]) are fields of application of the prediction tools presented in this paper.…”

Section: Discussionmentioning

confidence: 99%

On Aggregation and Prediction of Cybersecurity Incident Reports

et al. 2021

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The study of cybersecurity incidents is an active research field. The purpose of this work is to determine accurate measures of cybersecurity incidents. An effective method to aggregate cybersecurity incident reports is defined to set these measures. As a result we are able to make predictions and, therefore, to deploy security policies. Forecasting time-series of those cybersecurity aggregates is performed based on Koopman's method and Dynamic Mode Decomposition algorithm. Both techniques have shown to be accurate for a wide variety of dynamical systems ranging from fluid dynamics to social sciences. We have performed some experiments on public databases. We show that the measure of the risk trend can be effectively forecasted. INDEX TERMSCybersecurity, extended dynamic mode decomposition, Koopman operator, time series forecasting, threat prediction. MSC[2010]: 00A72 (General methods of simulation), 93A30 (Mathematical modeling), 65F20 (Overdetermined systems and pseudo-inverses), 68T10 (Pattern recognition), 37N40 (Dynamical systems in optimization and economics), 62-07 (Data analysis).

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“…That apart, incorporating variables denoting proportions of the older generation may also reveal interesting findings. Thus far, numerous studies have been done to model the transmission of COVID-19 in various parts of the world [32][33][34], as the pandemic continues to wreak havoc on human lives. Future research may build upon our findings through the extraction of principal components to be applied in machine learning models, for making predictions of the spread of COVID-19.…”

Section: Plos Onementioning

confidence: 99%

Multivariate visualization of the global COVID-19 pandemic: A comparison of 161 countries

et al. 2021

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Background The aim of the study was to visualize the global spread of the COVID-19 pandemic over the first 90 days, through the principal component analysis approach of dimensionality reduction. Methods This study used data from the Global COVID-19 Index provided by PEMANDU Associates. The sample, representing 161 countries, comprised the number of confirmed cases, deaths, stringency indices, population density and GNI per capita (USD). Correlation matrices were computed to reveal the association between the variables at three time points: day-30, day-60 and day-90. Three separate principal component analyses were computed for similar time points, and several standardized plots were produced. Results Confirmed cases and deaths due to COVID-19 showed positive but weak correlation with stringency and GNI per capita. Through principal component analysis, the first two principal components captured close to 70% of the variance of the data. The first component can be viewed as the severity of the COVID-19 surge in countries, whereas the second component largely corresponded to population density, followed by GNI per capita of countries. Multivariate visualization of the two dominating principal components provided a standardized comparison of the situation in the161 countries, performed on day-30, day-60 and day-90 since the first confirmed cases in countries worldwide. Conclusion Visualization of the global spread of COVID-19 showed the unequal severity of the pandemic across continents and over time. Distinct patterns in clusters of countries, which separated many European countries from those in Africa, suggested a contrast in terms of stringency measures and wealth of a country. The African continent appeared to fare better in terms of the COVID-19 pandemic and the burden of mortality in the first 90 days. A noticeable worsening trend was observed in several countries in the same relative time frame of the disease’s first 90 days, especially in the United States of America.

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Modeling and forecasting of COVID-19 using a hybrid dynamic model based on SEIRD with ARIMA corrections

Cited by 64 publications

References 42 publications

Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

On Aggregation and Prediction of Cybersecurity Incident Reports

Multivariate visualization of the global COVID-19 pandemic: A comparison of 161 countries

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