RETRACTED ARTICLE: Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML

Han, Tao; Gois, Francisco Nauber Bernardo; Oliveira, Ramsés; Prates, Luan Rocha; Porto, Magda Moura de Almeida

doi:10.1007/s00500-020-05503-5

Cited by 21 publications

(11 citation statements)

References 64 publications

(37 reference statements)

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“…Yu et al ( 2021 ) provided a detailed review on recurrent neural networks and LSTM models. More contributions in this field include Yu et al (2029) for assessing deep learning-based prediction performance of COVID-19 artificial intelligence-based system and Han et al ( 2021 ) for modeling the progression of COVID-19 using Kalman filter method and automated machine learning techniques. In this paper, the adaptive moment estimation optimizer is applied.…”

Section: Methodsmentioning

confidence: 99%

Forecasting COVID-19 infections in the Arabian Gulf region

Khedhiri

2021

Model. Earth Syst. Environ.

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Section: Methodsmentioning

confidence: 99%

Forecasting COVID-19 infections in the Arabian Gulf region

Khedhiri

2021

Model. Earth Syst. Environ.

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“…These frameworks find solutions to neural architecture search (NAS), where they aim to find the optimal neural network, minimising a loss function. Han et al [31] used TPOT and H2O to forecast COVID-19 mortality data from Ceará. In their study, they found that TPOT outperforms regression models not automatically tuned, achieving a higher R 2 score.…”

Section: Related Workmentioning

confidence: 99%

Automated Machine Learning for COVID-19 Forecasting

2022

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When responding to a pandemic situation, policy makers rely on forecasts of the spread. In the context of the current COVID-19 pandemic, various sophisticated epidemic and machine learning models have been used for forecasting. These models, however, rely on carefully selected architectures and detailed data that is often only available for specific regions. Automated machine learning (AutoML) addresses these challenges by allowing to automatically create forecasting pipelines in a data-driven manner, resulting in high-quality predictions. In this paper we study the role of open data along with AutoML systems in acquiring high-performance forecasting models for COVID-19. Here, we adapted the AutoML framework auto-sklearn to the time series forecasting task and introduced two variants for multi-step ahead COVID-19 forecasting which we refer to as (a) multi-output and (b) repeated single output forecasting. We studied the usefulness of anonymized open mobility data sets (place visits, and the use of different transportation modes) in addition to open mortality data. We evaluated three drift adaptation strategies to deal with concept drifts in data by (i) refitting our models on part of the data, (ii) the full data, or (iii) retraining the models completely. We compared the performance of our AutoML methods in terms of RMSE with five baselines on two testing periods (over 2020 and 2021). Our results show that combining mobility features and mortality data improves forecasting accuracy. Furthermore, we show that when faced with concept drifts, our method refitted on recent data using place visits mobility features outperforms all other approaches for 22 of the 26 countries considered in our study.INDEX TERMS automated machine learning, time series forecasting, concept drift, COVID-19, mobility data

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“…The outbreak of the novel coronavirus disease (COVID-19) in early December 2019 in Wuhan, China, attracted many researchers to evaluate the dynamics of infectious COVID-19 virus using various mathematical models [ 1 – 17 ]. Mathematical compartmental models, such as SIR (Susceptible—Infectious—Recovered) [ 18 , 19 ], in epidemiology, are generally expressed by a system of ordinary differential equations (ODE). Recent studies on COVID-19 modelling includes using the basic SIR model [ 12 , 18 , 19 ] or its extension (modified) versions such as SEIR (Susceptible—Exposed—Infectious—Recovered) [ 7 , 10 , 11 , 19 – 21 ], SIRD (Susceptible—Infectious—Recovered—Dead) [ 1 – 4 , 16 , 17 , 22 ] and SEIRD (Susceptible—Exposed—Infected—Recovered—Dead) [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

An application of the ensemble Kalman filter in epidemiological modelling

Lal

Huang

2021

PLoS ONE

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Since the novel coronavirus (COVID-19) outbreak in China, and due to the open accessibility of COVID-19 data, several researchers and modellers revisited the classical epidemiological models to evaluate their practical applicability. While mathematical compartmental models can predict various contagious viruses’ dynamics, their efficiency depends on the model parameters. Recently, several parameter estimation methods have been proposed for different models. In this study, we evaluated the Ensemble Kalman filter’s performance (EnKF) in the estimation of time-varying model parameters with synthetic data and the real COVID-19 data of Hubei province, China. Contrary to the previous works, in the current study, the effect of damping factors on an augmented EnKF is studied. An augmented EnKF algorithm is provided, and we present how the filter performs in estimating models using uncertain observational (reported) data. Results obtained confirm that the augumented-EnKF approach can provide reliable model parameter estimates. Additionally, there was a good fit of profiles between model simulation and the reported COVID-19 data confirming the possibility of using the augmented-EnKF approach for reliable model parameter estimation.

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RETRACTED ARTICLE: Modeling the progression of COVID-19 deaths using Kalman Filter and AutoML

Cited by 21 publications

References 64 publications

Forecasting COVID-19 infections in the Arabian Gulf region

Forecasting COVID-19 infections in the Arabian Gulf region

Automated Machine Learning for COVID-19 Forecasting

An application of the ensemble Kalman filter in epidemiological modelling

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