Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Zhao, Wei; Sun, Yunpeng; Liu, Ying; Guan, Weimin

doi:10.3389/fpubh.2022.923978

Cited by 5 publications

(4 citation statements)

References 33 publications

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“…Using combination of different models has proven to be an effective way of improving emprical predictions in various applications. One can refer to the Hybrid modelling of other types of infectious disease [25][26][27][28][29] and sunspot monitoring 30 . The idea using an additive combination of AR model (or more generally, ARIMA model) with LSTM has recently appeared in 31,32 for time series forecasting with applications in gas and oil well production and sunspot monitoring.…”

Section: Related Workmentioning

confidence: 99%

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

Zhang

Tang

Guo

2022

Preprint

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The Coronavirus Disease 2019 (COVID-19) has posed a severe threat to global human health and economic. It is an urgent task to build reliable data-driven prediction models for Covid 19 cases to improve public policy making. However, COVID-19 data shows special transmission characteristics such as significant fluctuations and non-stationarity, which may be difficult to be captured by a single predictive model and poses grand challenges in effective forecasting. In this paper, we proposed a novel Hybrid data-driven model combining Autoregressive model (AR) and long short-term memory neural networks (LSTM). It can be viewed as a new neural network model and the contribution of AR and LSTM is auto tuned in the training procedure. We conduct extensive numerical experiments on data collected from 8 counties of California that display various trends. The numerical results show the Hybrid model' advantages over AR and LSTM by its predictive powers. We show that the Hybrid model achieved 4.195% MAPE, outperformed the AR 5.629% and LSTM 5.070% on average, and provide a discussion on interpretability.

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Section: Related Workmentioning

confidence: 99%

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

Zhang

Tang

Guo

2022

Preprint

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“…The limitation of the SEIR model lies in its initial inability to ascertain virus-related probabilities. Typically, these probabilities are derived from extensive data analysis, which is slow and prone to local optimum errors 30 . Currently, the advantages of fast training speed and strong global optimization ability of GA are shown.…”

Section: Methodsmentioning

confidence: 99%

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

Wang,

Wu,

Luo

et al. 2024

Sci Rep

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The global spread of COVID-19 has profoundly affected health and economies, highlighting the need for precise epidemic trend predictions for effective interventions. In this study, we used infectious disease models to simulate and predict the trajectory of COVID-19. An SEIR (susceptible, exposed, infected, removed) model was established using Wuhan data to reflect the pandemic. We then trained a genetic algorithm-based SEIR (GA-SEIR) model using data from a specific U.S. region and focused on individual susceptibility and infection dynamics. By integrating socio-psychological factors, we achieved a significant enhancement to the GA-SEIR model, leading to the development of an optimized version. This refined GA-SEIR model significantly improved our ability to simulate the spread and control of the epidemic and to effectively track trends. Remarkably, it successfully predicted the resurgence of COVID-19 in mainland China in April 2023, demonstrating its robustness and reliability. The refined GA-SEIR model provides crucial insights for public health authorities, enabling them to design and implement proactive strategies for outbreak containment and mitigation. Its substantial contributions to epidemic modelling and public health planning are invaluable, particularly in managing and controlling respiratory infectious diseases such as COVID-19.

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“…In addition, data driven logistic-SEIR-type hybrid models for COVID-19 were developed in Zhao et al. ( 2022 ); Ala’raj et al. ( 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Hybrid discrete-time-continuous-time models and a SARS CoV-2 mystery: Sub-Saharan Africa’s low SARS CoV-2 disease burden

Siewe

Yakubu

2023

J. Math. Biol.

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Worldwide, the recent SARS-CoV-2 virus has infected more than 670 million people and killed nearly 67.0 million. In Africa, the number of confirmed COVID-19 cases was approximately 12.7 million as of January 11, 2023, that is about 2% of the infections around the world. Many theories and modeling techniques have been used to explain this lower-than-expected number of reported COVID-19 cases in Africa relative to the high disease burden in most developed countries. We noted that most epidemiological mathematical models are formulated in continuous-time interval, and taking Cameroon in Sub-Saharan Africa, and New York State in the USA as case studies, in this paper we developed parameterized hybrid discrete-time-continuous-time models of COVID-19 in Cameroon and New York State. We used these hybrid models to study the lower-than-expected COVID-19 infections in developing countries. We then used error analysis to show that a time scale for a data-driven mathematical model should match that of the actual data reporting.

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Prediction of COVID-19 Data Using Hybrid Modeling Approaches

Cited by 5 publications

References 33 publications

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

Integrating socio-psychological factors in the SEIR model optimized by a genetic algorithm for COVID-19 trend analysis

Hybrid discrete-time-continuous-time models and a SARS CoV-2 mystery: Sub-Saharan Africa’s low SARS CoV-2 disease burden

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