Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries

Khoojine, Arash Sioofy; Shadabfar, Mahdi; Hosseini, Vahid Reza; Kordestani, Hadi

doi:10.3390/e23101267

Cited by 17 publications

(7 citation statements)

References 33 publications

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“…The data-driven models formulate the prediction of the COVID-19 cases primarily as a regression problem and exploit fully data-adaptive approaches to understand the functional relationship between COVID-19 cases with a set of observable variables. Data-driven models include classical statistical models such as Autoregressive models (AR) [6][7][8] and Support Vector Regression (SVR) [9][10][11] , and deep learning models [12][13][14][15][16][17][18] . In this paper, we will focus on data-driven models.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Zhang

Tang

Guo

2022

Preprint

View full text Add to dashboard Cite

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“…The data-driven models formulate the prediction of the COVID-19 cases primarily as a regression problem and exploit fully data-adaptive approaches to understand the functional relationship between COVID-19 cases with a set of observable variables. Data-driven models include classical statistical models such as Autoregressive models (AR) 6 – 8 , Support Vector Machine (SVM) 9 – 11 , and the deep learning models 12 – 18 . In this paper, we will focus on data-driven models.…”

Section: Introductionmentioning

confidence: 99%

An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM

Zhang

Tang

Guo

2023

Sci Rep

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The Coronavirus Disease 2019 (COVID-19) has had a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve public policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose a great challenge for effectively predicting COVID-19 cases. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two single composing models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE, outperforming the composing AR (5.629%) and LSTM (4.934%) alone on average. In country-level datasets, our hybrid model outperforms the widely-used predictive models such as AR, LSTM, Support Vector Machines, Gradient Boosting, and Random Forest, in predicting the COVID-19 cases in Japan, Canada, Brazil, Argentina, Singapore, Italy, and the United Kingdom. In addition to the predictive performance, we illustrate the interpretability of our proposed hybrid model using the estimated AR component, which is a key feature that is not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models for COVID-19 cases, which could have significant implications for public health policy making and control of the current COVID-19 and potential future pandemics.

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“…Since the HFMD incidence has prominent seasonal characteristics [14][15][16] , the seasonal autoregressive integrated moving average (SARIMA) model has been widely used in predicting seasonal infectious diseases for its efficient forecasting ability for periodic time series. Many works have been done using the SARIMA model to predict HFMD incidence [17][18][19][20] . In practice, the time series of HFMD often contain linear and nonlinear patterns.…”

Section: Introductionmentioning

confidence: 99%

Analysis of A SARIMA-XGBoost Model for Hand, Foot and Mouth Disease in Xinjiang, China

Man

Huang

Qin

et al. 2023

Preprint

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Hand, foot, and mouth disease (HFMD) is a common childhood illness. The incidence of HFMD has a pronounced seasonal tendency and is closely related to meteorological factors such as temperature, rainfall, and wind speed. A SARIMA model is used for seasonal trends, and an XGBoost algorithm is applied for the nonlinear effects of meteorological factors. In this paper, we propose a SARIMA-XGBoost combined model to improve the prediction accuracy of HFMD in 15 regions of Xinjiang, China. The geographical and temporal weight model is designed to analyze the influence of meteorological factors from temporal and spatial perspectives. The results show that the factors affecting the spread of HFMD vary among regions. Temperature and daylight significantly impact the transmission of the disease in most areas. Based on the verification experiment of forecasting, the SARIMA-XGBoost model is superior to other models in accuracy, especially in some areas with high incidence.

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Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries

Cited by 17 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

An interpretable hybrid predictive model of COVID-19 cases using autoregressive model and LSTM

Analysis of A SARIMA-XGBoost Model for Hand, Foot and Mouth Disease in Xinjiang, China

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