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Chen W, Luo H, Li J, Chi J. Long-term trend prediction of pandemic combining the compartmental and deep learning models. Sci Rep 2024; 14:21068. [PMID: 39256475 PMCID: PMC11387753 DOI: 10.1038/s41598-024-72005-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 09/02/2024] [Indexed: 09/12/2024] Open
Abstract
Predicting the spread trends of a pandemic is crucial, but long-term prediction remains challenging due to complex relationships among disease spread stages and preventive policies. To address this issue, we propose a novel approach that utilizes data augmentation techniques, compartmental model features, and disease preventive policies. We also use a breakpoint detection method to divide the disease spread into distinct stages and weight these stages using a self-attention mechanism to account for variations in virus transmission capabilities. Finally, we introduce a long-term spread trend prediction model for infectious diseases based on a bi-directional gated recurrent unit network. To evaluate the effectiveness of our model, we conducted experiments using public datasets, focusing on the prediction of COVID-19 cases in four countries over a period of 210 days. Experiments shown that the Adjust-R2 index of our model exceeds 0.9914, outperforming existing models. Furthermore, our model reduces the mean absolute error by 0.85-4.52% compared to other models. Our combined approach of using both the compartmental and deep learning models provides valuable insights into the dynamics of disease spread.
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Affiliation(s)
- Wanghu Chen
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China.
| | - Heng Luo
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
| | - Jing Li
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
| | - Jiacheng Chi
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China
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Wu M, Lin S, Xiao C, Xiao X, Xu S, Yu S. The emotion prediction of college students with attention LSTM during the COVID19 epidemic. Sci Rep 2023; 13:22825. [PMID: 38129509 PMCID: PMC10739690 DOI: 10.1038/s41598-023-50322-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/18/2023] [Indexed: 12/23/2023] Open
Abstract
During the COVID19 pandemic, there is a pronounced collective mental health issue among college students. Forecasting the trend of emotional changes in on-campus students is crucial to effectively address this issue. This study proposes an Attention-LSTM neural network model that performs deep learning on key input sequence information, so as to predict the distribution of emotional states in college students. By testing 60 consecutive days of emotional data, the model successfully predicts students' emotional distribution, triggers and resolution strategies, with an accuracy rate of no less than 99%. Compared with models such as ARIMA, SARIMA and VAR, this model shows significant advantages in accuracy, operational efficiency, and data collection requirements. The integration of deep learning technology with student management in this study offers a novel approach to address emotional issues among students under exceptional circumstances.
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Affiliation(s)
- Mengwei Wu
- College of Mechanical and Intelligent Manufacturing, Fujian Chuanzheng Communications College, Fuzhou, 350007, China
| | - Shaodan Lin
- College of Mechanical and Intelligent Manufacturing, Fujian Chuanzheng Communications College, Fuzhou, 350007, China.
| | - Chenhan Xiao
- College of Mechanical and Intelligent Manufacturing, Fujian Chuanzheng Communications College, Fuzhou, 350007, China
| | - Xiulin Xiao
- College of Mechanical and Intelligent Manufacturing, Fujian Chuanzheng Communications College, Fuzhou, 350007, China
| | - Siwei Xu
- College of Mechanical and Intelligent Manufacturing, Fujian Chuanzheng Communications College, Fuzhou, 350007, China
| | - Shuhan Yu
- College of Mechanical and Intelligent Manufacturing, Fujian Chuanzheng Communications College, Fuzhou, 350007, China
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Ojha VP, Yarahmadian S, Bobo RH. The long-run analysis of COVID-19 dynamic using random evolution, peak detection and time series. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT : RESEARCH JOURNAL 2023; 37:1-19. [PMID: 37362846 PMCID: PMC10165298 DOI: 10.1007/s00477-023-02455-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 06/28/2023]
Abstract
It is now almost three years that COVID-19 has been the cause of misery for millions of people around the world. Many countries are in process of vaccination. Due to the social complexity of the problem, the future of decisions is not clear. As such, there is a need for the mathematical modeling to predict the long-run behavior of the COVID-19 dynamic for the decision-making with regard to the result of the pandemic on the economy, health, and others. In this paper, we have studied the short and long-run behavior of COVID-19. In a novel way, random evolution (Trichotomous and Dichotomous Markov Noise) is used to model and analyze the long-run behavior of the pandemic in different phases of the pandemic in different countries. On the given conditions, the random evolution model can help us establish the long-run asymptotic behaviour of the pandemic. This allows us to consider different phases of the pandemic as well as the effect of vaccination and other measures taken. The simplicity of the model makes it a practical tool for decision-making based on the long-run behavior of the pandemic. As such, we have established a criterion for the comparison of different regions and countries in different phases. In this regard, we have used real pandemic data from different countries to validate our results.
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Affiliation(s)
- Vaghawan Prasad Ojha
- Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS 39762 USA
- IKebana Solutions LLC, Tokyo, Japan
| | - Shantia Yarahmadian
- Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS 39762 USA
| | - Richard Hunt Bobo
- Department of Mathematics and Statistics, Mississippi State University, Mississippi State, MS 39762 USA
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Qureshi M, Daniyal M, Tawiah K. Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4864920. [PMID: 36406332 PMCID: PMC9668471 DOI: 10.1155/2022/4864920] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/15/2022] [Accepted: 10/02/2022] [Indexed: 09/08/2024]
Abstract
COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.
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Affiliation(s)
- Moiz Qureshi
- Department of Statistics, Shaheed Benazir Bhutto University, Shaheed Benazirabad, Pakistan
| | - Muhammad Daniyal
- Department of Statistics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Kassim Tawiah
- Department of Mathematics and Statistics, University of Energy and Natural Resources, Sunyani, Ghana
- Department of Statistics and Actuarial Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
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SARIMA Model Forecasting Performance of the COVID-19 Daily Statistics in Thailand during the Omicron Variant Epidemic. Healthcare (Basel) 2022; 10:healthcare10071310. [PMID: 35885836 PMCID: PMC9324558 DOI: 10.3390/healthcare10071310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 11/17/2022] Open
Abstract
This study aims to identify and evaluate a robust and replicable public health predictive model that can be applied to the COVID-19 time-series dataset, and to compare the model performance after performing the 7-day, 14-day, and 28-day forecast interval. The seasonal autoregressive integrated moving average (SARIMA) model was developed and validated using a Thailand COVID-19 open dataset from 1 December 2021 to 30 April 2022, during the Omicron variant outbreak. The SARIMA model with a non-statistically significant p-value of the Ljung–Box test, the lowest AIC, and the lowest RMSE was selected from the top five candidates for model validation. The selected models were validated using the 7-day, 14-day, and 28-day forward-chaining cross validation method. The model performance matrix for each forecast interval was evaluated and compared. The case fatality rate and mortality rate of the COVID-19 Omicron variant were estimated from the best performance model. The study points out the importance of different time interval forecasting that affects the model performance.
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Branda F, Abenavoli L, Pierini M, Mazzoli S. Predicting the Spread of SARS-CoV-2 in Italian Regions: The Calabria Case Study, February 2020-March 2022. Diseases 2022; 10:38. [PMID: 35892732 PMCID: PMC9326619 DOI: 10.3390/diseases10030038] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/23/2022] [Accepted: 06/28/2022] [Indexed: 12/27/2022] Open
Abstract
Despite the stunning speed with which highly effective and safe vaccines have been developed, the emergence of new variants of SARS-CoV-2 causes high rates of (re)infection, a major impact on health care services, and a slowdown to the socio-economic system. For COVID-19, accurate and timely forecasts are therefore essential to provide the opportunity to rapidly identify risk areas affected by the pandemic, reallocate the use of health resources, design countermeasures, and increase public awareness. This paper presents the design and implementation of an approach based on autoregressive models to reliably forecast the spread of COVID-19 in Italian regions. Starting from the database of the Italian Civil Protection Department (DPC), the experimental evaluation was performed on real-world data collected from February 2020 to March 2022, focusing on Calabria, a region of Southern Italy. This evaluation shows that the proposed approach achieves a good predictive power for out-of-sample predictions within one week (R-squared > 0.9 at 1 day, R-squared > 0.7 at 7 days), although it decreases with increasing forecasted days (R-squared > 0.5 at 14 days).
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Affiliation(s)
- Francesco Branda
- Department of Computer Science, Modeling, Electronics and Systems Engineering (DIMES), University of Calabria, 87036 Rende, Italy;
| | - Ludovico Abenavoli
- Department of Health Sciences, University Magna Graecia, 88100 Catanzaro, Italy
| | - Massimo Pierini
- Guglielmo Marconi University, 00193 Rome, Italy;
- SITO WEB del Gruppo Epidemiologico, EpiData.it, 24121 Bergamo, Italy;
| | - Sandra Mazzoli
- SITO WEB del Gruppo Epidemiologico, EpiData.it, 24121 Bergamo, Italy;
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