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AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods
Muhammad Usman Tariq, Shuhaida Binti Ismail
Received October 13, 2023  Accepted January 26, 2024  Published online March 28, 2024  
DOI: https://doi.org/10.24171/j.phrp.2023.0287    [Epub ahead of print]
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AbstractAbstract PDF
Objectives
The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making.
Methods
This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models.
Results
The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset.
Conclusion
This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.
Risk Assessment Program of Highly Pathogenic Avian Influenza with Deep Learning Algorithm
Hachung Yoon, Ah-Reum Jang, Chungsik Jung, Hunseok Ko, Kwang-Nyeong Lee, Eunesub Lee
Osong Public Health Res Perspect. 2020;11(4):239-244.   Published online August 31, 2020
DOI: https://doi.org/10.24171/j.phrp.2020.11.4.13
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  • 2 Web of Science
  • 2 Crossref
AbstractAbstract PDFSupplementary Material
Objectives

This study presents the development and validation of a risk assessment program of highly pathogenic avian influenza (HPAI). This program was developed by the Korean government (Animal and Plant Quarantine Agency) and a private corporation (Korea Telecom, KT), using a national database (Korean animal health integrated system, KAHIS).

Methods

Our risk assessment program was developed using the multilayer perceptron method using R Language. HPAI outbreaks on 544 poultry farms (307 with H5N6, and 237 with H5N8) that had available visit records of livestock-related vehicles amongst the 812 HPAI outbreaks that were confirmed between January 2014 and June 2017 were involved in this study.

Results

After 140,000 iterations without drop-out, a model with 3 hidden layers and 10 nodes per layer, were selected. The activation function of the model was hyperbolic tangent. Precision and recall of the test gave F1 measures of 0.41, 0.68 and 0.51, respectively, at validation. The predicted risk values were higher for the “outbreak” (average ± SD, 0.20 ± 0.31) than “non-outbreak” (0.18 ± 0.30) farms (p < 0.001).

Conclusion

The risk assessment model developed was employed during the epidemics of 2016/2017 (pilot version) and 2017/2018 (complementary version). This risk assessment model enhanced risk management activities by enabling preemptive control measures to prevent the spread of diseases.

Citations

Citations to this article as recorded by  
  • Big data-based risk assessment of poultry farms during the 2020/2021 highly pathogenic avian influenza epidemic in Korea
    Hachung Yoon, Ilseob Lee, Hyeonjeong Kang, Kyung-Sook Kim, Eunesub Lee, Mathilde Richard
    PLOS ONE.2022; 17(6): e0269311.     CrossRef
  • Artificial Intelligence Models for Zoonotic Pathogens: A Survey
    Nisha Pillai, Mahalingam Ramkumar, Bindu Nanduri
    Microorganisms.2022; 10(10): 1911.     CrossRef

PHRP : Osong Public Health and Research Perspectives