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Published June 2022 | v2
Journal Article Open

condLSTM-Q: A novel deep learning model for predicting COVID-19 mortality in fine geographical scale

Abstract

Background: Modern machine learning-based models have not been harnessed to their total capacity for disease trend predictions prior to the COVID-19 pandemic. This work is the first use of the conditional RNN model in predicting disease trends that we know of during development that complemented classical epidemiological approaches. Methods: We developed the long short-term memory networks with quantile output (condLSTM-Q) model for making quantile predictions on COVID-19 death tolls. Results: We verified that the condLSTM-Q was accurately predicting fine-scale, county-level daily deaths with a two-week window. The model's performance was robust and comparable to, if not slightly better than well-known, publicly available models. This provides unique opportunities for investigating trends within the states and interactions between counties along state borders. In addition, by analyzing the importance of the categorical data, one could learn which features are risk factors that affect the death trend and provide handles for officials to ameliorate the risks. Conclusion: The condLSTM-Q model performed robustly, provided fine-scale, county-level predictions of daily deaths with a two-week window. Given the scalability and generalizability of neural network models, this model could incorporate additional data sources with ease and could be further developed to generate other valuable predictions such as new cases or hospitalizations intuitively.

Additional Information

The authors thank Prof Yaser Abu-Mostafa, and the Teaching Assistants of CS156 in Caltech for organizing the COVID19 prediction initiative and for providing the data pipeline for parsing data sources. We thank Isaac Yen-Hao Chu, M.D. for reading the manuscript. Yu-Li Ni was supported by Taipei Veterans General Hospital Yang-Ming University Excellent Physician Scientists Cultivation Program (No.103-Y-A-003).

Copyright and License

© 2022 The Author (s). Published by Higher Education Press. This article is licensed by the CC By under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Additional details

Created:
September 19, 2023
Modified:
September 19, 2023