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

Jo, HyeongChan and Kim, Juhyun and Huang, Tzu-Chen and Ni, Yu-Li (2022) condLSTM-Q: A novel deep learning model for predicting COVID-19 mortality in fine geographical scale. Quantitative Biology, 10 (2). pp. 125-138. ISSN 2095-4689. doi:10.15302/j-qb-021-0276.

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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.

Item Type:Article
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URLURL TypeDescription
Huang, Tzu-Chen0000-0002-8738-7695
Ni, Yu-Li0000-0003-1600-9854
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).
Funding AgencyGrant Number
Taipei Veterans General Hospital / National Yang-Ming University103-Y-A-003
Issue or Number:2
Record Number:CaltechAUTHORS:20220823-628672500
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:116434
Deposited By: Melissa Ray
Deposited On:29 Aug 2022 23:03
Last Modified:29 Aug 2022 23:04

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