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

Quantitative Biology 2022年第2期10卷 125-138页

作者： hyeongchan jo Juhyun Kim Tzu-Chen Huang Yu-Li Ni Division of Biology and Biological Engineering CaltechPasadena CA 91125USA The Division of Physics Mathematics and AstronomyCaltechPasadena CA 91125USA Walter Burke Institute for Theoretical Physics CaltechPasadena CA 91125USA

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 ***: We developed the long short-term memory networks with quantile output (condLSTM-Q) model for making quantile predictions on COVID-19 death ***: 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 ***: 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.

关键词： COVID-19 machine learning deep learning epidemiology time series forecast

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