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

• URL: http://arxiv.org/abs/2011.11507v1
• Date: Mon, 23 Nov 2020 16:14:48 GMT
• Title: condLSTM-Q: A novel deep learning model for predicting Covid-19 mortality in fine geographical Scale
• Authors: HyeongChan Jo (1), Juhyun Kim (2), Tzu-Chen Huang (3), Yu-Li Ni (1) ((1) Division of Biology and Biological Engineering, Caltech, (2) The Division of Physics Mathematics and Astronomy, Caltech, (3) Walter Burke Institute for Theoretical Physics, Caltech)
• Abstract summary: CondLSTM-Q is a model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile output (condLSTM-Q), a well-performing model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models, which would especially benefit state-level officials to coordinate resources within the state. The quantile predictions from condLSTM-Q inform people about the distribution of the predicted death tolls, allowing better evaluation of possible trajectories of the severity. 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 useful predictions such as new cases or hospitalizations intuitively.

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