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TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric ConditionsUnderstanding the impact of atmospheric conditions on SARS-CoV2 is critical to model COVID-19 dynamics and sheds a light on the future spread around the world. Furthermore, geographic distri- butions of expected clinical severity of COVID-19 may be closely linked to prior history of respiratory diseases and changes in humidity, tem- perature, and air quality. In this context, we postulate that by tracking topological features of atmospheric conditions over time, we can provide a quanti?able structural distribution of atmospheric changes that are likely to be related to COVID-19 dynamics. As such, we apply the machinery of persistence homology on time series of graphs to extract topological signatures and to follow geographical changes in relative humidity and temperature. We develop an integrative machine learning framework named Topological Lifespan LSTM (TLife-LSTM) and test its predictive capabilities on forecasting the dynamics of SARS-CoV2 cases. We validate our framework using the number of con?rmed cases and hospitalization rates recorded in the states of Washington and California in the USA. Our results demonstrate the predictive potential of TLife-LSTM in forecasting the dynamics of COVID-19 and modeling its complex spatio-temporal spread dynamics.
Document ID
20220005802
Acquisition Source
Jet Propulsion Laboratory
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Gel, Yulia R.
Wagh, Rishabh
Zhen, Zhiwei
Segovia-Dominguez, Ignacio
Lee, Kyo
Date Acquired
May 11, 2021
Publication Date
May 11, 2021
Publication Information
Publisher: Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2021
Distribution Limits
Public
Copyright
Other
Technical Review

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