Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement

• URL: http://arxiv.org/abs/2006.12617v2
• Date: Wed, 1 Jul 2020 14:19:23 GMT
• Title: Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement
• Authors: Stewart W Doe, Tyler Russell Seekins, David Fitzpatrick, Dawsin Blanchard, Salimeh Yasaei Sekeh
• Abstract summary: We adapt the state and county level model, TDEFSI-LONLY, to national and county level COVID-19 data. We show that this model poorly forecasts the current pandemic. We propose an alternate forecast model, it County Level Epidemiological Inference Recurrent Network (alg) that trains an LSTM backbone on national confirmed cases to learn a low dimensional time pattern.
• Score: 1.8899300124593645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately forecasting county level COVID-19 confirmed cases is crucial to optimizing medical resources. Forecasting emerging outbreaks pose a particular challenge because many existing forecasting techniques learn from historical seasons trends. Recurrent neural networks (RNNs) with LSTM-based cells are a logical choice of model due to their ability to learn temporal dynamics. In this paper, we adapt the state and county level influenza model, TDEFSI-LONLY, proposed in Wang et a. [l2020] to national and county level COVID-19 data. We show that this model poorly forecasts the current pandemic. We analyze the two week ahead forecasting capabilities of the TDEFSI-LONLY model with combinations of regularization techniques. Effective training of the TDEFSI-LONLY model requires data augmentation, to overcome this challenge we utilize an SEIR model and present an inter-county mixing extension to this model to simulate sufficient training data. Further, we propose an alternate forecast model, {\it County Level Epidemiological Inference Recurrent Network} (\alg{}) that trains an LSTM backbone on national confirmed cases to learn a low dimensional time pattern and utilizes a time distributed dense layer to learn individual county confirmed case changes each day for a two weeks forecast. We show that the best, worst, and median state forecasts made using CLEIR-Net model are respectively New York, South Carolina, and Montana.

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