Adaptive County Level COVID-19 Forecast Models: Analysis and Improvement
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Ensemble Machine Learning Methods for Modeling COVID19 Deaths
Using a hybrid of machine learning and epidemiological approaches, we propose a novel data-driven approach in predicting US COVID-19 deaths at a county level. The model gives a more complete description of the daily deat…
BIG-bench Machine LearningA spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA
With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeli…
Curating a COVID-19 data repository and forecasting county-level death counts in the United States
As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this paper, we present our continuous curation of a large data repository containing …
COVID-19 TrackingDecision MakingDecision Making Under UncertaintyPrediction+1Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests
Rapid and accurate detection of community outbreaks is critical to address the threat of resurgent waves of COVID-19. A practical challenge in outbreak detection is balancing accuracy vs. speed. In particular, while esti…
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level. To implement it and evaluate the public health policies, we devel…
BIG-bench Machine Learning