A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA. (arXiv:2109.12094v4 [stat.ML] UPDATED)

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 modeling,
compartmental models, are based on the assumption of spatiotemporal homogeneity
of the spread of the virus, which may cause forecasting to underperform,
especially at high spatial resolutions. In this paper we approach the
forecasting task with an alternative technique - spatiotemporal machine
learning. We present COVID-LSTM, a data-driven …

arxiv county covid covid-19 forecasting learning machine machine learning ml usa