Weighted Neural Networks for Predicting Daily Covid-19 Death Counts

Document Type

Presentation Abstract

Presentation Date

4-13-2021

Abstract

Covid-19 is a highly contagious virus that has almost frozen the world. This virus is more likely to be moved from one county to adjacent counties. Accurate predictions of disease trajectory in the near term are critical. Thus, spatial contagion is an important aspect of the Covid-19 spread and the death counts attribute to Covid-19 in the adjacent counties are spatially correlated. The task poses the challenge that the dataset is spatially and temporally correlated. Artificial neural networks (ANNs) are presently the single best class of predictive functions but cannot handle this kind of dataset. To overcome this and attempt to exploit information induced by spatial and temporal dependencies, we modified ANNs by adding observation weights to the conventional neural networks referred to as a weighted neural network. The performance of the model is quantified by the mean absolute error.

Additional Details

April 13, 2021 at 3:00 p.m. via Zoom

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