Toward Interpretable Machine Learning for Understanding Epidemic Data

The COVID-19 pandemic is a worldwide crisis with impacts that are both devastating and inequitable as effects often fall hardest on communities that are already suffering from economic, social, and political disparities. Interpretable machine learning (IML) offers the possibility for detailed understanding of this and similar disease outbreaks, allowing subject matter experts to explore the data more thoroughly and find patterns and connections that might otherwise remain hidden. As an active area of research in artificial intelligence, IML has great significance yet numerous technical challenges to overcome. In this paper, we focus on approximating epidemic curves using an interpretable artificial neural network. This is a first step toward a flexible and interpretable modeling framework that we plan to use to study impacts of various demographic, socioeconomic, and other factors on disease outbreaks. We tap into a substantial but little-known collection of IML studies in nonlinear function approximation from engineering mechanics, where domain knowledge including visually observable features of the data is systematically sorted and directly utilized in the initialization of sigmoidal neural networks leading to training success and good generalization. After an introductory review of existing work, we present a feasibility study on approximating a particular epidemic curve leading to a promising result.