Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction?

Reliable, short-term forecasts of traditional public health reporting streams (such as cases, hospitalizations, and deaths) are a key ingredient in effective public health decision-making during a pandemic. Since April 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity. This paper studies the utility of these indicators from a forecasting perspective. We focus on five indicators, derived from medical insurance claims data, web search queries, and online survey responses. For each indicator, we ask whether its inclusion in a simple model leads to improved predictive accuracy relative to a similar model excluding it. We consider both probabilistic forecasting of confirmed COVID-19 case rates and binary prediction of case "hotspots". Since the values of indicators (and case rates) are commonly revised over time, we take special care to ensure that the data provided to a forecaster is the version that would have been available at the time the forecast was made. Our analysis shows that consistent but modest gains in predictive accuracy are obtained by using these indicators, and furthermore, these gains are related to periods in which the auxiliary indicators behave as "leading indicators" of case rates.