Fusing Low-Latency Data Feeds with Death Data to Accurately Nowcast COVID-19 Related Deaths

TL;DR

This paper introduces a machine learning and epidemiological modeling approach that combines social media data with death records to improve real-time COVID-19 death nowcasting accuracy.

Contribution

It presents a novel method that fuses low-latency social media signals with death data using a probabilistic model to enhance COVID-19 death predictions.

Findings

Fusing social media data improves nowcast accuracy.

The model outperforms death-only forecasts.

Real-time social media signals provide valuable early indicators.

Abstract

The emergence of the novel coronavirus (COVID-19) has generated a need to quickly and accurately assemble up-to-date information related to its spread. While it is possible to use deaths to provide a reliable information feed, the latency of data derived from deaths is significant. Confirmed cases derived from positive test results potentially provide a lower latency data feed. However, the sampling of those tested varies with time and the reason for testing is often not recorded. Hospital admissions typically occur around 1-2 weeks after infection and can be considered out of date in relation to the time of initial infection. The extent to which these issues are problematic is likely to vary over time and between countries. We use a machine learning algorithm for natural language processing, trained in multiple languages, to identify symptomatic individuals derived from social media and, in particular Twitter, in real-time. We then use an extended SEIRD epidemiological model to fuse combinations of low-latency feeds, including the symptomatic counts from Twitter, with death data to estimate parameters of the model and nowcast the number of people in each compartment. The model is implemented in the probabilistic programming language Stan and uses a bespoke numerical integrator. We present results showing that using specific low-latency data feeds along with death data provides more consistent and accurate forecasts of COVID-19 related deaths than using death data alone.