Continuous Artificial Prediction Markets as a Syndromic Surveillance Technique

TL;DR

This paper explores the application of continuous Artificial Prediction Markets (c-APM) to enhance syndromic surveillance, demonstrating improved accuracy over Google Flu Trends models in early outbreak detection.

Contribution

It introduces the use of c-APM for syndromic surveillance and shows its effectiveness in outperforming existing Google Flu Trends models.

Findings

c-APM achieves lower MAE than Google Flu Trends in most years

c-APM shows significant improvement between 2011 and 2013

The approach enhances early outbreak detection accuracy

Abstract

The main goal of syndromic surveillance systems is early detection of an outbreak in a society using available data sources. In this paper, we discuss what are the challenges of syndromic surveillance systems and how continuous Artificial Prediction Market [Jahedpari et al., 2017] can effectively be applied to the problem of syndromic surveillance. We use two well-known models of (i) Google Flu Trends, and (ii) the latest improvement of Google Flu Trends model, named as GP [Lampos et al., 2015], as our case study and we show how c-APM can improve upon their performance. Our results demonstrate that c-APM typically has a lower MAE to that of Google Flu Trends in each year. Though this difference is relatively small in some years like 2004 and 2007, it is relatively large in most years and very large between 2011 and 2013.