Paper Info
Title
Reconstructing The Mers Disease Outbreak From News
Abstract
Disease surveillance is critical for mobilizing health care resources and deciding on isolation measures to contain the spread of infectious diseases. Because ground truth signals of rare and deadly diseases are sparse, it can be useful to enrich surveillance systems using measures of social and environmental factors which are known to influence the spread of a disease. One approach to measure such factors is by using real time news streams. In this study, we model the epidemiological transmission of the Middle Eastern Respiratory Syndrome (MERS) disease during the outbreak that occurred from 2013 to 2018 in the Arabian peninsula. Using the GDELT news event database, we show that conflict related signals allow us to reconstruct the time series of newly infected cases per week. This reduces the residual sum of squared errors by a factor of 3.36 as compared to a standard epidemiological model. We also capture interpretable time-sensitive factors which illustrate the importance of using real time news stream to model the evolution of a disease such as MERS and facilitate early and effective policy interventions.
Year
DOI
Venue
2019
10.1145/3314344.3332498
COMPASS '19 - PROCEEDINGS OF THE CONFERENCE ON COMPUTING & SUSTAINABLE SOCIETIES
Keywords
DocType
Citations 
Disease Surveillance, Big Data, News Analytics, Sparse Signals, Time Series
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Ananth Balashankar122.06
Aashish Dugar200.34
Lakshminarayanan Subramanian31540163.14
Samuel Fraiberger401.01