Online Social Networks Flu Trend Tracker: A Novel Sensory Approach to Predict Flu Trends - Biomedical Engineering Systems and Technologies - page 328

Online Social Networks Flu Trend Tracker:
A Novel Sensory Approach to Predict Flu Trends

Harshavardhan Achrekar 1 , Avinash Gandhe 2 , Ross Lazarus 3 ,
Ssu-Hsin Yu 2 , and Benyuan Liu 1

1 Department of Computer Science, University of Massachusetts Lowell, Massachusetts, U.S.A.
2 Scientific Systems Company Inc, 500 West Cummings Park, Woburn, Massachusetts, U.S.A.
3 Department of Population Medicine, Harvard Medical School, Boston, Massachusetts, U.S.A.

Abstract. Seasonal influenza epidemics cause several million cases of illnesses
cases and about 250,000 to 500,000 deaths worldwide each year. Other pan-
demics like the 1918 “Spanish Flu” may change into devastating event. Reducing
the impact of these threats is of paramount importance for health authorities,
and studies have shown that effective interventions can be taken to contain the
epidemics, if early detection can be made. In this paper, we introduce Social
Network Enabled Flu Trends (SNEFT), a continuous data collection framework
which monitors flu related messages on online social networks such as Twit-
ter and Facebook and track the emergence and spread of an influenza. We show
that text mining significantly enhances the correlation between online social net-
work(OSN) data and the Influenza like Illness (ILI) rates provided by Centers for
Disease Control and Prevention (CDC). For accurate prediction, we implemented
an auto-regression with exogenous input (ARX) model which uses current OSN
data and CDC ILI rates from previous weeks to predict current influenza statis-
tics. Our results show that, while previous ILI data from the CDC offer a true (but
delayed) assessment of a flu epidemic, OSN data provides a real-time assessment
of the current epidemic condition and can be used to compensate for the lack of
current ILI data. We observe that the OSN data is highly correlated with the ILI
rates across different regions within USA and can be used to effectively improve
the accuracy of our prediction. Therefore, OSN data can act as supplementary
indicator to gauge influenza within a population and helps to discover flu trends
ahead of CDC.

1 Introduction

Seasonal influenza epidemics result in about three to five million cases of severe ill-
ness and about 250,000 to 500,000 deaths worldwide each year [11]. In 1918, the so-
called “Spanish flu” killed an estimated 20-40 million people worldwide, and since
then, human-to-human transmission capable influenza virus has resurfaced in a vari-
ety of particularly virulent forms much like “SARS” and “H1N1” against which no
prior immunity exists, resulting in a devastating situation with severe casualties. Re-
ducing the impact of seasonal epidemics and pandemics such as the H1N1 influenza is
of paramount importance for public health authorities. Studies have shown that pre-
ventive measures can be taken to contain epidemics, if an early detection is made
or if we have some form of an early warning system during the germination of an