Using social media for Nowcasting the Flu Activity Infectious diseases impose a significant burden to the U.S. public he

[answerhappygod]

Using social media for Nowcasting the Flu
Activity
Infectious diseases impose a significant burden to the U.S.
public health system. The rise of HIV/AIDs in the late seventies,
pandemic H1N1 flu in 2009, H3N2 epidemic during the 2012-2013
winter sea-son, the Ebola virus disease outbreak in 2015, and the
Zika virus scare in 2016, have demonstrated the susceptibility of
people to such contagious diseases. Virtually each year influenza
outbreaks happen in various forms and result in consequences of
varying impacts. The annual impact of seasonal influenza outbreaks
in the United States is reported to be an average of 610,660
undiscounted life-years lost,3.1 million hospitalized days, 31.4
million outpatient vis-its, and a total of $87.1 billion in
economic burden. As a result of this growing trend, new data
analytics techniques and technologies capable of detecting,
tracking, mapping, and managing such diseases have come on the
scene in recent years. In particular, digital surveillance systems
have shown promise in their capacity to discover public health
seeking patterns and transform these discoveries into action-able
strategies.
This project demonstrated that social media can be utilized as
an effective method for early detection of influenza outbreaks. We
used a Big Data platform to employ Twitter data to monitor
influenza activity in the United States. Our Big Data analytics
methods comprised temporal, spatial, and text mining. In the
temporal analysis, we examined whether Twitter data could indeed be
adapted for the nowcasting of influenza outbreaks. In spatial
analysis, we mapped flu outbreaks to the geospatial property of
Twitter data to identify influenza hotspots. Text analytics was
performed to identify popular symptoms and treatments of flu that
were mentioned in tweets.
The IBM InfoSphere BigInsights platform was employed to analyze
two sets of flu activity data: Twitter data were used to monitor
flu outbreaks in the United States, and Cerner Health Facts data
warehouse was used to track real-world clinical encounters. A huge
volume of flu-related tweets was crawled from Twitter using Twitter
Streaming API and was then ingested into a Hadoop cluster. Once the
data were successfully imported, the JSON Query Language (JAQL)
tool was used to manipulate and parse semistructured JavaScript
Object Notation (JSON)data. Next, Hive was used to tabularize the
text data and segregate the information for the spatial-temporal
location analysis and visualization in R. The entire data mining
process was implemented using MapReduce functions, we used the
package BigR to submit the R scripts over the data stored in HDFS.
The package BigR enabled us to benefit from the parallel
computation of HDFS and to perform MapReduce operations. Google's
Maps API libraries were used as a basic mapping tool to visualize
the tweet locations.
Our findings demonstrated that the integration of social media
and medical records can be a valuable supplement to the existing
surveillance systems. Our results confirmed that flu-related
traffic on social media is closely related with the actual flu
outbreak. This has been shown by other researchers as well (St
Louis & Zorlu,2012; Broniatowski, Paul, & Dredze, 2013). We
performed a time-series analysis to obtain the spatial-temporal
cross-correlation between the two trends (91%) and observed that
clinical flu encounters lag behind online posts. In addition, our
location analysis revealed several public locations from which a
majority of tweets were originated. These findings can help health
officials and governments to develop more accurate and timely
forecasting models during outbreaks and to inform individuals about
the locations that they should avoid during that time period.
QUESTIONS FOR DISCUSSION
1. Why would social media be able to serve as an early
predictor of flu outbreaks?