Monitoring and forecasting the spread of epidemics: social media analysis vs/& analysis of Google Flu search requests

1. SOCIAL NETWORKS
AS A FORECASTING TOOL
Brand Analytics
(с) 2014
Monitoring and forecasting the spread of epidemics

2. SOCIAL NETWORKS AS MEANS OF FORECASTING
Google Flu is a popular example of «Big data» effectiveness: the service
recognizes flu epidemics faster than physicians by analysing the search
request statistics from Google search engine.
The way Google Flu works is simple: the people who fell sick, or those who
are afraid of it, are using the search engine to find medicine, symptoms
description and other information about the flu. In the time of epidemics, the
number of search requests increases.
We decided to find out if it is possible to do the same by analyzing the posts
in social networks. Counting the complaints of social networks users citing
various symptoms of common cold, we tracked the scale and spread
sickness in different regions of Russia.
In comparison with Google Flu data, our research allows to:
• Increase the accuracy and speed of epidemics recognition,
• Obtain more detailed information including the age of the sick
people,
• Identify the basic symptoms of the sickness.

3. We researched the public messages of Russian authors in social networks VKontakte, Twitter, Odnoklassniki,
Moy Mir, Facebook, LiveLournal, etc.
Sickness indicators were posts with complaints on SARS and flu symptoms: «I am coughing», «throat ache»,
«fever», «body aches», etc.
Research period: 01.09.2013 – 31.10.2013
During the period, more than 220 thousands of such messages were recorded from 165 thousands posters.
EPIDEMICS SPREAD DYNAMICS
During the “usual" period: 2.500 – 3.000 posts per day
Sharp increase: from 7 till 22 of september
Maximum: September 12 – 7.844 posts

4. COMPARING THE RESULTS WITH DATA FROM GOOGLE FLU
The results of our research match the data of Google Flu for Russia: period with the most requests is 8 - 22
september.
Social network data is more agile – note the correlations between the health complaints in social networks and
search requests:
• 12 september – most messages in social media about sickness symptoms,
• 15 september (3 days later) - suffering flu-stricken people are searching for treatment methods in
Google.

5. GEOGRAPHY
It would be interesting to analyze the features of the
spread of sicknesses in Russia. We researched the
periods of September sickness increase in several
cities of our country and came to two main
conclusions:
 Sickness peak is observed in all cities, but the
magnitude is different;
 The sickness is concurrent everywhere.
The charts represent the data for Moscow, St.
Petersburg and Novosibirsk:
 In all cities the sickness peak is on 2-3 week of
September;
 The sickness increase in St. Petersburg is not as
pronounced as in other cities.

6. AGE
Increase in the number of the sick during the
epidemics, %
The amount of complaints from users, who are
older than 45 years has also increased
sharply– by 38%
At the same time the amount of users from 25
to 44 years, who speak about the symptoms,
did not change much: the increase was from 2
to 14%
In the periods of sickness increase, the reference number of flu and cold symptoms increased mainly because
of messages from users younger than 18 years. The post increase within age bracket was 110%

7. MOST COMMON SYMPTOMS
17006
8704
2484
2426
1587
1167
0 2000 4000 6000 8000 10000 12000 14000 16000 18000
Throat ache
Temperature and fever
Headache
Cough
Body aches
Shiver
During the research period social media users mostly complained
about the following symptoms:
• Throat ache – 16,65% from the overall amount of messages
• Fever – 8,52%
• Headache – 2,43%
• Cough – 2,38%
• Body aches– 1,55%
• Chill – 1,14%

8. USE OF SOCIAL MEDIA MONITORING IN MEDICINE
Early detection of epidemics will allow to:
• Decrease the sickness rate and increase the «nation’s health»;
• Decrease the rate and limit the spread of sickness;
• Decrease the delay in delivery of medicine;
• Considerably decrease the government and business expenses for medical treatment.
Thanks to the social media monitoring and analysis technologies we can track the scale and
spread of sicknesses in real time and and take timely measures of disease control.

9. BE HEALTHY! 
Brand Analytics
http://br-analytics.ru

10. BE HEALTHY! 
Brand Analytics
http://br-analytics.ru