['flu','headache','bodyache','sinusinfection','tonsillitis','tonsil','pneumonia','influenza','strep','fever','migraine','colds','cold','bronchitis','sniffly','sniffling','sinuses','sinus','runnynose','throat','allergy','allergies', 'allergyseason','food','pollen','allergyproblems','allergens','medication']

Architecture Detailed Design

Data Processing & Analysis

Data Analysis – Use Case

Sample Use Case

Location New York

Date 06/28/2015

# Matched Tweets 2000

Matched Tweets: Tweets contain one of the keywords listed below

Flu Cold Allergyflu sinusinfectio

nallergy

bodyache tonsillitis allergies

headache tonsil allergyseason

pneumonia migraine pollen

influenza colds allergyproblems

Fever sniffly allergens

strep cough

bronchitis sniffly

throat sniffling

infection sinus

runnynose

Keywords

Raw Data

Tweets

Weather

Allergy

EMR 1 – Tweets Processing

1 2 3 n

n Number of DISTINCT combination of key wordsEMR Reduce Algorithm

For each tweet

Identify the key words present in the tweet text

More than 1 keyword in the tweet

text

Keyword = keyword 1 +

‘+’ + keyword 2

Get the cumulative count for each keyword

Location: New YorkDate: 06/28/2015

Yes

Data Pipeline Processes

A bucket is created for each combination of• Location• Tweet date• Keyword

5

5 For each bucket

5

Syndrome Count Process

Flu Category Cold Category Allergy Category

Preference: Flu > Allergy > Cold

Noise Category

Examples of keywords• “throat”• “food + allergies”

•

July 29 data missing, so ignore the dip