1. Epidemic Alerts
EECS E6898: TOPICS – INFORMATION PROCESSING: From Data to Solutions
Alexander Loh
May 5, 2016

2. Epidemic Alerts Motivation Prior Work Description Experimental Setup
Requirements
Evaluation

3. Motivation Infectious diseases are a problem
Early warning system could help
Actively alert users
Infectious diseases are one of the leading causes of death worldwide (and also a major source of annoyance to people, as those of us who’ve gotten sick know)
An early warning system that tells people whether the place that they are at is at risk of being the site of an epidemic could go a long way in mitigating its spread. For example, people who’ve been told that there is an epidemic going on in their area may choose to stay at home and in so doing reduce the chance of them being infected or passing the infection on to others.
Provide active alerts to users: most users are not going to go out of their way to specifically check whether a disease outbreak is going on in the area, so give them an extremely convenient interface via a mobile app that proactively alerts you

4. Prior work Google Flu Trends BioMosaic HealthMap
Google developed their own flu tracking service called Google Flu Trends in 2008, which tracked search queries for flu on Google in the hope that more searches would correlate with higher incidents of the flu and allow for early detection.
The CDC used a new tool called BioMosaic that accurately modeled the spread of Ebola using health, population and movement data
HealthMap aggregates data from freely available sources along with non-clinical data sources like news articles and Twitter in order to show a comprehensive view of the current global state of infectious diseases.
BioMosaic was able to provide a good model of the spread of Ebola, while Healthmap was able to provide a user-friendly interface; I would like to combine the best of both worlds.

5. Description Obtain geotagged event data User submissions via the app
Hospital records
Social media posts, ie. Twitter
Cluster data by location
Simulate epidemic outbreaks
Alert users
Obtain geotagged data from a variety of sources where available
Simulate an epidemic at each location
Alert users if the simulation shows that the epidemic is likely to spread to their location in the near future
Use big data in order to generate estimators for the parameters of each epidemic outbreak as a function of the data

6. Experimental Design Want to confirm two things Run system in two modes
System’s predictions are accurate
System impacts public health
Informing users may change their behavior
Run system in two modes
Mode 1: does the system predict accurately?
Just gather data
Run simulation based on data
Is ground truth close to simulation?
Mode 2: does the system help users?
Alert users and gather data
Is ground truth less severe than simulation?
Mode 1 – when the system runs in mode 1, it will first try to generate estimators for the parameters in the epidemic outbreak as a function of data. For example, if there are lots of Twitter posts about the epidemic in an area, it may choose to increase the probability that two people can infect each other in the model. We can then compare the output of the system to the ground truth

7. Requirements App programming A server for communication with users
Data collection/simulation may require multiple additional servers
Require a programmer to write code for a mobile device of choice; iPhone or Android
Multiple servers may be required in order to run the simulationand process the data

8. Evaluation Mode 1: does the system predict accurately?
Look at the geotagged data
Look at the simulated epidemic region
Do the two have a similar structure?
Mode 2: does the system help users?
Is the geotagged data roughly contained in the simulated epidemic region?