The document details a simulated epidemiological investigation of a foodborne outbreak in Oswego, New York, highlighting the importance of data synchronization across multiple counties. Epidemiologists used the epi infotm mesh4x tool to analyze, visualize, and share data regarding cases of gastrointestinal illness linked to vanilla ice cream and baked ham. The findings illustrated that collaborative data sharing significantly improved outbreak investigation outcomes, ultimately identifying the cause of the illness.

1. Oswego in the Cloud: Epi Info™—Mesh4x Synchronization
Prototype
By: Taha Kass-Hout, MD, MS (InSTEDD)
Many epidemiologists are familiar with the foodborne outbreak in Oswego, New York, U.S.A.
on April 18th, 1940. In this outbreak, over half of the participants at a pot-luck church supper
developed a gastro-intestinal illness. A survey was created and interviews were conducted with
75 of the 80 people known to have been present to determine the source of the contamination.
While the Oswego study focused on a single region, the significant value of data synchronization
can be demonstrated by expanding this scenario to where interviews and data entry are
conducted in different localities. Therefore, we expanded the scenario by populating fictitious
addresses spread across five counties in New York upstate region. The actual church supper was
held in the Oswego county; however, in our imaginary scenario we added four additional
neighboring counties: Jefferson, Lewis, Oneida, and Wayne. In this scenario, two
epidemiologists conducted the outbreak investigation; an Epidemic Intelligence Service (or EIS)
officer investigated the outbreak in Oneida county and another officer at the state health
department investigated the other four counties. Before the data synchronization, the Oneida
county EIS officer could not conclude the cause of the outbreak since his partial data showed two
competing factors. However, after data synchronization, both investigators had a clear picture of
the spread of the illness (over space and time) and the Oneida county EIS officer was able to
clearly detect the actual cause of the outbreak, the Vanilla Ice Cream.
The Scenario Script

2. DISCLAIMER: All places used in the modified (imaginary) scenario are fictitious. Any
similarity to real places is a coincidence.
Outbreak investigation in Oneida County
Scenario: Oneida Medical Officer (Dr. Roger Mir) receives a call from local doctors about
patients reporting to clinics with gastrointestinal illness. Dr. Mir launches the investigation and
begins interviews.
Analysis of Outbreak Data: Dr. Mir opens the Analysis module in Epi Info™
Roger: I have returned to the office from the interviews and I have done some preliminary
analysis of the data. I will use Epi Info™ to show you my initial results. First, I need to open
the Epi Info™ Analysis tool and READ the data into Epi Info™:
• Epi Info™ Command: READ epiinfo.mdb: viewOswego

3. Roger: In the output window, Dr. Mir finds a Record Count of 28.
• Frequencies of COUNTY and ILL fields
o Epi Info™ Command: FREQ COUNTY ILL
Roger: The results show that all 28 cases are in Oneida County (12 interviewees that were ILL
[Ill = 1] and 16 interviewees that were NOT ILL [Ill = 0])
Roger: I will create a 2 by 2 table of BAKED HAM by ILL:
• Epi Info™ Command: TABLES BAKEDHAM ILL

4. The results show that BAKED HAM is significant (at P = 0.05 level).
Roger: I will now create a 2 by 2 table for VANILLA by ILL.
• Epi Info™ Command: TABLES VANILLA ILL

5. These results show VANILLA is also significant (at P = 0.05 level).
Roger: Having analyzed the data for my county, I found that there are at least two potential
sources of the illness—BAKED HAM and VANILLA ICE CREAM.
Generate local Google Earth map for Oneida county
Roger: In order to gain a perspective of the cases, I will create a map of all the data and display
the map in Google Earth (or KML map layers). In order to generate the map, I will launch the
Epi Info™ Mesh4x Data Synchronization tool and generate a KML file as follows:
• Click the “Map Exchange” tab.
• Specify the data source (EpiInfo.mdb) and select the Oswego table.
• Click the “Create Map” button which will automatically generate a spatio-temporal map.

6. It takes Dr. Mir approximately 30 seconds for the tool to automatically geocode all the addresses
in his database and generate the map layers (create a Google Earth KML file).
Roger: To show the map, I will now open Google Earth and show the file generated by the
Epi Info™ Mesh4x tool. The results map has 28 cases in Oneida county:
• 12 patients reported with GI illness (Red pins) between April 18th and 19th
• 16 interviewers did not show any illness (Yellow pins)

7. Dr. Mir logged into Epi-X, CDC’s Epidemic Exchange collaboration environment and
discovered a discussion about an outbreak in his neighboring county of Oswego. He initiates a
call to Dr. Nitschke (with the State Health Department) to further discuss the situation…
Roger: I logged into Epi-X and discovered a discussion about an outbreak in my neighboring
counties; I’d like to ask if you can share the data for these counties with me.
Outbreak investigation in the other counties
Scenario: The NY State Epidemiologist requests an EIS officer (Dr. David Nitschke) to help
investigate a GI outbreak that appears in the counties of Oswego, Lewis, Jefferson, and Wayne.
David goes through similar steps as described above for all counties except Oneida.

8. Analysis of Outbreak Data: Dr. Nitschke opens the Analysis module in Epi Info™
David: Having returned to the office from the interviews and running some preliminary
analyses, allow me to use Epi Info™ to show you my initial results. First, I need to open
Analysis and READ the data in Epi Info™:
• Epi Info™ Command: READ epiinfo.mdb: viewOswego
David: In the Output window, I find a Record Count of 47 across all the 4 counties. Notice that
I do not yet have data for Oneida County.
• Frequencies of County and ILL
o Epi Info™ Command: FREQ COUNTY ILL
David: I create a 2 by 2 table for BAKEDHAM by ILL and for VANILLA by ILL:
• Epi Info™ Command: TABLES BAKEDHAM ILL
• Epi Info™ Command: TABLES VANILLA ILL

9. These results only show VANILLA to be significant (at P = 0.05 level), BAKEDHAM was not
significant. Now I will share this information with Roger for further collaboration on the
outbreak investigation.
Generate local Google Earth map for the other counties
David: In order to gain a perspective of the cases, I will create a map of all the data and display
in Google Earth (or KML map layers). In order to generate the map, I will launch the Epi Info™
Mesh4x tool and generate a KML file as follows:
• Click the “Map Exchange” tab
• Specify the data source (EpiInfo.mdb) and select the Oswego table
• Click the “Create Map” button which will automatically generate a spatio-temporal map

10. It takes Dr. Nitschke less than a minute for the tool to automatically geocode all the addresses in
his database and generate the map layers (create a KML file).
David: To show the map, I will now open Google Earth and show the file generated by the Epi
Info™ Mesh4x tool. The results map has 47 records in the 4 counties:
• 33 patients reported with GI illness (Red pins) between April 18th and 19th
• 14 interviewers did not show any illness (Yellow pins)
Dr. Nitschke just received a call from Dr. Mir and learned that Oneida county has 28 cases, but
Dr. Mir is unable to conclude the source of the outbreak based on his information.
David: While speaking to Dr. Mir, I noticed that I don’t have any cases in Oneida County. I also
learned that he is unable to conclude the source of the outbreak given the information he has and

11. the analysis he ran. We both agree to share our information to further collaborate and investigate
the situation.
Data synchronization between Oneida County and the other neighboring counties
Dr. Nitschke prepares to launch the Epi Info™ Mesh4x tool and Sync data over the Amazon
EC2/S3 cloud (State’s available online data)
David: I will first create a mesh (a collaborative space where I can share and synchronize my
data with others) by visiting this site: http://sync.staging.instedd.org:8080/mesh4x.
I will create an Epi Info collaborative mesh space (Epiinfo):
Then I will create the data feed (Oswego) and the fields I want to show on the map (mapping)
Here are the fields I want to share on a map (in the Mapping field):
<item.title>patient name: {Oswego/Name}</item.title>
<item.description>adress: {Oswego/Address}</item.description>
<geo.location>{geoLocation(Oswego/Address)}</geo.location>
<geo.longitude>{geoLongitude(Oswego/Address)}</geo.longitude>
<geo.latitude>{geoLatitude(Oswego/Address)}</geo.latitude>
<patient.ill>{Oswego/ILL}</patient.ill>

12. <patient.updateTimestamp>{Oswego/DateOnset}</patient.updateTimestamp>
Then, I will evaluate the data in the cloud (State’s Data ready to be shared) by visiting this site:
http://sync.staging.instedd.org:8080/mesh4x/feeds/Epiinfo/Oswego. There are currently no
records in the State’s online database.
David: I will now make my data available on our online database (over the Amazon EC2/S3
cloud for this exercise) so Dr. Mir can synchronize with our records. I will now select the Data
Exchange tab in the Epi Info™ Mesh4x tool and click Synchronize.
David: I will evaluate if my records are now available in the online database for Dr. Mir to
synchronize. I refresh the page
(http://sync.staging.instedd.org:8080/mesh4x/feeds/Epiinfo/Oswego) and here are all the 47
records from all the counties except for Oneida.

13. Roger (on the phone with David): Now I can see the data in the cloud, I will now synchronize
my information with yours.
Roger: I prepare to launch the Epi Info™ Mesh4x tool and Sync data over the Amazon EC2/S3
cloud (State’s available online data)
• Select the Data Exchange tab and click Synchronize
Roger: Now I have the data from the other counties. The State Database on the mesh should
have all of the data from my Oneida County. I Return to the web browser and refresh the Mesh
URL to show the full complement of 75 records.

14. Analysis of Outbreak Data for ALL Counties: Dr. Mir and Nitschke open the Analysis
module in Epi Info™
Roger: To show that I have the data, I can rerun the statistics from before. I see in my
Epi Info™ database that I now have 75 cases across all five counties (46 ILL and 29 NOT ILL)
• Epi Info™ Command: READ ‘C:epiinfodataepiinfo.mdb’: viewOswego
• Epi Info™ Command: FREQ COUNTY ILL
Roger: I will re-run the analysis from earlier.

15. • Epi Info™ Command: TABLES BAKEDHAM ILL
• Epi Info™ Command: TABLES VANILLA ILL
The results show that BAKEDHAM is no longer significant but VANILLA is clearly the
potential source of the outbreak. I will call David and update him with the results.
David already synchronized his records with the cloud following the same steps as above in
order to get Oneida county data, re-ran the analysis and came up with the same conclusion as
Roger.
Generate Google Earth map for ALL counties
Roger: In order to gain a perspective of the cases for ALL counties, I will re-create the map and
display in Google Earth (or KML map layers). In order to generate the new map, I will follow
the same steps as before by launching the Epi Info™ Mesh4x tool and generate a KML file:

16. • Click the “Map Exchange” tab
• Specify the data source (EpiInfo.mdb) and select the Oswego table
• Click the “Create Map” button which will automatically generate a spatio-temporal map
It takes Dr. Mir less than a minute for the tool to automatically geocode the new addresses in his
database and generate the map layers (create a KML file).
Roger: To show the map, I will now open Google Earth and show the file generated by the
Epi Info™ Mesh4x tool. The results map has 75 records in the 4 counties:
• 46 patients reported with GI illness (Red pins) between April 18th and 19th
• 29 interviewers did not show any illness (Yellow pins)

17. Updating Records
Scenario: Dr. Nitschke receives a call about a new incident case from Lewis County. Upon
further investigation, the previously not ill person is now ill because he ate Vanilla Ice Cream
that he took home from the church supper.
David: I will update the information for Patient19 in the Epi Info™ database.

18. David: I will now make the update available on our online database (over the Amazon EC2/S3
cloud for this exercise) so Dr. Mir can have the latest update. I will now select the Data
Exchange tab in the Epi Info™ Mesh4x tool and click Synchronize.
David: I will evaluate if my records are updated in the online database for Dr. Mir to re-initiate
the synchronization process. I refresh the page and check the status of illness and vanilla for
Patient19.

19. Roger: I will re-launch Epi Info™ Mesh4x tool to get the updated record:
• Sync data over the Amazon EC2/S3 cloud (State’s online available data)
Roger: I will check the data in my Epi Info database and I see that I have the most up-to-date
information on Patient19. Next, I will regenerate the map as previously described. The results
map shows 75 cases across ALL counties:
• 46 patients reported with GI illness (Red pins) between April 18th and 19th
• 29 interviewers did not show any illness (Yellow pins)
o Patient19 status has changed to ill (Red pin)

20. Sharing Data with the Centers for Disease Control and Prevention (CDC)
Scenario: Dr. Mark Berndt, an epidemiologist at the CDC foodborne branch, is now involved in
the investigation and wants to access State data.
Mark: I will launch the Epi Info™ Mesh4x tool, synchronize to receive NY state’s data, then
create a map of the data:
• Select the Data Exchange tab and click Synchronize
• Select the Map Exchange tab and click Create Map
The results map shows 75 cases in NY state across 5 counties:
• 47 patients reported with GI illness (Red pins) between April 18th and 19th
• 28 interviewers did not show any illness (Yellow pins)

21. Competing interests
InSTEDD was supported by research funding from Google.org and Rockefeller Foundation.