Hello, I’m Clint McDaniel.
I’m an epidemiologist with the Molecular Epidemiology
and Outbreak Investigations Team in the Division of Tuberculosis Elimination at CDC.
This presentation will introduce the Location and Time to Epidemiology algorithm,
or LATTE.
LATTE is a free analytic tool that can be used to analyze data
on dates of stays in locations and dates of infectious periods
to identify epidemiologic links during location-based contact investigations.
When a patient with active TB disease is identified,
public health officials will generally initiate a contact investigation
to determine whether the patient had contact with other persons
that might have resulted in TB transmission.
Managing and analyzing data are often among the most challenging
aspects of conducting a contact investigation.
This is especially true for location-based contact investigations in which a patient
names one or more locations where they spent time.
Public health officials must then identify other persons who also spent time
at these locations and determine whether, when, and for how long
any of these persons overlapped with the patient in order to identify
and prioritize contacts for follow-up.
It is not uncommon for a patient to name multiple locations
and for a location-based contact investigation
to identify hundreds of potential contacts.
Manually organizing and comparing large amounts of location and date data
to identify overlaps and prioritize contacts of concern
can be an extremely time consuming, error prone, and frustrating task.
To facilitate this process, CDC created LATTE,
the Location and Time to Epidemiology algorithm.
LATTE is a free analytic tool that can be used to analyze data on
dates of stays in locations and dates of infectious periods
to identify epidemiologic links during location-based contact investigations.
LATTE can rapidly quantify the timing and duration of overlaps
between persons at multiple locations, including identifying overlaps
that occurred during a patient’s infectious period
when TB transmission could have occurred.
This information can help public health officials to quickly
and reliably identify and prioritize contacts for follow-up.
To use LATTE, investigators should have data on dates of stays in locations
for patients and potential contacts.
These data can be obtained from sources such as
employee timecards at workplaces, enrollment rosters for school courses,
client logs for homeless shelters, and inmate manifests for correctional facilities.
This screenshot shows hypothetical data entered into a LATTE data upload template
available from CDC and ready for a LATTE analysis.
Each row includes the start and end date for a stay
of a particular person at a particular location.
A single LATTE analysis can include data for multiple stays
by multiple persons at multiple locations.
To identify overlaps that occurred when patients were infectious,
investigators should also have data on estimated dates of infectious periods.
These dates can be estimated using information such as symptom onset dates,
treatment start dates, and isolation start dates.
Again, this screenshot shows hypothetical data entered into
a LATTE data upload template available from CDC
and ready for a LATTE analysis.
A LATTE analysis can be performed using this simple online user interface.
Users can quickly upload one of two data files,
specify a handful of analysis settings, and then hit the run button.
A LATTE analysis typically takes less than a minute to complete.
Among the various outputs of a LATTE analysis are Gantt charts,
which provide visualizations of dates of stays in locations
and estimated dates of infectious periods
With dates listed across columns, and persons are listed
on the left-hand side of the chart.
Cells shaded black and grey indicate dates of stays in locations.
And cells shaded red indicate estimated dates
of infectious periods of patients.
By scanning down columns, overlaps of one or more persons
at a location, and overlap that occurred during the estimated infectious period
of one or more patients, can be quickly identified.
Another output of a LATTE analysis is the summary by person output file.
For each person included in the analysis, this file reports
the total number of days of overlap with other persons...
…and the total number of days of overlap with patients
during their infectious periods if infectious period data were available.
This information can inform cluster and contact investigations in multiple ways.
Among the contacts, Contact 2 had a relatively high
number of days of overlap with one or more patients
during their infectious periods.
Because of this exposure, Contact 2 could be prioritized for follow-up.
In contrast, Contact 3 had no days of overlap with a patient during their infectious period.
Because of this lack of exposure, Contact 3 is lower risk of having been infected
at Homeless Shelter A and could represent a low priority for follow-up.
By sorting the data based on values in column F,
a list of persons ordered according to their priority
for follow-up can easily be generated.
In summary, LATTE was designed to identify overlaps in space and time
between cases or between cases and contacts.
It can be used for any congregate setting investigation
with one or more locations.
It is important to keep in mind the need to collect and clean
the data being used in LATTE.
Users should always consider the accuracy and completeness
of the data inputs, as this can impact the results.
We recommend users update the data inputs and re-run LATTE
as new information becomes available.
This will ensure the most accurate results and conclusions are achieved in your investigation.
Finally, because LATTE is an algorithm that is hosted on CDC servers,
users should not enter any personally identifiable information into the templates.
Should you want to use identifiable information with LATTE,
we’re happy to show you how to run the code
in Rstudio on your local computer.
Additional information on how to access and use LATTE
can be obtained at the URLs shown here.
Supporting material available for download include a user’s manual,
training presentation, input file templates and training datasets that can be used
to practice running LATTE.
The LATTE R source code is also available online.
Interested users can send an email to TBGenotyping@cdc.gov
with questions or to initiate credentialing for access to the online user interface.
We would like to thank the CDC Office of Advanced Molecular Detection
for their support in developing the LATTE algorithm.