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Apriqot is a community magnifying glass that enables data-driven decision-making.

What We Do

What We Do

Apriqot develops health data analytics solutions that combine publicly available data on demographics, geographics, and public health metrics. Apriqot’s team has deep expertise in developing neighborhood and census tract-level estimates of health conditions using publicly available data, health surveys, disease registries, vital records, and administrative records.

Public Health Has a Problem

Health officials lack access to quality local data to inform program and policy decisions.


While many datasets contain some local data, no tool exists that can extract, combine, and transform that data to make it usable at the local level.

As a result, health officials miss community insights and trends that should be in plain sight, leading to ineffective and inefficient use of limited funds and resources.


Apriqot Has a Solution

ApriqotTM is the community magnifying glass missing from the public health data toolkit.

Apriqot is a demographic and geographic platform upon which local data can be grafted, community-level analysis can be performed, and future-looking models can be constructed.

Apriqot makes local data usable, allowing stakeholders to zoom in on their local context to detect what is happening, where it's happening, and who is impacted.

Our Team
Nicole Hewes
Director of Projects & Communications
Xinyan Liu
Data & Software Engineer
Tim Masse
Fractional Chief Strategy Officer
Rafael G. Ramos, PhD
Geospatial Consultant
Orijeet Mukherjee
Machine Learning Engineer
(Joining July 2024)

Our Advisors

Why Apriqot?

Why Are We Called Apriqot?

There are many different reasons why we’re called Apriqot. 


First, we like apricots!


But we also named our company Apriqot because apricots kept coming up while we were developing the idea.   For example, Apriqot establishes and maintains many copies of the  places we are characterizing, kind of like an ORCHARD. And within each characterization (a TREE) there are many individuals (like the APRICOTS).  We also call the process of bringing in users’ local data and merging it with our data GRAFTING, and some of the methods we use to produce estimates use something called a KERNEL


Also, we like that apricot is a color, which makes some decisions a lot easier (e.g., what color should our logo be?)


And apricots have a nice blush to them which makes them look a little like people. 


But the real reason we are called Apriqot is a bit more convoluted. In public health, one typically uses “p” for the probability of having some condition or disease. If you are talking about a lot of people in community, then pit  would be the probability of the ith person have the disease at time t.  And if you wanted to talk about the average probability, you might use pot , pronounced “p naught t,” as the average probability of having the disease at time t. So the chance of not having a condition is 1-p, but that is also called q.  So qot is the average probability of not having the condition or disease in the community– the probability of being well.  That is the “-qot,”


And we added that to one of the most boring names that we thought of and didn’t use as our company name: “Applied Population Research Inc.” 🥱


And that is why we’re called Apriqot.  Now if we could just figure out how it’s pronounced - ape-ricot or ah-pricot…  Let us know what you think.

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