4 Measures

4.1 Data: MPX NYC Participant–Community Network

The MPX NYC Participant–Community Network links individuals in the study to the community districts of New York City. In this bipartite network, nodes represent either individual participants or community districts, and ties connect each person to the districts with which they have a relationship.

Spatial data were originally collected at the census tract level but aggregated to community districts for analysis. Census tracts are geographic units that typically contain 2,500 to 8,000 residents, while community districts divide New York City into administrative areas nested within its five boroughs and encompassing multiple census tracts.

A tie between an individual and a community district indicates either residence or attendance at a gathering in that district. Two related network views are derived from this structure: the Community District Network and the Participant Network, each described below.

MPX NYC Community District Network

The MPX NYC Community District Network represents connections among New York City community districts based on participants’ reported residences and gatherings. In this network, nodes correspond to community districts, and a weighted tie between any two districts indicates that at least one participant reported locations in both. The edge weight equals the number of participants linking the two districts through shared activity or residence.

This structure allows us to examine how districts are connected through participants’ movement and social activity, offering insight into how local dynamics may shape city-wide outbreak patterns.
Each district’s role is characterized by its social reach and spatial reach, measures described in later sections.

MPX NYC Participant Network

The MPX NYC Participant Network captures how survey participants are linked through shared physical contexts within the city. In this network, nodes represent individual participants, and a weighted edge connects any two participants who share at least one community district—either through residence or through gatherings they both attended. The edge weight corresponds to the number of community districts that the pair have in common.

Analyzing this network allows us to assess how participants are connected through shared spaces and to explore patterns of connectedness across demographic and behavioral subgroups.

4.2 Node Characteristics

Individual Measures

We collected detailed information on participants’ demographics, health, and patterns of social and sexual connection.

Demographic measures included age, race, sex assigned at birth, gender identity, sexual orientation, and community district of residence.

Health-related measures covered HIV status, viral suppression, STI-related symptoms, use of pre-exposure prophylaxis (PrEP), and mpox vaccination status.

Connection measures captured each participant’s degree of engagement within queer and transgender communities, including:

The number of recent individual sexual contacts,
The number of recent individual physical (non-sexual) contacts,
The number of important social contacts, and
Whether the participant reported any recent physical or sexual contact in a group setting.

Gender Modality

Gender modality was measured using a two-step approach. Participants were first asked to indicate their current gender (options: man, woman, trans man, trans woman, non-binary, or other) and then their sex assigned at birth (male, female, or other). Combining these two responses, we constructed a gender modality variable, which distinguishes between cisgender and transgender participants and captures additional gender diversity in the sample.

Code

data.frame(
  Level = c(
              "Cisgender man", 
              "Cisgender woman",
              "Transgender man",
              "Transgender woman",
              "Transgender man",
              "Transgender woman",
              "Non-binary",
              "Another demographic"
              ),
  Condition = c(
                "gender is 'man' and assigned sex is 'male' ", 
                "gender is 'woman' and assigned sex is 'female'",
                "gender is 'man' and assigned sex is 'female'",
                "gender is 'woman' and assigned sex is 'male'",
                "gender is 'trans man' and sex is any value",
                "gender is 'trans woman' and sex is any value",
                "gender is 'non-binary' and sex is any value",
                "gender is 'other' and sex is any value"
                )
             
) |>
  gt::gt()

Table 4.1: _construction of gender modality variable

Level	Condition
Cisgender man	gender is 'man' and assigned sex is 'male'
Cisgender woman	gender is 'woman' and assigned sex is 'female'
Transgender man	gender is 'man' and assigned sex is 'female'
Transgender woman	gender is 'woman' and assigned sex is 'male'
Transgender man	gender is 'trans man' and sex is any value
Transgender woman	gender is 'trans woman' and sex is any value
Non-binary	gender is 'non-binary' and sex is any value
Another demographic	gender is 'other' and sex is any value

The table above summarizes how gender modality was derived from these responses.

Race–Gender

Building on the gender modality variable, we created a race–gender variable to capture intersectional differences within the sample. This categorical variable mirrors the gender modality categories but provides additional resolution among cisgender men. Rather than grouping all cisgender men together, we divided this category into white cisgender men, Latinx cisgender men, Black cisgender men, and Other cisgender men—the latter combining Asian, Pacific Islander, and other racial identities.

Community District Measures

Spatial Reach

For each community district, spatial reach is defined as the sum of the edge weights connecting that district to other community districts in the MPX NYC Community District Network. It represents the extent of participant movement or overlap between districts, capturing how frequently individuals in the study population reported activity or residence across multiple areas of the city.

4.3 Edge Characteristics

Residence Edges

Each participant reported the location of their residence using the Person–Place Network Map question.
In the MPX NYC Participant–Community Network, each residence is represented as an edge linking the participant to the community district that contains their home.

Gathering Edges

Participants who reported recent physical or sexual contact at a gathering were asked to identify the locations of each event where that contact occurred. In the MPX NYC Participant–Community Network, each gathering is represented as an edge connecting the participant to the community district in which the event took place.

Event Type

To contextualize the settings of reported gatherings, participants were asked to select the type of event for each location where physical or sexual contact occurred. Response options included dance party, sex party, darkroom, sports event, concert, theatre or show, private residence, sex club, bathhouse, park, or another type of event.

Sexual Contact

For each reported gathering, participants indicated whether sexual contact occurred. This measure helps distinguish between gatherings that primarily served social functions and those involving sexual activity, allowing for comparisons across event types and contexts.

Distance from Home

We constructed an approximate measure of distance from home to describe how far participants traveled between their residence and each gathering location. Gatherings were categorized as:
1. Same community district as the participant’s residence,
2. Different community district but within the same borough, or
3. Different borough entirely.

This measure provides insight into how spatial mobility shapes opportunities for contact and potential transmission.

4.4 Network Characteristics

Size of the Largest Connected Component

In the MPX NYC Participant Network, overall connectedness was measured using the largest connected component (LCC)—the subset of nodes in which every participant can be reached from every other participant through one or more existing network ties. The proportion of all nodes contained in the LCC serves as a measure of how cohesively the network is connected: a larger LCC indicates greater overall connectedness among participants.

Spatial Mixing Ratio

We assessed spatial patterns of interaction between distinct subgroups of participants using the spatial mixing ratio. This measure quantifies the extent to which individuals from one group tend to live or gather in the same community districts as individuals from another group.

Formally, the spatial mixing ratio is a positive real number greater than 1 when members of the first subgroup are more likely than expected to share community districts with members of the second subgroup. It is calculated as the average (across all individuals) of each person’s spatial preference for the second group—defined as the proportion of their total edge weights that connect to members of that group—divided by the group’s overall prevalence in the network.