BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages

 
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
BRINGING MODELS DOWN TO EARTH
Locally grounded network models for
supporting HIV policy planning
          Martina Morris
          Deven Hamilton, Jeanette Birnbaum
          Susan Buskin, Roxanne Kerani, Sara Glick, Tom Jaenicke

          UW Network Modeling Group
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
Start by acknowledging my collaborators

Modelers                                      Epidemiologists
   UW Network Modeling Group                     Joint UW/PHSKC
    Deven Hamilton                                Sarah Glick
    Jeanette Birnbaum                             Roxanne Kerani

             Advisory Board
                PHSKC – Amy Bennett, Susan Buskin, Katelyn Gardner Toren
                WA DOH – Jason Carr, Tom Jaenicke
                UW – Matt Golden, David Katz

                            Funding: NIAID R21

                                   NME 2018                                 2
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
Outline

   Project overview
   Model structure … and data sources
     Demography
     Transmission system
     Care continuum and clinical outcomes

   Epidemic results
     Preliminary – first set of runs 

                              NME 2018       3
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
Project goals

   Build locally grounded projection model to support HIV policy
    Models have traditionally been built at the country level
     But there is significant variation in HIV prevalence within countries
     And in the US, prevention happens at the State/Local level

   Start with the heterosexual epidemic in King County
    Why?
     Small, but potential for eradication
     First step towards a more comprehensive model
     It’s a challenge…

                                   NME 2018                                   4
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
Heterosexual cases of HIV
Measured with Uncertainty

             New HIV Diagnoses in King                In King County
                County: 2011-2016
350
                                                          6-7% of incidence is
300                                                        attributed to
250
                                                           Heterosexual contact
200                                                       Another 15-20% is
                                                           “No Identified Risk”
150

100
                                                          Total range: 6-27%
50

 0

      2011     2012    2013   2014      2015   2016
                                                      In Southeast US
                                                          As high as 30%
                      Total   Het/NIR                      Heterosexual

                                           NME 2018                               5
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
Race and Immigration in KC HIV

    HIV Prevalence: 2016

                   Estimated HIV
                                               US Born       Foreign Born
                  Prevalence/100K

   White                 314.2                   93%              2%

   Black                 1001.0                  56%              41%

   Hispanic              434.6                   42%              52%

    We see large racial disparities          And profound differences in
                                             country of origin by race

                                  NME 2018                                  6
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
Importance of local MSM epidemic

   MSM comprise the largest group of HIV diagnoses

   Several papers have shown evidence that there is substantial
    transmission across subpopulations
     Based on phylogenetic clustering of HIV sequence data

   In the US: Oster et al. (2015)
      “Of heterosexual women for whom we identified potential transmission
      partners,
       29% were linked to MSM,

       21% to heterosexual men...

       a higher percentage of women in the West (52%) were linked to MSM”

                                  NME 2018                                   7
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
What this suggests

   Ongoing transmission in the heterosexual population
     could be below the reproductive threshold
     sustained by cross-boundary transmissions?

   This has implications for targeting prevention policy
     Target the boundary to have the maximum impact

                            NME 2018                    8
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
9   The modeling framework
    Dynamic network foundation (statnet)
    Epidemic model components (EpiModel)

                    NME 2018
BRINGING MODELS DOWN TO EARTH - Locally grounded network models for supporting HIV policy planning UW Network Modeling Group - GitHub Pages
Key components of our framework

   Transmission system
     Dynamic partnership network                      Handled by
         Multilayer: Cohab, Persistent and one-time
          partnerships                                 statnet
         Behavior within partnerships
     Transmission
         Function of viral load/stage of infection

   Care continuum                                     Handled by
     Testing, treatment, viral suppression            EpiModel

   Demography
     Aging
     Travel
     Entry/Exit                     NME 2018                       10
Dynamic network model(s)

              Partnerships modeled with a STERGM
              • Formation ERGM
              • Dissolution ERGM
              • Estimated from egocentrically sampled data

              3 different types of partnerships
              • Cohabiting
              • Persistent    So three different STERGMs
              • One-time

                NME 2018                               11
Transmission system components

 Several processes are overlaid, and interact with the network

Within discordant                                                 Boundary exposure
partnerships:
Behavior                                                                       Foreign
                                        FB                                      Travel
• Coital Frequency
• Condom Use                                               Force of
                                                           infection
Infectivity, by                 MSMF
• Stage                                                                     Local MSM
• CC engagement
• Clinical outcome
                                             FB: Foreign Born
                                             MSMF: Males who have sex with both males and females

                             NME 2018                                                               12
So our population has multiple subgroups

   Race / Immigration subgroups (5)
     US and foreign-born Black
     US and foreign-born Hispanic
     Other (predominantly White)

   Sex / Sexual preference subgroups (3)
     Female (F)
     Males who have sex with females only (MSF)
     Males who have sex with males and females (MSMF)

   And age…
                              NME 2018                   13
Lots of other model components

   Engagement in Care
       HIV Testing (at sex and race-specific rates, some never test)
       Treatment with ARVs
       Adherence, with episodes of drop and return
       Viral Suppression (some fraction are not full suppressors)

   Clinical progression after infection
     4 stages (acute rise, acute fall, chronic, AIDS)
     Progression time: 6.4 wks, 3 wks, 10 yrs, 2 yrs
     Stage-specific viral load (influences infectivity)

   Demography: Open population model
     Entry at 18, exit at 45
     Age and AIDS specific mortality rates
     Travel for foreign born (pauses local sexual activity, activates boundary exposure)

                                          NME 2018                                      14
15   Data sources
     Locally sourced, … when possible

                        NME 2018
Model components: LOCAL DATA NEEDED

Model Component                  Governs:                          Source
Sexual network                   Partnership                       NSFG (18-45)
                                 formation/dissolution
                                 dynamics
Behavior within partnerships     Coital frequency, condom use,     NSFG (18-45)
                                 HIV status disclosure
Natural history of within-host   Viral load, CD4, symptoms and     Global Estimates
HIV infection                    infectivity
Clinical care cascade            Testing, referral, adherence,     PHSKC HIV Core Surveillance
                                 suppression
Demographics                     Entries and Exits into the        King County Census
                                 population (pop’n growth,
                                 mortality and in/out migration)

                                              NME 2018                                           16
US Data on sexual behavior

   National Survey of Family Growth (NSFG, 2006-15)
     Representative national sample with annual waves
     Age 15-45
     Egocentric data on last 3 heterosexual partners
            Partner attributes (age, race/immig, cohab, duration/once only, ongoing)
            Behavior within partnerships
     HIV testing rates

                        Combined sample size: ~40K

       Reweighted by age, sex, race/immigration group
            To match King County demographics

                                          NME 2018                                      17
Local data on travel back to home country:
collected in public health interviews of new HIV cases

                                             Added in
                                              2010

                                             But only for newly
                                             diagnosed HIV cases

                            NME 2018                               18
Local “Cascade of Care”

       We use sex/race specific values
                     NME 2018            19
20   Some descriptive statistics
     Population attributes
     Partnerships patterns by subgroup

                       NME 2018
KC Demographics by race/imm/sex

   King County is predominantly white
   About 15% of the population is Black or Hispanic
     And half of those are foreign born

                                  NME 2018             21
KC Sex Group estimates
by Age and Race

About 1% of the population are MSMF
   Based on NSFG, reweighted by age/race/sex to KC

                    49% 50%         50% 50%

                  50%         49%

                                               1% 1% 1%

                                    NME 2018              22
Partnership Type Prevalence
by Age, Sex, and Race

        Cohab           Persistent       One Time

                                                      Race

                                                    Sex

                             Age
                              NME 2018                       23
Age Mixing

            Cohabiting         Persistent         One time
Ego Age

                                Alter Age

           Strong age homophily for all types of partnerships
                                 NME 2018                       24
Partnership Durations: Cohab & Persistent
by Race

                    AGE OF ACTIVE TIES
      Cohab:                    Persistent:
      average 10-15 years       average 2 – 4 years

                             NME 2018                 25
Mixing by Race/Immigration group

                               Cohab                           Persistent
Ego Race/Immigration group

                                       Alter Race/Immigration group

                                               NME 2018                     26
Overlapping partnership networks

At any point in time, a person can have none, or some of each partnership type
   About 1-2% of the population has two or more concurrent partners
                                                Sex
       Cohabiting partners
     Cohab

                                                          1.2    9.6    0.0     Rate of 1
                             0.1   2.5    0.0
                                                                               time partners
                                                                               per 100
                                                                               persons
                             4.1   1.9    2.2             23.2   15.3   13.1

                                         Persistent partners
                                    # Persistent Partners
                                             NME 2018                                 27
Concurrency by sex group

                                          Highest overall rates are in
2.5                                        one of the boundary
                                           populations: MSMF ~2%
2.0
                                            About half of this is cross-
1.5                                          network

1.0                                       This is just the concurrency
                                           with opp sex partners
0.5
                                            45% of the MSMF also have
0.0                                          M partners during the year
      F      MSF       MSMF
      Any   Cross-Network                 Lowest overall rates are
                                           among women: ~0.5%

                            NME 2018                                        28
Concurrency by race/immigration group

                             Female                                         Male
          5                                                5

          4                                                4
percent

          3                                                3

          2                                                2

          1                                                1

          0                                                0
                B      BI         H          HI   W              B   BI         H          HI   W

                            Any   CrossNet                                Any   CrossNet

             Highest rates are for Black, Hispanic and Hispanic immigrant men ~4%
               Mostly multiple persistent partners for Black men
               Mostly cross-network for Hispanic immigrants
               Split equally for US born Hispanics
             Black women have slightly higher rates among females : ~2%
                                                      NME 2018                                      29
Concurrency: By age

                             This is a young
                              person’s game
                               Highest rates for young
                                men: 3-7%

                             But the configuration
                              changes with age too
                               The cross-network
                                fraction rises, as rates
                                of cohabitation rise

               NME 2018                                    30
Boundary force of infection

                                          Boundary Groups:
                              BI                        HI                        MSMF
Percent of
                             2.3%                     5.4%                         1.5%
population
Exposure                         Depart: 0.01
probabilities                                                               2.5 partners/yr
                                  Return: 0.25
HIV acquisition          F: 2.0e-04          F: 2.0e-05
probabilities *                                                                  7.2e-06
                         M: 1.0e-04          M: 9.9e-06

* The HIV acquisition probabilities are    For example for MSMF:
                                          MSM prevalence x
a function of several components, and     condom use (.304) x efficacy (cond.rr=.4) x
determine the FOI at the boundary         P(transmission | contact) (((.0082*1.09)+(.0031*1.09))/2) x
                                          P(contact per week) (2.5/52)

                                      NME 2018                                                  31
Much uncertainty about boundary inputs

   So, we will use these for model calibration

     At this stage, by just manually trying some values
     Multiplying the FOI by a factor

     Later: we have a better plan

                             NME 2018                      32
33   ERGM Results

               NME 2018
Formation models for each network
                                     Cohab          Pers       OT
  Age                                    -0.87         -0.20    -0.38
  Age2                                    0.02          0.00     0.01
  Age Diff                               -3.22         -2.59    -2.40
  Race (main)       Black                   1.10       1.14      0.42
                    Black Imm               1.14       1.61     -0.52
                    Hispanic                3.17       2.00      0.52
                    Hisp Imm                1.63       1.13     -0.78
  Race (matching)   Black                   3.35       3.21
                    Black Imm               3.85       2.86
                    Hispanic                0.01       0.27
                    Hisp Imm                2.88       2.30
                    White                   3.14       2.17
  Concurrency       Cross net               -5.96     -4.36
                    Within net               NA        -2.85
                                 NME 2018                               34
Model assessment: Convergence

                           This is what you
                           want to see

                           But we found it
                           requires a very
                           long MCMC
                           interval (1e5)

               NME 2018                35
Model assessment: Network fidelity

   We want the dynamic simulations to reproduce the
    observed network statistics (on average)
     Degree distributions                 ERGMS should be
         Within and between networks      able to reproduce
         By sex, age, race/imm            the joint distribution
                                           of all of the network
                                           statistics
     Mixing patterns
         By age, race/imm                 in each model

     Partnership durations

                                NME 2018                            36
Persistent network: All model stats

                            Good fit to observed…

                 NME 2018                           37
Example: Persistent degree by race

Good fit to observed, even though the
degree terms are not in the model

                                   NME 2018   38
Durations by partnership type

                                These also look
                                roughly like the
                                observed stats

                                … But there is
                                more of a story
                                here

                NME 2018                       39
40   Epidemic results
     Now we can simulate the epidemic,
     on a network that we know closely represents
     the observed data

                       NME 2018
First things first

   Even with a simulation size of 50,000 nodes

     The smallest groups are
Persistence and equilibrium

                           1. We can sustain an
                              epidemic

                           2. And we are “in the
                              ballpark”

                               KC Observed Prevalence:

                               Het only: 0.006
                               Het+NIR: 0.012

                NME 2018                            42
Prevalence by race & immigration status

               Simulation                         Observed KC prevalence

                                          Black
                                          Immigrant

 Here, the rank order matches the observed pattern

 But the BI prevalence is too high (by an order of magnitude)

                               NME 2018                                    43
Prevalence by sexual partner group

           Simulation
                                   Here the rank order
                                   is correct

                                   And the prevalence
                                   for Females and MSF
                                   are about right

                                   Working on
                                   estimating the true
                                   local prevalence is
                                   for MSMF, current
                                   estimate is ~30%

                        NME 2018                         44
Infections by source

       Infections from
                                               No persistence
       original local      Boundary            without
       heterosexual        Infections          continual
       seeds                                   infections across
                                        MSMF
                                               the boundary

                                               And the primary
                                               contribution is
                                               via MSMF

                                          Downstream
                                          Infections

                         NME 2018                            45
A note on workflow

   The project is managed on GitHub
     Code and data repository
     Organizing issues with Projects

   We’re keeping a lab book using markdown/html
     Exploratory data analysis: bookdown
         Records both the descriptives and our decisions
     Model results

   It’s still a bit overwhelming …
                                  NME 2018                  46
47   What’s next?
     Model Calibration against Local Phylogenetics

                        NME 2018
From another project here at UW

   Phylogenetic analyses of local HIV diagnoses
                                White
                                Black
                                Asian
                                          • B Clades predominate in
                                Latino      the US

                                          • Non-B clades predominate
                                            in Africa and Asia

                                         They form a distinctive
                                         cluster here,
                Non-B
                                         predominantly black

                          NME 2018                                    48
Herbeck & Kerani

   Phylogenetic analyses of local HIV diagnoses

                            MSM
                            Heterosexual

                                           They form a distinctive
                                           cluster here,
                 Non-B
                                           And predominantly
                                           heterosexual
                          NME 2018                                   49
An empirical foundation for calibration

   Directly relevant for calibrating our most uncertain
    parameters – the FOI across the boundaries

   And separate from HIV incidence and prevalence
    data
     So those can be preserved for model validation

Deven Hamilton is taking the lead on this project

                            NME 2018                       50
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