Learning on Forecasting HIV Epidemic Based on Individuals’ Contact

Networks

Chaoyue Sun

1 a

, Yiyang Liu

2 b

, Christina Parisi

2 c

, Rebecca Fisk-Hoffman

2 d

, Marco Salemi

3,7 e

Ruogu Fang

1,4,5,7 f

, Brandi Danforth

, Mattia Prosperi

2,7 g

and Simone Marini

2,7,∗ h

Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida,

Gainesville, FL, U.S.A.

Department of Epidemiology, College of Public Health and Health Professions and College of Medicine,

University of Florida, Gainesville, FL, U.S.A.

Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville,

FL, U.S.A.

J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering,

University of Florida, Gainesville, FL, U.S.A.

Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, U.S.A.

Florida Department of Health, 4025 Esplanade Way, Tallahassee, FL, U.S.A.

Emerging Pathogens Institute, University of Florida, Gainesville, FL, U.S.A.

ﬁ ﬂ ﬂ ﬂ

Brandi.Danforth@ﬂhealth.gov, {m.prosperi, simone.marini}@uﬂ.edu

Keywords:

Epidemiology, Contact Networks, Machine Learning, Graph Learning.

Abstract:

Improving the diagnosis of HIV is a fundamental objective of the Ending the HIV Epidemic initiative, as it

represents the initial step toward treatment and achieving undetectable status, thereby reducing transmission.

To attain these objectives effectively, it is crucial to identify the groups most susceptible to HIV, allowing

interventions to be tailored to their speciﬁc needs. In this study, we developed a predictive model designed

to assess individual HIV risk within a high-risk contact network – predicting treatment or at-risk – leverag-

ing surveillance data collected through routine HIV case interviews in Florida. Unique to our analysis, we

explored the incorporation of behavioral network information with Graph Neural Networks to enhance the

predictive capacity for identifying individuals within the treatment or intervention categories, when compared

to models that mainly consider conventional HIV risk factors. Our deployed Graph Isomorphism Network

achieved 77.3% and 73.2% balanced accuracy in inductive and transductive learning scenarios respectively,

outperforming the traditional prediction algorithms that do not leverage the network structure. We then used

our model to further investigate the importance of demographic and behavioral factors in the HIV risk pre-

diction process. Our ﬁndings provide valuable insights for healthcare practitioners and policymakers in their

efforts to combat HIV infection.

1 INTRODUCTION

Improving diagnosis of HIV is a key pillar of the End-

ing the HIV Epidemic (EHE) initiative, as knowing

https://orcid.org/0000-0003-0913-5668

https://orcid.org/0000-0002-5519-3853

https://orcid.org/0000-0002-2546-507X

https://orcid.org/0000-0002-2421-2601

https://orcid.org/0000-0003-0136-2102

https://orcid.org/0000-0003-3980-3532

https://orcid.org/0000-0002-9021-5595

https://orcid.org/0000-0002-5704-3533

∗

Corresponding author

the HIV status of someone is the ﬁrst step in get-

ting them treated and for their HIV infection to be-

come undetectable (i.e., a viral load of less than 200

copies of HIV per milliliter of blood), which in turn

decreases transmission (Fauci et al., 2019). How-

ever, the resources do not exist to universally scale

up testing in healthcare facilities and beyond; fur-

thermore, current testing guidelines are often not fol-

lowed. Having a greater understanding of who is re-

cently acquiring HIV, where they are, and how they

are acquiring HIV, is key to the implementation of

effective and contextually- and culturally-appropriate

testing and interventions. Another EHE pillar is to

Sun, C., Liu, Y., Parisi, C., Fisk-Hoffman, R., Salemi, M., Fang, R., Danforth, B., Prosperi, M. and Marini, S.

Learning on Forecasting HIV Epidemic Based on Individuals’ Contact Networks.

DOI: 10.5220/0012375400003657

In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 2, pages 103-111

ISBN: 978-989-758-688-0; ISSN: 2184-4305

103

provide rapid response resources to real-time out-

breaks, including deployment of prevention and treat-

ment tools to those in need. In order to identify and

promptly respond to potential outbreaks, cutting-edge

precision public health approaches are needed. These

approaches enable us to not only identify emerging

transition networks and high-risk communities but

also strategies to coordinate a rapid and targeted re-

sponse. Stakeholders, (e.g., public health and reg-

ulatory agencies, the scientiﬁc community, health

care professionals) can enact culturally- appropriate,

evidence-based best practices, including encouraging

pre-exposure prophylaxis (PrEP) uptake, creating sy-

ringe exchange programs, or developing new inter-

ventions. To achieve these goals, it is important to

understand the groups most impacted by HIV so in-

terventions can be tailored to meet their needs.

Florida is a diverse state that is also one of the

states most affected by HIV in the United States. It

consistently has among the highest rates of HIV inci-

dence in the nation (Centers for Disease Control and

Prevention, 2023). In 2019, seven counties in Florida

were selected as geographic priority areas in the EHE

initiative: Miami-Dade, Broward, Palm Beach, Or-

ange, Duval, Pinellas, and Hillsborough (Fauci et al.,

2019; Florida Department of Health, 2020). While

many of these high incidence and high prevalence

counties are urban, rural areas in Florida also are

heavily impacted by HIV (Florida Department of

Health, 2023; Trepka et al., 2013). Florida expe-

riences varying levels of socioeconomic status and

is impacted by tourism, seasonal residents, and mi-

gration. A variety of structural and social barriers

can prevent people with HIV (PWH) from receiv-

ing HIV care or receiving testing in urban compared

with rural areas. Additionally, stigma and discrimi-

nation surrounding HIV as well as racial/ethnic, gen-

der, and sexual identities can vary by location, within

groups, and across cultures, and be substantial barri-

ers to knowledge about HIV, engagement in HIV pre-

vention behaviors, and receipt of HIV healthcare (Tan

et al., 2023). Similar to the rest of the nation, non-

Hispanic Black and Hispanic men who have sex with

men (MSM) and non-Hispanic Black women are dis-

proportionately impacted by HIV in Florida (Florida

Department of Health, 2017; Lieb et al., 2010; Wright

et al., 2022; Liu et al., 2023). Given these circum-

stances, not only is our research well-suited to be

done using Florida data, but our work has the poten-

tial to help inform and streamline public health efforts

given the diversity of PWH living in the state.

Deep learning methods, particularly Graph Neu-

ral Networks (GNNs), have garnered signiﬁcant re-

search attention in epidemiology in recent years.

GNNs are speciﬁcally designed for tasks that incor-

porate graph topology as an additional input, allow-

ing them to learn representations by exchanging in-

formation among neighboring nodes (Zhang et al.,

2018; Zhou et al., 2020; Wu et al., 2020). The appli-

cation of GNNs in epidemiological prediction tasks

has gained momentum due to the inherent connec-

tions between geolocation and temporal dynamics.

Notably, GNNs have demonstrated their effectiveness

in capturing patterns for a range of epidemiological

predictions, including the prediction of inﬂuenza-like

illness (Deng et al., 2020) and forecasting COVID-

19 cases (Kapoor et al., 2020; Ramchandani et al.,

2020; Wang et al., 2022). Approaches developed by

our team (Sun et al., 2022; Sun et al., 2023) are se-

quentially designed to predict the transmission dy-

namics of risk groups in disease transmission net-

works inferred by phylogenetic trees, while other au-

thors (Tomy et al., 2022) developed methods to recon-

struct the underlying social network structure with the

health status of involved patients.

The purpose of this study is to present a predictive

model designed to assess individual HIV risk within

a high-risk contact network (i.e., predicting individ-

uals in treatment or at risk), leveraging surveillance

data collected through routine HIV case interviews

in Florida. We will explore how the incorporation

of behavioral network information enhances the pre-

dictive capacity of the model when compared to a

model that mainly considers conventional HIV risk

factors, such as the diagnosis of sexually transmitted

infections (STIs) and the sharing of injection drug use

equipment. We will then investigate the importance of

demographic and behavior factors in the well-trained

models, providing insights for healthcare practition-

ers and policymakers in their efforts to control HIV

infection.

2 MATERIALS

2.1 Dataset Collection and Description

The Florida Department of Health (FDOH) man-

ages the Surveillance Tools and Reporting System

(STARS). Disease intervention specialists (DIS) at-

tempt to contact individuals who have recently been

diagnosed with a reportable sexually transmitted in-

fection, including those newly diagnosed with HIV,

for an interview. Among those who are able to be

reached, FDOH staff will identify recent risk behav-

iors, such as sexual and/or needle-sharing contacts

who are at higher risk for HIV transmission, during

the interview and then input this data into STARS.

HEALTHINF 2024 - 17th International Conference on Health Informatics

104

STARS data does not include data on prevalent cases

nor does it follow incident cases over time. FDOH

staff also collected information about the intervie-

wee’s demographics and potential exposures to HIV,

including past behaviors that increase the risk of con-

tracting HIV. They then attempted to notify the iden-

tiﬁed contacts to encourage them to be tested for HIV.

If the contact was subsequently diagnosed with HIV,

that individual would then be interviewed to elicit ad-

ditional contacts and collect further information on

their demographics and risk factors.

The STARS dataset used in this study contains in-

formation on PWH who received an HIV diagnosis

in Florida between 2000 and 2023, as well as their

reported contacts. We were provided with a version

of the dataset comprising 95,034 records related to

67,727 individuals. Through interviews, FDOH doc-

umented 77,984 pairs of contact relationships, and we

excluded 16, 355 individuals who lacked any contact

within the dataset (i.e., singletons). Consequently, the

dataset is organized into 14,978 distinct networks,

with network sizes varying between 2 and 50 indi-

viduals, except for one largest network containing

10,509 individuals.

2.2 Dataset Preprocessing

In our data preprocessing phase, we retained duplicate

records that were closest in timestamp to the creation

of the corresponding network link. For instance, if

multiple records existed for the same individual from

different years, we retained the record closest to the

time when all links connected to the node were estab-

lished, prioritizing temporal accuracy.

We identiﬁed 12 demographic and behavioral fea-

tures: gender, age, race, ethnicity, marital status, pres-

ence of needle-sharing partners, results of syphilis

tests, sexual orientation, history of sexually transmit-

ted diseases (STDs), engagement in MSM relations,

residence in the seven counties designated as EHE

Initiative, and number of sexual partners, as illus-

trated in Table 1. Data labeled as self-reported in-

formation, if available, took precedence over data en-

tered by clinical staff. For example, 205 individuals

were recorded as males, while a self-reporting gender

variable indicated transgender (male to female). We

considered them as transgender in our prediction. The

interview records indicate that a small portion of in-

dividuals refused to answer certain questions, such as

those related to ethnicity and a history of STIs. There-

fore, we treat this situation as a distinct category sep-

arate from missing data issues. To address missing

data, we employed the MissForest (Stekhoven and

uhlmann, 2012) algorithm for data imputation, en-

suring the integrity and completeness of our dataset.

For the continuous variables, namely age and num-

ber of sexual partners, we applied z-score normaliza-

tion, standardizing each feature to have zero means

and unit variances. Conversely, for the remaining cat-

egorical features, we employed one-hot encoding for

their representation. It is noteworthy that we decided

to encode the presence of needle-sharing partners (a

binary categorical feature) rather than using the actual

number of reported needle-sharing partners (a numer-

ical one) in contrast to the number of sexual partners.

This decision stemmed from the fact that the majority

of needle-sharing partners counts, as evident in Ta-

ble 1, equate to zero. Utilizing z-score normaliza-

tion in this context would fail to effectively capture

distinctions among the numbers due to their predom-

inantly skewed distribution. Finally, each individual

was represented by 33 features after the preprocess-

ing phase.

There is an imbalanced distribution issue on net-

work size posed by the exceptionally large network

consisting of 10,509 individuals, resulting in a low

learning efﬁciency of models. To address this is-

sue, we employed the Leiden graph partitioning algo-

rithm (Traag et al., 2019), which was applied with de-

fault hyperparameters, primarily leveraging network

connectivity. The outcome of this partitioning process

yielded a total of 86 sub-networks, each comprising a

more manageable size, ranging from 43 to 290 indi-

viduals.

In terms of forecasting targets, we categorized the

population into two groups: treatment (comprising in-

dividuals diagnosed as HIV-positive) and prevention

(encompassing individuals not diagnosed as positive

but involved in interviews). Within our dataset, the

majority group consists of 51,372 (∼ 97.9%) individ-

uals classiﬁed as treatment (i.e., PWH), while the re-

maining 1,076 (∼ 2.1%) individuals are considered at

risk (i.e., HIV status negative or unknown).

3 METHODOLOGY

3.1 Learning Tasks

In our study, our primary focus was on the prediction

of HIV risk among individuals within a high-risk con-

tact network. We leveraged our set of 33 preprocessed

features in conjunction with network topology data.

We conducted our investigation through two distinct

learning tasks. The ﬁrst task is named as inductive

learning, where our models were trained using a por-

tion of the networks and subsequently tested on en-

tirely new networks that were not part of the training

Learning on Forecasting HIV Epidemic Based on Individuals’ Contact Networks

105

Table 1: Demographic features of HIV epidemic data in Florida.

Treatment Prevention Σ

n 50,296 1,076 51,372

Gender (in %)

Male/Female/Other/Null

74.7/24.8/0.44/0.05 89.8/9.76/0.09/0.37 75.0/24.5/0.44/0.05

Age (in years)

mean±std

34.63 ± 12.05 29.98 ± 9.51 34.53 ± 12.02

Race (in %)

A/B/I/P/W/O/Null

0.63/51.6/0.17/0.11/

40.6/3.79/3.14

0.65/47.7/0.09/0.00/

36.8/4.83/9.94

0.63/51.5/0.17/0.11/

40.5/3.81/3.28

Ethnicity (in %)

H/NH/R/Null

19.5/75.1/0.17/5.20 17.1/70.1/0.09/12.7 19.5/75.0/0.17/5.35

Marital status (in %)

A/M/S/W/Null

0.52/6.51/30.1/0.24/62.7 0.00/1.49/28.0/0.09/70.4 0.51/6.41/30.0/0.24/62.8

Having needle partners (in %)

Yes/No

0.93/99.1 0.00/100.0 0.91/99.1

Syphilis test (in %)

Positive/Negative/Other/Null

13.2/8.69/2.66/74.4 5.86/2.79/19.2/72.1 13.1/8.57/3.99/74.4

Sexual orientation (in %)

Bisexual/Gay/Straight/Null

3.01/28.5/23.3/45.3 2.23/22.8/5.58/69.4 3.00/28.3/22.9/45.8

History of STDs (in %)

Yes/No/Refused to answer/Null

15.2/25.1/0.47/59.2 0.00/0.00/0.00/100.0 14.9/24.6/0.46/60.0

MSM (in %)

Yes/No

39.2/60.8 20.3/79.7 38.8/61.2

EHE (in %)

Yes/No

64.8/35.2 67.8/32.2 64.8/35.2

Number of sexual partners

mean±std/Null (in %)

4.14 ± 35.85/53.6 0 ± 0/100.0 4.14 ± 35.85/54.6

Other includes transgender populations.

A: Asian, B: Black or African American, I: American Indian or Alaska Native, P: Native Hawaiian or Other Paciﬁc Islander, W: White, O:

Some other races.

H: Hispanic or Latino, NH: Not Hispanic or Latino, R: Refused to answer.

A: Attached, M: Married, S: Single, W: Widowed.

Other includes people who are deceased, out of jurisdiction, not recorded tests and administrative closeout.

set. In the second task, known as transductive learn-

ing, we constrained the training and validation dataset

to networks that existed between the years 2010 and

2018 while excluding nodes falling outside this tem-

poral range. Subsequently, the model was rigorously

evaluated on nodes introduced after the year 2018. To

facilitate the model learning about transmission pat-

terns from recent years, we excluded the data before

the year 2010 and only kept the corresponding net-

work topology.

We incorporated three traditional machine learn-

ing approaches as baseline models for our study.

These models were trained and evaluated using the

same training, validation, and testing datasets as the

graph-based approach. The only difference lies in the

input data, with baseline models exclusively utilizing

the 33 preprocessed features for prediction, but not

the network topology information.

3.2 Traditional Machine Learning

Approaches

In our investigation, we assessed the performance of

three traditional machine learning methods, i.e., lo-

gistic regression (LR) (Hosmer Jr et al., 2013), deci-

sion tree (DT) (Von Winterfeldt and Edwards, 1986)

and random forest (RF) (Breiman, 2001). LR aims

to ﬁt a generalized linear model while minimizing the

sum of squared loss between node labels and linear

approximations, with inclusion of L2 regularization

for overﬁtting prevention. DT is an interpretable al-

gorithm often used for tabular data classiﬁcation, cre-

ating a hierarchical set of rules through node-based

decisions. RF, on the other hand, is a robust en-

semble technique that constructs multiple decision

trees on bootstrapped subsets of training data and ag-

gregates their outputs for improving predictive accu-

racy. These methods were selected for their versatil-

ity and effectiveness in various machine learning ap-

plications, offering a range of complexity levels for

comparison in our study.

3.3 Graph Learning Approaches

The advantage of GNNs is utilizing the graph (a.k.a.

network) topology information to learn the node rep-

resentations. Following a general framework (Xu

et al., 2019), we provide a GNNs’ learning mecha-

HEALTHINF 2024 - 17th International Conference on Health Informatics

106

nism for node i with the Eq. 1 and 2 for the HIV risk

forecasting learning task.

= ρ

N(i)→i

l−1

), j ∈ N(i) (1)

= φ(m

l−1

) (2)

Here v

stands for the representations of node i in

layer l. For the ﬁrst layer, node representations are

initialized as the 33-dimension preprocessed features.

N(i) denotes the neighbor nodes of node i in con-

tact network and function ρ(·) describes that node

i’s neighbors generate a message vector m

based on

their representations and send it to node i. φ(·) is the

representation updating function, taking the message

vector m

and previous node representations v

l−1

inputs. This process is called the message-passing

mechanism and it guides the network neighbors to

learn similar representations for the ﬁnal prediction.

After iterative updating across layers, the ﬁnal repre-

sentations are used for the prediction task through a

fully-connected layer:

= σ(W

+ b

) (3)

where s

∈ R

represents the predicted score of C

classes and L is the number of GNN layers. W

and

are learned parameters.

Inspired by spectral graph theory, Graph Convo-

lutional Networks (GCN) (Kipf and Welling, 2017)

was proposed by generalizing the Convolutional Neu-

ral Networks to graphs. Formally, a layer-wise prop-

agation rule is deﬁned as:

∑

j∈{N(i),i}

l−1

/C (4)

= σ(m

+ b

) (5)

Here W

and b

are learned parameters in the l-th

layer. σ(·) is an activation function and we used

LeakyReLU in our experiments. C is a normalization

constant corresponding with the neighbor size.

Xu et al. proposed the GIN variant (Xu et al.,

2019), which supposedly achieves the maximum dis-

criminative power among graph neural networks. It

uses a multilayer perceptron (MLP) model in the

message-passing process:

∑

j∈N (i)

l−1

(6)

= MLP

((1 + ε

) · v

l−1

+ m

) (7)

where ε is a learnable parameter. Different from gen-

erating whole graph embedding as proposed in (Xu

et al., 2019), here we directly apply fully connected

layers on nodes’ features in each layer and generate

the ﬁnal prediction score by summation over random

dropout:

∑

l=1

Dropout(W

+ b

) (8)

In this work, we use both the GCN and GIN models.

3.4 Model Evaluation Metrics

We evaluate all models through four key performance

metrics: accuracy, precision, F1-score, and the area

under the receiver operator characteristic curve (AU-

ROC). To address the highly imbalanced label dis-

tribution within our dataset, we adopted a macro-

averaging strategy, which involves the initial calcu-

lation of each metric for each class individually, fol-

lowed by their equal averaging to yield the ﬁnal met-

rics. This approach can effectively avoid overestimat-

ing the models that only perform well on the common

classes while performing pooling on the rare classes.

3.5 Model Interpretation

To investigate how important each feature performs in

the prediction process, we calculated the permutation

importance (Altmann et al., 2010) of each feature in

the model evaluation phase. Permutation importance

entails the random shufﬂing of each feature within the

testing dataset, followed by an evaluation of the ex-

tent to which the model’s performance is affected. A

larger decrement in performance signiﬁes a higher de-

gree of importance for the respective feature.

4 EVALUATION

4.1 Experiment Settings

Because the dataset is heavily imbalanced, with

97.9% treatment individuals and 2.1% prevention in-

dividuals, we utilized a weighted cross-entropy loss

function for optimization, and the weights for each

class are calculated based on their inverses of class’s

prevalence, so that the minor class will be assigned

with higher weights. This will enforce the model to

learn to predict samples from minor class accurately

as opposed to simply classifying all samples into the

major class. An Adam optimizer was employed, ini-

tialized with a learning rate of 10

−3

. Furthermore,

we implemented a learning rate reduction strategy,

wherein the learning rate was reduced by 90% if the

validation loss did not improve for several consecu-

tive epochs until it reached a minimum value of 10

−6

Learning on Forecasting HIV Epidemic Based on Individuals’ Contact Networks

107

Table 2: Model performances on inductive learning task.

Acc Pr F1 AUROC

LR 0.708 0.512 0.420 0.782

RF 0.721 0.514 0.454 0.805

DT 0.723 0.513 0.425 0.783

GCN 0.727 0.514 0.437 0.789

GIN 0.773 0.521 0.487 0.837

The number of waiting epochs, i.e., patience, is dif-

ferent from different models. In the inductive learn-

ing task, we included all 86 sub-networks separated

from the largest network to the training set. The idea

is to avoid the data linkage because the sub-networks

from the same largest network might have potential

relationships and this will break the independence of

the testing dataset.

4.2 Inductive Learning Task

To explore the inﬂuence of network topology on our

prediction task, we conducted an evaluation of GNNs

within the framework of inductive learning. In the

case of LR, the best model employed an L2 regular-

ization parameter of 10

−4

. The top-performing RF

model comprised 50 ensemble estimators, each with

a maximum depth of 25, a minimum of 14 samples

required for leaf nodes, and a minimum of 6 samples

to split an internal node. As for the DT model, its op-

timal conﬁguration featured a maximum depth of 7,

a minimum of 10 samples for leaf nodes, and a min-

imum of 10 samples for split nodes. Regarding the

GNN variants, the most effective GCN model con-

sisted of 10 GCN layers, with a hidden vector size of

32 dimensions. This model underwent training with

a mini-batch size of 128. The GIN model, on the

other hand, utilized 10 GIN layers and a hidden vec-

tor size of 64 dimensions. It employed a 5-layer MLP

for message-passing and a dropout rate of 50%. The

mini-batch size for GIN was set to 256. For both mod-

els, the waiting patience is set to 50.

As illustrated in Table 2, both GNN models

demonstrated commendable performance when com-

pared to traditional machine learning models. Specif-

ically, the GIN model achieved a balanced accu-

racy of 77.3% and a macro-averaged AUROC of

0.837. Notably, GIN outperformed the Decision Tree

(DT), the top-performing model among traditional

machine learning approaches, by a substantial margin

of 6.91% in balanced accuracy and 6.90% in macro-

averaged AUROC. Both precision and F1-score met-

rics, due to the dataset’s inherent class imbalance, ex-

hibit some sensitivity to misclassiﬁcation of the ma-

jority class, resulting in relatively lower scores. Nev-

ertheless, GIN achieved improvements of 1.56% and

14.6% in Precision and F1-score, respectively, over

the baseline DT model. In the case of the GCN model,

while its performance was slightly less than GIN, it

still exhibited an accuracy of 72.7% and an AUROC

of 0.789, surpassing the Decision Tree (DT) by 0.55%

in accuracy and 0.77% in AUROC. These ﬁndings un-

derscore the signiﬁcant contribution of network topol-

ogy information to our prediction task. Furthermore,

they establish GIN as an effective model in compar-

ison to established baseline models, highlighting its

potential to enhance predictive accuracy.

4.3 Transductive Learning Task

In our quest to simulate a more challenging real-world

scenario, we narrowed our focus to networks estab-

lished between 2010 and 2018 for training and valida-

tion, with a subsequent evaluation of predictive per-

formance on records generated post-2019. The op-

timal conﬁguration for LR involved an L2 regular-

ization parameter of 10

−3

. In the case of RF, peak

performance was achieved with an ensemble of 120

estimators, each requiring a minimum of 21 samples

for leaf nodes and a minimum of 19 samples for split

nodes. For the DT model, the most effective settings

comprised a maximum depth of 7, a minimum of 15

samples for leaf nodes, and a minimum of 11 samples

for split nodes. Regarding the GNN variants, Both

GCN and GIN models featured 5 GNN layers with a

hidden vector size of 64 dimensions. The mini-batch

size was conﬁgured at 128. The waiting patience of

GCN is set to 15 and the value is 25 for GIN.

The outcomes, as outlined in Table 3, unveil a pro-

nounced decline in model performance compared to

the results presented in Table 2. This shift under-

scores the evolving dynamics of disease transmission

over time, implying that the data distribution is likely

to shift rather than remain stable. This dynamic nature

of the data distribution poses a signiﬁcant challenge

to the prediction task. Notably, the GIN model ex-

perienced a 5.3% drop in balanced accuracy, shifting

from 77.3% to 73.2%, while the GCN model faced a

3.58% decrement. In this context, RF exhibited su-

perior resilience to temporal shifts, displaying a rel-

ative decrement of only 2.50%, thus establishing its

supremacy among traditional machine learning mod-

els for this particular task and dataset. Despite the

challenges posed by shifting transmission patterns,

the GIN model remained at the forefront, delivering

a balanced accuracy of 73.2% and a macro-averaged

AUROC of 0.776. GIN surpassed RF by a margin

of 4.13% in balanced accuracy; and 1.17% in macro-

averaged AUROC. Further underscoring GIN’s dom-

inance, it also posted notable improvements of 0.95%

HEALTHINF 2024 - 17th International Conference on Health Informatics

108

Table 3: Model performances on the transductive learning

task.

Acc Pr F1 AUROC

DT 0.666 0.518 0.444 0.718

LR 0.670 0.518 0.440 0.745

RF 0.703 0.523 0.464 0.767

GCN 0.697 0.519 0.450 0.752

GIN 0.732 0.528 0.481 0.776

in precision and 3.66% in F1-score over the baseline

RF model. In contrast, the GCN model grappled with

limitations inherent to training on data with temporal

restrictions, ultimately yielding performance inferior

to RF in this context. These ﬁndings not only high-

light the temporal evolution of transmission patterns

but also reafﬁrm the efﬁcacy of the GIN model as a

predictive model, positioning it as a potent instrument

for bolstering predictive accuracy even in the face of

shifting epidemiological dynamics.

4.4 Interpretation of Learned GIN

Model

To address questions regarding the signiﬁcance of fea-

tures in predictive modeling and their variations be-

tween the inductive and transductive learning tasks,

we conducted an analysis of permutation importance.

This analysis was performed using the GIN models

from the inductive and transductive learning scenar-

ios respectively. To ensure robustness and reliability

in our assessment, we conducted the permutation pro-

cess 20 times for each feature, and the results are pre-

sented in Figures 1 and 2.

In Figure 1, the permutation importance analysis

reveals that having a history of STDs emerges as the

most inﬂuential factor in forecasting the risk of HIV

infection among individuals. This is followed closely

by gender, age, and MSM. Referring to the demo-

graphic and behavioral feature distribution in Table 1,

the treatment population has higher proportions of in-

dividuals who are of older age and who identify as

MSM. In the transductive learning task, the top four

important features, namely gender, MSM, having a

history of STDs, and ethnicity, underscore their sub-

stantial impact on the prediction task. Intriguingly, as

we limit the training dataset to records before 2019,

age assumes a comparatively diminished importance.

Instead, the model’s focus shifts towards sexual ori-

entation and ethnicity.

Notably, in both ﬁgures, individuals residing in

EHE-designated counties exhibit notably lower im-

portance. This observation may be attributed to a cou-

ple of factors. Firstly, the similarity in feature distri-

butions between the treatment and prevention popula-

tions, as emerging from Table 1, could reﬂect a bias

in the data collection process, particularly as inter-

views were conducted through contact tracing. Sec-

ondly, the embedding of county information within

network topology may contribute to the marginal im-

pact of shufﬂing the EHE feature on performance re-

duction. The same explanation comes from the low

importance of having needle partners and the number

of sexual partners.

Figure 1: Permutation feature importance of GIN model in

the inductive learning task. Features were ranked by the

permutation importance scores. The x-axis shows a relative

reduction value of balanced accuracy.

Figure 2: Permutation feature importance of GIN model in

transductive learning task. Features were ranked by the per-

mutation importance scores. The x-axis shows a relative

reduction value of balanced accuracy.

Learning on Forecasting HIV Epidemic Based on Individuals’ Contact Networks

109

5 CONCLUSIONS

In this work, we investigated the utility of GNNs in

the HIV risk forecasting task. Our results on inductive

and transductive learning tasks indicate that GNNs,

especially the GIN model, outperform traditional ma-

chine learning approaches. Our exploration of demo-

graphic and behavioral factors underscored the signif-

icance of certain variables–having a history of STDs,

gender, age, and MSM–in our predictive model.

In summary, our study highlights the potential of

embedding the contact network in enhancing the ac-

curacy of HIV risk prediction. We stress the impor-

tance of accounting for temporal dynamics and de-

mographic factors in predictive modeling for pub-

lic health applications. The ﬁndings presented here

might offer valuable insights for healthcare practition-

ers and policymakers as they continue their efforts to

combat HIV infection. Our model can inform the tar-

geted allocation of resources, providing understand-

ing beyond only knowing the demographics and lo-

cations of those newly diagnosed with HIV, for more

impactful intervention. This could allow stakeholders

to address critical EHE pillars more effectively.

Based on our experiments in the transductive

learning task, it becomes evident that capturing tem-

poral relationships poses a more intricate challenge

for predictions. In this context, the performance

of the GIN model did not reach the same level

of effectiveness observed in the inductive learning

task. This limitation highlights an avenue for po-

tential enhancement, suggesting the implementation

of dynamic GNNs (Skarding et al., 2021) to bet-

ter capture the evolving temporal dynamics across

the years. Another limitation is that the models are

trained and validated solely on the STARS dataset

from Florida. Their effectiveness on the datasets with

more heterogeneous sources from other regions and

demographics remains unexplored. Additionally, for

model architecture, a comparative analysis with exist-

ing GNNs utilized in forecasting tasks, e.g., (Kapoor

et al., 2020; Ramchandani et al., 2020; Wang et al.,

2022) is warranted for future studies.

6 CODE AVAILABILITY

The code we used to develop our models is at

https://github.com/lab-smile/HIV Risk Pred with an

MIT License and is written in Python.

ACKNOWLEDGEMENTS

The authors abide to the Declaration of Helsinki. The

study protocol was approved by the University of

Florida’s Institutional Review Board (IRB) and by

FDOH’s IRB (protocol #IRB201901041 and #2020-

069, respectively) as exempt. We received data ex-

tracts from FDOH’s STARS in a fully de-identiﬁed

format according to the Health Insurance Portabil-

ity and Accountability Act (HIPAA). For replication

purposes, a STARS data request to the FDOH can

be made according to state, federal regulations and

compliance with required ethical and privacy policies

(Research@ﬂhealth.gov), including IRB approval by

FDOH and execution of data user agreement. Re-

quests are independently reviewed by FDOH. We

would like to express our gratitude to Colby Cohen

and Jared Jashinsky from FDOH for their invaluable

assistance in preparing the STARS data for our analy-

sis, for their responsiveness to our inquiries regarding

the dataset, and for their instrumental role in facil-

itating the FDOH internal review and approval pro-

cess for our manuscript. This work was supported

in part by NIH grant R01AI145552, 1F31AA030518-

01, R01AI172875 and 1F31AA030733-01. The ﬁnd-

ings and conclusions in this work are those of the au-

thors and do not necessarily represent the views of the

FDOH.

REFERENCES

Altmann, A., Tolos¸i, L., Sander, O., and Lengauer, T.

(2010). Permutation importance: a corrected feature

importance measure. Bioinformatics, 26(10):1340–

1347.

Breiman, L. (2001). Random forests. Machine learning,

45(1):5–32.

Centers for Disease Control and Prevention (2023). Diag-

noses of HIV infection in the United States and de-

pendent areas, 2021. https://www.cdc.gov/hiv/statisti

cs/overview/ataglance\\.html.

Deng, S., Wang, S., Rangwala, H., Wang, L., and Ning,

Y. (2020). Cola-GNN: Cross-location attention based

graph neural networks for long-term ILI prediction.

In Proceedings of the 29th ACM International Con-

ference on Information & Knowledge Management,

pages 245–254. Association for Computing Machin-

ery. October 19-23, 2020.

Fauci, A. S., Redﬁeld, R. R., Sigounas, G., Weahkee, M. D.,

and Giroir, B. P. (2019). Ending the HIV epidemic: a

plan for the United States. JAMA, 321(9):844–845.

Florida Department of Health (2017). Epidemiological Pro-

ﬁle, Florida: Continuum of HIV Care by County,

2017. Retrieved March 30, 2018, from https://ww

w.ﬂoridahealth.gov/diseases-and-conditions/aids/sur

HEALTHINF 2024 - 17th International Conference on Health Informatics

110

veillance/epi-proﬁles/Epi\ Proﬁle\ Tables\ Florida

\ 2018\ Locked.xlsx.

Florida Department of Health (2020). Florida’s Uni-

ﬁed Ending the HIV Epidemic Plan [Division

of Disease Control and Health Protection Bu-

reau of Communicable Diseases HIV/AIDS Sec-

tion]. https://www.ﬂoridahealth.gov/diseases-and-

conditions/aids/administration/ documents/ﬂ-uniﬁed-

ehe-plan.pdf.

Florida Department of Health (2023). HIV, AIDS and

PWH Cases by Year and by County in Florida.

https://www.ﬂhealthcharts.gov/ChartsDashboards/rd

Page.aspx?rdReport=HIVAIDS.DataViewer&cid=986

Hosmer Jr, D. W., Lemeshow, S., and Sturdivant, R. X.

(2013). Applied logistic regression, volume 398. John

Wiley & Sons.

Kapoor, A., Ben, X., Liu, L., Perozzi, B., Barnes, M., Blais,

M., and O’Banion, S. (2020). Examining COVID-

19 forecasting using spatio-temporal graph neural net-

works. arXiv preprint arXiv:2007.03113.

Kipf, T. N. and Welling, M. (2017). Semi-Supervised Clas-

siﬁcation with Graph Convolutional Networks. In In-

ternational Conference on Learning Representations

(ICLR), Palais des Congr

es Neptune, Toulon, France.

April 24 - 26, 2017.

Lieb, S., White, S., Grigg, B. L., Thompson, D. R., Lib-

erti, T. M., and Fallon, S. J. (2010). Estimated

HIV incidence, prevalence, and mortality rates among

racial/ethnic populations of men who have sex with

men, Florida. JAIDS Journal of Acquired Immune De-

ﬁciency Syndromes, 54(4):398–405.

Liu, Y., Parisi, C., Fisk-Hoffman, R., Salemi, M., Viteri,

D., Prosperi, M., and Marini, S. (2023). Behavioral

and demographic proﬁles of HIV contact networks in

Florida. In International Conference on Intelligent Bi-

ology and Medicine (ICIBM 2023), Tampa, Florida,

United States. July 16-19, 2023. Abstract number

6527.

Ramchandani, A., Fan, C., and Mostafavi, A. (2020). Deep-

covidnet: An interpretable deep learning model for

predictive surveillance of COVID-19 using heteroge-

neous features and their interactions. IEEE Access,

8:159915–159930.

Skarding, J., Gabrys, B., and Musial, K. (2021). Founda-

tions and modeling of dynamic networks using dy-

namic graph neural networks: A survey. IEEE Access,

9:79143–79168.

Stekhoven, D. J. and B

uhlmann, P. (2012).

MissForest—non-parametric missing value im-

putation for mixed-type data. Bioinformatics,

28(1):112–118.

Sun, C., Fang, R., Salemi, M., Prosperi, M., and Rife Ma-

galis, B. (2023). DeepDynaForecast: Phylogenetic-

informed graph deep learning for epidemic transmis-

sion dynamic prediction. bioRxiv, pages 2023–07.

Sun, C., Li, Y., Marini, S., Riva, A., Wu, D. O., Salemi,

M., and Magalis, B. R. (2022). Phylogenetic-informed

graph deep learning to classify dynamic transmission

clusters in infectious disease epidemics. bioRxiv,

pages 2022–04.

Tan, R. K. J., Tang, W., and Tucker, J. D. (2023). Pub-

lic health services and intersectional stigma: a social

sciences perspective with implications for HIV ser-

vice design and delivery. Current Opinion in HIV and

AIDS, 18(1):18–26.

Tomy, A., Razzanelli, M., Di Lauro, F., Rus, D., and

Della Santina, C. (2022). Estimating the state of epi-

demics spreading with graph neural networks. Non-

linear Dynamics, 109(1):249–263.

Traag, V. A., Waltman, L., and Van Eck, N. J. (2019). From

Louvain to Leiden: guaranteeing well-connected

communities. Scientiﬁc Reports, 9(1):5233.

Trepka, M. J., Niyonsenga, T., Maddox, L., Lieb, S., Lutﬁ,

K., and Pavlova-McCalla, E. (2013). Community

poverty and trends in racial/ethnic survival dispari-

ties among people diagnosed with AIDS in Florida,

1993–2004. American Journal of Public Health,

103(4):717–726.

Von Winterfeldt, D. and Edwards, W. (1986). Decision

analysis and behavioral research.

Wang, L., Adiga, A., Chen, J., Sadilek, A., Venkatramanan,

S., and Marathe, M. (2022). Causalgnn: Causal-based

graph neural networks for spatio-temporal epidemic

forecasting. In Proceedings of the AAAI Conference

on Artiﬁcial Intelligence, volume 36, pages 12191–

12199. February 22 – March 1, 2022.

Wright, I. A., Reid, R., Shahid, N., Ponce, A., Nelson,

C. M., Sanders, J., Gardner, N., Liu, J., Simmons, E.,

Phillips, A., Pan, Y., Alcaide, M. L., Rodriguez, A.,

Ironson, G., Feaster, D. J., Safren, S. A., and Dale,

S. K. (2022). Neighborhood Characteristics, Intersec-

tional Discrimination, Mental Health, and HIV Out-

comes Among Black Women Living With HIV, South-

eastern United States, 2019–2020. American Journal

of Public Health, 112(S4):S433–S443.

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip,

S. Y. (2020). A comprehensive survey on graph neural

networks. IEEE Transactions on Neural Networks and

Learning Systems, 32(1):4–24.

Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2019).

How powerful are graph neural networks? In-

ternational Conference on Learning Representations

(ICLR). May 6 - 9, 2017.

Zhang, L., Wang, S., and Liu, B. (2018). Deep learning

for sentiment analysis: A survey. Wiley Interdisci-

plinary Reviews: Data Mining and Knowledge Dis-

covery, 8(4):e1253.

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z.,

Wang, L., Li, C., and Sun, M. (2020). Graph neu-

ral networks: A review of methods and applications.

AI Open, 1:57–81.

Learning on Forecasting HIV Epidemic Based on Individuals’ Contact Networks

111