Learning on Forecasting HIV Epidemic Based on Individuals' Contact Networks

Chaoyue Sun, Yiyang Liu, Christina Parisi, Rebecca Fisk-Hoffman, Marco Salemi, Marco Salemi, Ruogu Fang, Ruogu Fang, Ruogu Fang, Ruogu Fang, Brandi Danforth, Mattia Prosperi, Mattia Prosperi, Simone Marini, Simone Marini

2024

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 – leveraging 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 prediction process. Our findings provide valuable insights for healthcare practitioners and policymakers in their efforts to combat HIV infection.

Paper Citation

in Harvard Style

Sun C., Liu Y., Parisi C., Fisk-Hoffman R., Salemi M., Fang R., Danforth B., Prosperi M. and Marini S. (2024). Learning on Forecasting HIV Epidemic Based on Individuals' Contact Networks. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 103-111. DOI: 10.5220/0012375400003657

in Bibtex Style

@conference{healthinf24,
author={Chaoyue Sun and Yiyang Liu and Christina Parisi and Rebecca Fisk-Hoffman and Marco Salemi and Ruogu Fang and Brandi Danforth and Mattia Prosperi and Simone Marini},
title={Learning on Forecasting HIV Epidemic Based on Individuals' Contact Networks},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={103-111},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012375400003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Learning on Forecasting HIV Epidemic Based on Individuals' Contact Networks
SN - 978-989-758-688-0
AU - Sun C.
AU - Liu Y.
AU - Parisi C.
AU - Fisk-Hoffman R.
AU - Salemi M.
AU - Fang R.
AU - Danforth B.
AU - Prosperi M.
AU - Marini S.
PY - 2024
SP - 103
EP - 111
DO - 10.5220/0012375400003657
PB - SciTePress