Progression and transmission of HIV (PATH 4.0)-A new agent-based evolving network simulation for modeling HIV transmission clusters
| Autor: | Paul G. Farnham, Alexandra M. Oster, Chaitra Gopalappa, Yao-Hsuan Chen, Sonza Singh |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
hiv modeling
Computer science HIV Infections 02 engineering and technology computer.software_genre Article Network simulation law.invention infectious disease modeling network cluster analyses law Component (UML) 0502 economics and business QA1-939 Prevalence 0202 electrical engineering electronic engineering information engineering Cluster (physics) Humans Computer Simulation Medical diagnosis Epidemics Network model network modeling Applied Mathematics 05 social sciences General Medicine Network dynamics Computational Mathematics Transmission (mechanics) Modeling and Simulation Path (graph theory) HIV-1 020201 artificial intelligence & image processing Data mining General Agricultural and Biological Sciences computer agent-based simulation TP248.13-248.65 Mathematics 050203 business & management Biotechnology |
| Zdroj: | Math Biosci Eng Mathematical Biosciences and Engineering, Vol 18, Iss 3, Pp 2150-2181 (2021) |
| ISSN: | 1551-0018 |
| DOI: | 10.3934/mbe.2021109 |
| Popis: | We present the Progression and Transmission of HIV (PATH 4.0), a simulation tool for analyses of cluster detection and intervention strategies. Molecular clusters are groups of HIV infections that are genetically similar, indicating rapid HIV transmission where HIV prevention resources are needed to improve health outcomes and prevent new infections. PATH 4.0 was constructed using a newly developed agent-based evolving network modeling (ABENM) technique and evolving contact network algorithm (ECNA) for generating scale-free networks. ABENM and ECNA were developed to facilitate simulation of transmission networks for low-prevalence diseases, such as HIV, which creates computational challenges for current network simulation techniques. Simulating transmission networks is essential for studying network dynamics, including clusters. We validated PATH 4.0 by comparing simulated projections of HIV diagnoses with estimates from the National HIV Surveillance System (NHSS) for 2010–2017. We also applied a cluster generation algorithm to PATH 4.0 to estimate cluster features, including the distribution of persons with diagnosed HIV infection by cluster status and size and the size distribution of clusters. Simulated features matched well with NHSS estimates, which used molecular methods to detect clusters among HIV nucleotide sequences of persons with HIV diagnosed during 2015–2017. Cluster detection and response is a component of the U.S. Ending the HIV Epidemic strategy. While surveillance is critical for detecting clusters, a model in conjunction with surveillance can allow us to refine cluster detection methods, understand factors associated with cluster growth, and assess interventions to inform effective response strategies. As surveillance data are only available for cases that are diagnosed and reported, a model is a critical tool to understand the true size of clusters and assess key questions, such as the relative contributions of clusters to onward transmissions. We believe PATH 4.0 is the first modeling tool available to assess cluster detection and response at the national-level and could help inform the national strategic plan. |
| Databáze: | OpenAIRE |
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