Progression and transmission of HIV (PATH 4.0)-A new agent-based evolving network simulation for modeling HIV transmission clusters

Abstract

<abstract> <p>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 <italic>agent-based evolving network modeling</italic> (ABENM) technique and <italic>evolving contact network algorithm</italic> (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. <italic>Ending the HIV Epidemic</italic> 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.</p> </abstract>

Agent-based evolving network modeling: a new simulation method for modeling low prevalence infectious diseases

“…One such study extended this work to model HIV in the United States, by modeling comprehensive dynamics and heterogeneity in population demographics and risk behavior, and showed good validation with national surveillance data on multiple epidemic and network features. [ 30 , 31 ] The results from the analysis conducted here on hypothetical networks and data assumptions, representative of a wide range of disease and behavioral features, are promising for application of the ABENM as an alternative simulation technique for study of other diseases with low prevalence.…”

“…These features, however, may not necessarily influence degree correlations, and therefore may need to be considered outside the structure of neural networks. For example, for sexually transmitted diseases, age of partners (age of neighboring nodes) are relevant, as persons of similar age are more likely to form partnerships, but it does not impact degree correlation when modeling lifetime partners as done by one HIV model [ 30 , 31 ]. They used the neural network presented here to predict the number of lifetime number of partners (degree) of a neighboring node, but developed new optimization methods to model the current age of the neighbor, the age at which the partnership would initiate, and the duration of the partnership, considering all of its lifetime partnerships.…”

Joint modeling HIV and HPV using a new hybrid agent-based network and compartmental simulation technique

“…Most HIV network models in the literature simulate sub-populations separately, which overlooks the mixing associated between sub-groups, e.g., about half of new HIV cases among women were linked to transmissions from MSM [56,57]. The MAC framework overcomes the computational challenges by simulating persons with at least one lower prevalence disease and their immediate contacts in an agent-based network model, and all other persons including those with only higher prevalence diseases in a compartmental model, using an agent-based evolving network algorithm (ABENM) to maintain the network dynamics between persons in the two models [58,59]. The MAC simulation framework has been described elsewhere [51], and ABENM for simulating sexual transmission networks in the U.S. has been applied to the 'Progression and Transmission of HIV' (PATH 4.0) model in the U.S. [59], and extensively validated against multiple epidemic and network metrics from the U.S. National HIV Surveillance Systems.…”

“…Persons infected with the lower prevalence HIV (they may also be infected with HPV), and their immediate contacts are tracked in the network and all other persons, including those with only higher prevalence HPV and those uninfected with either disease are tracked in a compartmental model. We newly calibrated an HPV model for the U.S. population and adopted the HIV model from the previously validated PATH 4.0 model [59]. Details of MAC are presented in [51], we present below an overview of the computational structure of MAC, an overview of the calibration of HIV and HPV, and numerical analyses.…”

Modeling nonlocal behavior in epidemics via a reaction-diffusion system incorporating population movement along a network

“…These systems should be recognized, however, as special cases within a far more general framework [19,8]. We note that many models, including agent-based and evolving network models, even if not expressed as integral or differential equations, still generally utilize some sort of compartmental structure [28,34]. Such compartment models have historically been applied with great success in the modeling of infectious disease, and offer a clear mechanistic description of epidemic progression.…”