Agent-based Network Research Articles

Stimulant use interventions may strengthen 'Getting to Zero' HIV elimination initiatives in Illinois: Insights from a modeling study.

Getting to Zero (GTZ) is an Illinois-based HIV elimination initiative. GTZ identifies younger Black men who have sex with men (YBMSM) as a population who have experienced disproportionate HIV incidence. Rising stimulant use among YBMSM has been determined to impede engagement in the HIV prevention and treatment continua for reducing onward HIV transmission. Given the limited development of dedicated or culturally appropriate interventions for this population, this modeling study explores the impact of stimulant use on HIV incidence among YBMSM and assesses the impact of interventions to treat stimulant use on downstream HIV transmission to achieve GTZ goals. A previously developed agent-based network model (ABNM), calibrated using data for YBMSM in Illinois, was extended to incorporate the impact of stimulant use (methamphetamines, crack/cocaine, and ecstasy) on sexual networks and engagement in HIV treatment and prevention continua. The model simulated the impact of a residential behavioral intervention (BI) for reducing stimulant use and an outpatient biomedical intervention (mirtazapine) for treating methamphetamine use. The downstream impact of these interventions on population-level HIV incidence was the primary intervention outcome. Baseline simulated annual HIV incidence in the ABNM was 6.93 [95% Uncertainty Interval (UI): 6.83,7.04] per 100 person years (py) and 453 [95% UI: 445.9,461.2] new infections annually. A residential rehabilitation intervention targeted to 25% of stimulant using persons yielded a 27.1% reduction in the annual number of new infections. Initiating about 50% of methamphetamine using persons on mirtazapine reduced the overall HIV incidence among YBMSM by about 11.2%. A 30% increase in antiretroviral treatment (ART) and preexposure prophylaxis (PrEP) uptake in the non-stimulant using YBMSM population combined with a 25% uptake of BI for stimulant using persons produces an HIV incidence consistent with HIV elimination targets (about 200 infections/year) identified in the GTZ initiative. Behavioral and biomedical interventions to treat stimulant use, in addition to expanding overall ART and PrEP uptake, are likely to enhance progress towards achieving GTZ goals.

Estimated Transmission Outcomes and Costs of SARS-CoV-2 Diagnostic Testing, Screening, and Surveillance Strategies Among a Simulated Population of Primary School Students

In addition to illness, the COVID-19 pandemic has led to historic educational disruptions. In March 2021, the federal government allocated $10 billion for COVID-19 testing in US schools. Costs and benefits of COVID-19 testing strategies were evaluated in the context of full-time, in-person kindergarten through eighth grade (K-8) education at different community incidence levels. An updated version of a previously published agent-based network model was used to simulate transmission in elementary and middle school communities in the United States. Assuming dominance of the delta SARS-CoV-2 variant, the model simulated an elementary school (638 students in grades K-5, 60 staff) and middle school (460 students grades 6-8, 51 staff). Multiple strategies for testing students and faculty/staff, including expanded diagnostic testing (test to stay) designed to avoid symptom-based isolation and contact quarantine, screening (routinely testing asymptomatic individuals to identify infections and contain transmission), and surveillance (testing a random sample of students to identify undetected transmission and trigger additional investigation or interventions). Projections included 30-day cumulative incidence of SARS-CoV-2 infection, proportion of cases detected, proportion of planned and unplanned days out of school, cost of testing programs, and childcare costs associated with different strategies. For screening policies, the cost per SARS-CoV-2 infection averted in students and staff was estimated, and for surveillance, the probability of correctly or falsely triggering an outbreak response was estimated at different incidence and attack rates. Compared with quarantine policies, test-to-stay policies are associated with similar model-projected transmission, with a mean of less than 0.25 student days per month of quarantine or isolation. Weekly universal screening is associated with approximately 50% less in-school transmission at one-seventh to one-half the societal cost of hybrid or remote schooling. The cost per infection averted in students and staff by weekly screening is lowest for schools with less vaccination, fewer other mitigation measures, and higher levels of community transmission. In settings where local student incidence is unknown or rapidly changing, surveillance testing may detect moderate to large in-school outbreaks with fewer resources compared with schoolwide screening. In this modeling study of a simulated population of primary school students and simulated transmission of COVID-19, test-to-stay policies and/or screening tests facilitated consistent in-person school attendance with low transmission risk across a range of community incidence. Surveillance was a useful reduced-cost option for detecting outbreaks and identifying school environments that would benefit from increased mitigation.

A New Mixed Agent-Based Network and Compartmental Simulation Framework for Joint Modeling of Related Infectious Diseases- Application to Sexually Transmitted Infections

A New Mixed Agent-Based Network and Compartmental Simulation Framework for Joint Modeling of Related Infectious Diseases- Application to Sexually Transmitted Infections

Contact Tracing for Disease Containment: a Network-Based Analysis*

Since the outbreak of the COVID-19 pandemic in spring 2020, the concept of test, trace, and isolate (TTI) was used as a non-pharmaceutical intervention against further spreading of the disease. Hereby, recent contact partners of newly confirmed SARS-CoV-2 infected persons were identified and isolated along with the originally detected case to avoid potential secondary infections. While the policy is, given the compliance of the traced persons, generally deemed efficient, not much is known about network-specific impact factors.In this work, we aim to evaluate the effectiveness of the TTI strategy when used (1) for diseases with different infectiousness levels and (2) on different contact networks. For the prior, we vary the infection probability per contact, for the latter, we analyse different clustering coefficients. Our goal is to test the validity of two hypotheses: First, we expect the policy to be more efficient if the infectiousness of the disease is small, since the time delay for isolating persons is crucial. Second, due to the implications of the friendship paradox, we expect the policy to be more effective if the clustering coefficient of the underlying contact network is high. We make use of an agent-based network model consisting of three intertwined model parts: an epidemiological SEIR model, a quarantine model and a contact-tracing model. To test the hypotheses, the disease parameters and the clustering coefficient of the underlying contact network are varied.The simulation results show that, indeed, tracing seems to have a slightly larger containment impact for networks with higher clustering, in particular for fast-spreading diseases. Yet, the effects are small compared to the impact of the infectiousness of the disease. Therefore, we find a significant decrease of the policy effectiveness the higher the transmission probability. The latter implies that the containment impact of tracing and isolating contacts becomes more efficient, if supported by additional measures that limit the infection probability or if applied in periods with low negative seasonality effects.

Passing the Test: A Model-Based Analysis of Safe School-Reopening Strategies.

The COVID-19 pandemic has induced historic educational disruptions. In April 2021, about 40% of U.S. public school students were not offered full-time in-person education. To assess the risk for SARS-CoV-2 transmission in schools. An agent-based network model was developed to simulate transmission in elementary and high school communities, including home, school, and interhousehold interactions. School structure was parametrized to reflect average U.S. classrooms, with elementary schools of 638 students and high schools of 1451 students. Daily local incidence was varied from 1 to 100 cases per 100000 persons. Students, faculty, staff, and adult household members. Isolation of symptomatic individuals, quarantine of an infected individual's contacts, reduced class sizes, alternative schedules, staff vaccination, and weekly asymptomatic screening. Transmission was projected among students, staff, and families after a single infection in school and over an 8-week quarter, contingent on local incidence. School transmission varies according to student age and local incidence and is substantially reduced with mitigation measures. Nevertheless, when transmission occurs, it may be difficult to detect without regular testing because of the subclinical nature of most children's infections. Teacher vaccination can reduce transmission to staff, and asymptomatic screening improves understanding of local circumstances and reduces transmission. Uncertainty exists about the susceptibility and infectiousness of children, and precision is low regarding the effectiveness of specific countermeasures, particularly with new variants. With controlled community transmission and moderate mitigation, elementary schools can open safety, but high schools require more intensive mitigation. Asymptomatic screening can facilitate reopening at higher local incidence while minimizing transmission risk. Centers for Disease Control and Prevention through the Council of State and Territorial Epidemiologists, National Institute of Allergy and Infectious Diseases, National Institute on Drug Abuse, and Facebook.

Effects of Condom Use on Human Immunodeficiency Virus Transmission Among Adolescent Sexual Minority Males in the United States: A Mixed Epidemiology and Epidemic Modeling Study.

We examined condom use patterns and potential population-level effects of a hypothetical condom intervention on human immunodeficiency virus (HIV) transmission among adolescent sexual minority males (ASMM). Using 3 data sets: national Youth Risk Behavior Survey 2015 to 2017 (YRBS-National), local YRBS data from 8 jurisdictions with sex of partner questions from 2011 to 2017 (YRBS-Trends), and American Men's Internet Survey (AMIS) 2014 to 2017, we assessed associations of condom use with year, age, and race/ethnicity among sexually active ASMM. Using a stochastic agent-based network epidemic model, structured and parameterized based on the above analyses, we calculated the percent of HIV infections averted over 10 years among ASMM ages 13 to 18 years by an intervention that increased condom use by 37% for 5 years and was delivered to 62% of ASMM at age 14 years. In YRBS, 51.8% (95% confidence interval [CI], 41.3-62.3%) and 37.9% (95% CI, 32.7-42.3%) reported condom use at last sexual intercourse in national and trend data sets, respectively. In AMIS, 47.3% (95% CI, 44.6-49.9%) reported condom use at last anal sex with a male partner. Temporal trends were not observed in any data set (P > 0.1). Condom use varied significantly by age in YRBS-National (P < 0.0001) and YRBS-Trends (P = 0.032) with 13- to 15-year-olds reporting the lowest use in both; age differences were not significant in AMIS (P = 0.919). Our hypothetical intervention averted a mean of 9.0% (95% simulation interval, -5.4% to 21.2%) of infections among ASMM. Condom use among ASMM is low and appears to have remained stable during 2011 to 2017. Modeling suggests that condom use increases, consistent with previous interventions, have potential to avert 1 in 11 new HIV infections among ASMM.

A Discrete-Event, Simulated Social Agent-Based Network Transmission (DESSABNeT) model for communicable diseases: Method and validation using SARS-CoV-2 data in three large Australian cities.

ImportanceDuring pandemics Agent Based Models (ABMs) can model complex, fine-grained behavioural interactions occurring in social networks, that contribute to disease transmission by novel viruses such as SARS-CoV-2.ObjectiveWe present a new agent-based model (ABM) called the Discrete-Event, Simulated Social Agent based Network Transmission model (DESSABNeT) and demonstrate its ability to model the spread of COVID-19 in large cities like Sydney, Melbourne and Gold Coast. Our aim was to validate the model with its disease dynamics and underlying social network.DesignDESSABNeT relies on disease transmission within simulated social networks. It employs an epidemiological SEIRD+M (Susceptible, exposed, infected, recovered, died and managed) structure. One hundred simulations were run for each city, with simulated social restrictions closely modelling real restrictions imposed in each location.Main outcome(s) and measure(s)The mean predicted daily incidence of COVID-19 cases were compared to real case incidence data for each city. Reff and health service utilisation outputs were compared to the literature, or for the Gold Coast with daily incidence of hospitalisation.ResultsDESSABNeT modelled multiple physical distancing restrictions and predicted epidemiological outcomes of Sydney, Melbourne and the Gold Coast, validating this model for future simulation work.Conclusions and relevanceDESSABNeT is a valid platform to model the spread of COVID-19 in large cities in Australia and potentially internationally. The platform is suitable to model different combinations of social restrictions, or to model contact tracing, predict, and plan for, the impact on hospital and ICU admissions, and deaths; and also the rollout of COVID-19 vaccines and optimal social restrictions during vaccination.

Software for Agent-based Network Simulation and Visualization

We present a network software suite that can model contagions or opinion manipulation in social networks, that combines features from the standard packages and extends them to allow complex, interacting, dynamic topologies, and dynamic heterogeneous agent types, with individual interaction policies. The framework allows for the easy implementation of new agent types, and provides flexible visualization tools to elucidate network behavior over time.

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

Agent-based network modeling (ABNM) simulates each person at the individual-level as agents of the simulation, and uses network generation algorithms to generate the network of contacts between individuals. ABNM are suitable for simulating individual-level dynamics of infectious diseases, especially for diseases such as HIV that spread through close contacts within intricate contact networks. However, as ABNM simulates a scaled-version of the full population, consisting of all infected and susceptible persons, they are computationally infeasible for studying certain questions in low prevalence diseases such as HIV. We present a new simulation technique, agent-based evolving network modeling (ABENM), which includes a new network generation algorithm, Evolving Contact Network Algorithm (ECNA), for generating scale-free networks. ABENM simulates only infected persons and their immediate contacts at the individual-level as agents of the simulation, and uses the ECNA for generating the contact structures between these individuals. All other susceptible persons are modeled using a compartmental modeling structure. Thus, ABENM has a hybrid agent-based and compartmental modeling structure. The ECNA uses concepts from graph theory for generating scale-free networks. Multiple social networks, including sexual partnership networks and needle sharing networks among injecting drug-users, are known to follow a scale-free network structure. Numerical results comparing ABENM with ABNM estimations for disease trajectories of hypothetical diseases transmitted on scale-free contact networks are promising for application to low prevalence diseases.

Using an agent-based sexual-network model to analyze the impact of mitigation efforts for controlling chlamydia

Chlamydia trachomatis (Ct) is the most reported sexually transmitted infection in the United States, with a major cause of infertility, pelvic inflammatory disease, and ectopic pregnancy among women. Despite decades of screening women for Ct, rates increase among young African Americans (AA). We create and analyze a heterosexual agent-based network model to help understand the spread of Ct. We calibrate the model parameters to agree with survey data showing Ct prevalence of 12% of the women and 10% of the men in the 15–25 year-old AA in New Orleans, Louisiana. Our model accounts for both long-term and casual partnerships. The network captures the assortative mixing of individuals by preserving the joint-degree distributions observed in the data. We compare the effectiveness of intervention strategies based on randomly screening men, notifying partners of infected people, which includes partner treatment, partner screening, and rescreening for infection. We compare the difference between treating partners of an infected person both with and without testing them. We observe that although increased Ct screening, rescreening, and treating most of the partners of infected people will reduce the prevalence, these mitigations alone are not sufficient to control the epidemic. The current practice is to treat the partners of an infected individual without first testing them for infection. The model predicts that if a sufficient number of the partners of all infected people are tested and treated, then there is a threshold condition where the epidemic can be mitigated. This threshold results from the expanded treatment network created by treating an individual’s infected partners’ partners. Although these conclusions can help design future Ct mitigation studies, we caution the reader that these conclusions are for the mathematical model, not the real world, and are contingent on the validity of the model assumptions.

Evaluation of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Mitigation Strategies on a University Campus Using an Agent-Based Network Model.

Universities are faced with decisions on how to resume campus activities while mitigating SARS-CoV-2 risk. To provide guidance for these decisions, we developed an agent-based network model of SARS-CoV-2 transmission to assess the potential impact of strategies to reduce outbreaks. The model incorporates important features related to risk at the University of California San Diego. We found that structural interventions for housing (singles only) and instructional changes (from in-person to hybrid with class size caps) can substantially reduce R0, but masking and social distancing are required to reduce this to at or below 1. Within a risk mitigation scenario, increased frequency of asymptomatic testing from monthly to twice weekly has minimal impact on average outbreak size (1.1-1.9), but substantially reduces the maximum outbreak size and cumulative number of cases. We conclude that an interdependent approach incorporating risk mitigation, viral detection, and public health intervention is required to mitigate risk.

Agent-based modeling of teacher interaction within a repository of digital objects

This article presents generalized model of collaborative actions, during which participants create, modify, and estimate digital objects. Such activities can be observed in numerous network communities. A prominent example is the repository of lesson scenarios of Moscow Electronic School (MES). The combination of methods of agent-based modeling and network analysis is used in the work. Using NetLogo environment in the frames of the model, an artificial community has been developed, where teachers-agents interact with scenarios-agents. Teacher-agent determines whether there are potential scenarios in his environment to be contacted with. If such scenarios are available, then the agent selects the nearest one and makes a step towards it. If the scenario has been opened by one of the teachers, then this is already an author’s scenario and the teacher-agent takes an action to reuse it. Variants of the reuse can be preset so that to correspond to the actions allowable in the environment of MES repository for learning scenarios: review, addition to bookmarks, running the scenario, downloading, using in home assignments. All these actions of teachers regarding scenarios are logged, then the log records are transformed into bipartite graph. The experiments demonstrate that while the area of participant scenarios is expanded, not only the general number of links among participants increases but also large networks of participants are subdivided into smaller and densely interconnected groups. One of the control trends of participant activities is in the use of multiagent-based modeling as a tool of collective reflection of teachers cooperating on the basis of MES.

Projecting the number of new HIV infections to formulate the "Getting to Zero" strategy in Illinois, USA.

Getting to Zero (GTZ) initiatives focus on expanding use of antiretroviral treatment (ART) and pre-exposure prophylaxis (PrEP) to eliminate new HIV infections. Computational models help inform policies for implementation of ART and PrEP continuums. Such models, however, vary in their design, and may yield inconsistent predictions. Using multiple approaches can help assess the consistency in results obtained from varied modeling frameworks, and can inform optimal implementation strategies. A study using three different modeling approaches is conducted. Two approaches use statistical time series analysis techniques that incorporate temporal HIV incidence data. A third approach uses stochastic stimulation, conducted using an agent-based network model (ABNM). All three approaches are used to project HIV incidence among a key population, young Black MSM (YBMSM), over the course of the GTZ implementation period (2016-2030). All three approaches suggest that simultaneously increasing PrEP and ART uptake is likely to be more effective than increasing only one, but increasing ART and PrEP by 20% points may not eliminate new HIV infections among YBMSM. The results further suggest that a 20% increase in ART is likely to be more effective than a 20% increase in PrEP. All three methods consistently project that increasing ART and PrEP by 30% simultaneously can help reach GTZ goals. Increasing PrEP and ART uptake by about 30% might be necessary to accomplish GTZ goals. Such scale-up may require addressing psychosocial and structural barriers to engagement in HIV and PrEP care continuums. ABNMs and other flexible modeling approaches can be extended to examine specific interventions that address these barriers and may provide important data to guide the successful intervention implementation.

1701. Added Benefits of Pre-exposure Prophylaxis Use on HIV Incidence with Minimal Changes in Efficiency in the Context of High Treatment Engagement among Men Who Have Sex with Men

BackgroundAlthough there is ongoing debate over the need for substantial increases in PrEP use when antiretroviral treatment confers the dual benefits of reducing HIV-related morbidity and mortality and the risk of HIV transmission, no studies to date have quantified the potential added benefits of PrEP use in settings with high treatment engagement across variable sub-epidemics in the United States.MethodsWe used a previously published agent-based network model to simulate HIV transmission in a dynamic network of 17,440 Black/African American and White MSM in Atlanta, Georgia from 2015 to 2024 to understand how the magnitude of reductions in HIV incidence attributable to varying levels of PrEP use (0–90%) changes in potential futures where high levels of treatment engagement (i.e. the UNAIDS ‘90-90-90’ goals and eventual ‘95-95-95’ goals) are achieved and maintained, as compared to current levels of treatment engagement in Atlanta (Figure 1).Model inputs related to HIV treatment engagement among Black/African American and White men who have sex with men in Atlanta. A comparison of current levels of treatment engagement (Panel A) to treatment engagement at ‘90-90-90’ (Panel B) and ‘95-95-95’ goals (Panel C).ResultsEven at achievement and maintenance of ‘90-90-90’ goals, 75% PrEP coverage reduced incidence rates by an additional 67.9% and 74.2% to 1.53 (SI: 1.39, 1.70) and 0.355 (SI: 0.316, 0.391) per 100 person-years for Black/African American and White MSM, respectively (Figure 2), compared to the same scenario with no PrEP use. Additionally, an increase from 15% PrEP coverage to 75% under ‘90-90-90’ goals only increased person-years of PrEP use per HIV infection averted, a measure of efficiency of PrEP, by 8.1% and 10.5% to 26.7 (SI: 25.6, 28.0) and 73.3 (SI: 70.6, 75.7) among Black/African American MSM and White MSM, respectively (Figure 3). Overall (Panel A) and race-stratified (Panel B and Panel C) marginal changes in HIV incidence over ten years among Black/African American and White men who have sex with men in Atlanta across scenarios of varied levels of treatment engagement among agents living with HIV infection and levels of pre-exposure prophylaxis use among HIV-uninfected agents. Note: All changes are calculated within each set of treatment scenarios relative to a scenario where no agents use pre-exposure prophylaxis. Person-years of pre-exposure prophylaxis use per HIV infection averted among Black/African American (Panel A) and White (Panel B) men who have sex with men in Atlanta across scenarios of varied levels of treatment engagement among agents living with HIV infection and levels of pre-exposure prophylaxis use among HIV-uninfected agents. Note: The number of HIV infections averted is calculated within each set of treatment scenarios relative to a scenario where no agents use pre-exposure prophylaxis. ConclusionEven in the context of high treatment engagement, substantial expansion of PrEP use still contributes to meaningful decreases in HIV incidence among MSM with minimal changes in person-years of PrEP use per HIV infection averted, particularly for Black/African American MSM.Disclosures All Authors: No reported disclosures

Cost-effectiveness of point-of-care testing with task-shifting for HIV care in South Africa: a modelling study

The number of people on antiretroviral therapy (ART) requiring treatment monitoring in low-resource settings is rapidly increasing. Point-of-care (POC) testing for ART monitoring might alleviate burden on centralised laboratories and improve clinical outcomes, but its cost-effectiveness is unknown. We used cost and effectiveness data from the STREAM trial in South Africa (February, 2017-October, 2018), which evaluated POC testing for viral load, CD4 count, and creatinine, with task shifting from professional to lower-cadre registered nurses compared with laboratory-based testing without task shifting (standard of care). We parameterised an agent-based network model, EMOD-HIV, to project the impact of implementing this intervention in South Africa over 20 years, simulating approximately 175 000 individuals per run. We assumed POC monitoring increased viral suppression by 9 percentage points, enrolment into community-based ART delivery by 25 percentage points, and switching to second-line ART by 1 percentage point compared with standard of care, as reported in the STREAM trial. We evaluated POC implementation in varying clinic sizes (10-50 patient initiating ART per month). We calculated incremental cost-effectiveness ratios (ICERs) and report the mean and 90% model variability of 250 runs, using a cost-effectiveness threshold of US$500 per disability-adjusted life-year (DALY) averted for our main analysis. POC testing at 70% coverage of patients on ART was projected to reduce HIV infections by 4·5% (90% model variability 1·6 to 7·6) and HIV-related deaths by 3·9% (2·0 to 6·0). In clinics with 30 ART initiations per month, the intervention had an ICER of $197 (90% model variability -27 to 863) per DALY averted; results remained cost-effective when varying background viral suppression, ART dropout, intervention effectiveness, and reduction in HIV transmissibility. At higher clinic volumes (≥40 ART initiations per month), POC testing was cost-saving and at lower clinic volumes (20 ART initiations per month) the ICER was $734 (93 to 2569). A scenario that assumed POC testing did not increase enrolment into community ART delivery produced ICERs that exceeded the cost-effectiveness threshold for all clinic volumes. POC testing is a promising strategy to cost-effectively improve patient outcomes in moderately sized clinics in South Africa. Results are most sensitive to changes in intervention impact on enrolment into community-based ART delivery. National Institutes of Health.

Network structure and rapid HIV transmission among people who inject drugs: A simulation-based analysis

As HIV incidence among people who inject drugs grows in the context of an escalating drug overdose epidemic in North America, investigating how network structure may affect vulnerability to rapid HIV transmission is necessary for preventing outbreaks. We compared the characteristics of the observed contact tracing network from the 2015 outbreak in rural Indiana with 1000 networks generated by an agent-based network model with approximately the same number of individuals (n = 420) and ties between them (n = 913). We introduced an initial HIV infection into the simulated networks and compared the subsequent epidemic behavior (e.g., cumulative HIV infections over 5 years). The model was able to produce networks with largely comparable characteristics and total numbers of incident HIV infections. Although the model was unable to produce networks with comparable cohesiveness (where the observed network had a transitivity value 35.7 standard deviations from the mean of the simulated networks), the structural variability of the simulated networks allowed for investigation into their potential facilitation of HIV transmission. These findings emphasize the need for continued development of injection network simulation studies in tandem with empirical data collection to further investigate how network characteristics played a role in this and future outbreaks.

Multi-value opinion sharing based on information source influence in agent-based network

This paper proposes the information sharing algorithm for preventing propagation of wrong information in the agent-based network such as SNS, and aims at investigating the effectiveness of the proposed algorithm through the complex network such as a small world network. Towards practical applications, this paper extends the conventional opinion sharing model (OSM) to cope with the multi-value opinion from the binary opinion, and proposes the new algorithm for preventing propagation of wrong information to cope with the multi-value opinion. The intensive simulations of three types of the complex network have revealed that the accuracy of the correct opinion of agents in the proposed algorithm (called Self-information Weight Tuning (SWT)) reaches around 80% which is not affected by the number of opinion states in the multi-value opinions, while that of the conventional algorithm (called Autonomous Adaptive Tuning (AAT)) is lower than the proposed algorithm and decreases as the number of opinion states increases.

Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory

Understanding the mechanism underlying the mobile telecommunications technologies (MTTs) diffusion in a country is crucial for telecom planners to know how to accelerate their diffusion by designing appropriate scenarios. Considering the technology diffusion as a bottom-up process, this study is aimed at exploring this mechanism, drawing on insights from diffusion of innovation theory and social network theory. Accordingly, an agent-based model is proposed to investigate how MTTs are diffused in Iran over time. The results of this study show, (1) social network of Iranian society seems more similar to a Watts–Strogatz small-world network than a Barabási–Albert preferential attachment network, where the clustering coefficient is high and average path length is low, (2) compared to the compatibility parameter, the advertisement parameter not only is less influential on diffusion of a targeted MTT (i.e., 4G) but also is not necessary for it, and (3) scenarios having the least number of steps and turning points are more appropriate for continuous diffusion of 4G. The proposed study is empirically validated against real-world data ranging from 7/1/2017 to 12/31/2017. We believe it provides telecom planners insights regarding MTTs diffusion mechanism in a social complex structure and the how of scenario designing for increasing their diffusion.

Cost-Effectiveness of Point-of-Care Testing with Task-Shifting for HIV Care in South Africa: A Modelling Study

Background: The scale-up of “HIV test and treat” has rapidly increased the number of persons on antiretroviral therapy (ART) requiring treatment monitoring in low-resource settings. Decentralized point-of-care (POC) testing for ART monitoring may alleviate burden on centralized laboratories and improve clinical outcomes, but its cost-effectiveness is unknown. Methods: We used primary cost and effectiveness data from the STREAM trial in South Africa, which assessed the impact of POC testing for viral load, CD4 count, and creatinine, with task-shifting from professional to lower-cadre registered nurses compared to laboratory-based testing without task-shifting. We parameterized an agent-based network model, EMOD-HIV, to project the impact of implementing this intervention in South Africa. We assumed POC monitoring increased viral suppression by 9%, enrollment into community-based ART delivery by 25%, and switching to second-line ART by 1%, as reported in STREAM. We evaluated POC scale-up in varying clinic sizes (10-50 patient initiating ART/month) over a 20-year time horizon. We used a cost-effectiveness threshold of $500 USD/disability adjusted life year (DALY) averted for our main analysis. Results: Implementing POC testing at 70% coverage of ART patients was projected to reduce HIV infections by 4.5% and HIV-related deaths by 3.9%. In clinics with 30 ART initiations/month, the intervention was associated with an incremental cost-effectiveness ratio (ICER) of $197/DALY averted; results remained cost-effective when varying background viral suppression, ART dropout, and intervention effectiveness within the 95% confidence bound of the trial results. Assuming POC testing did not increase enrollment into community ART delivery produced an ICER of $1,149, exceeding the cost-effectiveness threshold. At higher clinic volumes (≥40 ART initiations/month), POC testing was cost-saving compared to standard-of-care. At lower clinic volumes (20 patients initiated on ART/month) the ICER was $734/DALY averted. Conclusions: POC testing for ART monitoring with task-shifting is projected to be cost-effective in moderately-sized clinics in South Africa. Funding Statement: This study was funded by the U.S. National Institute of Allergy and Infectious Diseases (R21AI124719 and R01AI147752). MS received support from NIMH K01MH115789 Declaration of Interests: Dr. Paul Drain reports receiving consulting or speaking fees from Gilead Science and Cepheid, and research support from the NIH, CDC, Gilead Sciences, and the Bill and Melinda Gates Foundation, during the conduct of the study. Dr. Abdool Karim reports grants from the NIH during the conduct of the study. All authors declare that they have no competing interests. Ethics Approval Statement: Missing.

Optimal Network Topology Design in Composite Systems for Structural Controllability

This article deals with structural controllability of a linear time invariant (LTI) composite system consisting of several circuits/subsystems. We consider subsystems with structurally similar state matrices, i.e., the zero/nonzero pattern of the state matrices of the subsystems are the same, but dynamics can be different due to different numerical values. Structurally similar subsystems arise in large circuits implemented using many similar smaller circuits and agent-based networks consisting of homogeneous agents. The subsystems may not be structurally controllable individually; however, structural controllability of the composite system can be achieved by sharing state information. Sharing of information among subsystems incurs cost and our aim is to design a structurally controllable composite system with minimum information sharing. Minimizing information sharing is the same as minimizing the number of interaction links between subsystems, referred to as interconnections . An optimal network topology is one with a minimum number of interconnections. This article presents a closed-form expression for the minimum number of interconnections for structural controllability and derives a polynomial time algorithm to find an optimal network topology. The algorithm is based on a minimum weight perfect matching algorithm and a so-called edge reconstruction process. The minimum number of interconnections required is formulated in terms of two indices we define in the article: maximum commonality index ( $\alpha _{{{\mathscr N}}}$ ) and dilation index ( $\beta _{{\mathcal {I}}}$ ). Loosely speaking, more connectedness of subsystems leads to a lower total value of $\alpha _{{{\mathscr N}}}$ and $\beta _{{\mathcal {I}}}$ . Further, $\alpha _{{{\mathscr N}}}$ decreases if the subsystems have fewer numbers of connected components. We apply and verify our general result to special cases that arise in control, where the minimum number can be more directly obtained. The special cases considered are structurally cyclic subsystems, irreducible subsystems, and subsystems in controller canonical form.