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Inferring HIV Transmission Network Determinants Using Agent-Based Models Calibrated to Multi-Data Sources

Author

Listed:
  • David Niyukuri

    (Division of Epidemiology & Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
    The Department of Science and Technology-National Research Foundation (DST-NRF)/South African Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch 7600, South Africa)

  • Trust Chibawara

    (Division of Epidemiology & Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa)

  • Peter Suwirakwenda Nyasulu

    (The Department of Science and Technology-National Research Foundation (DST-NRF)/South African Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch 7600, South Africa
    Division of Epidemiology & Biostatistics, School of Public Health, Faculty of Health, University of the Witwatersrand, Johannesburg 2000, South Africa)

  • Wim Delva

    (Division of Epidemiology & Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7505, South Africa
    The Department of Science and Technology-National Research Foundation (DST-NRF)/South African Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch 7600, South Africa
    Center for Statistics, I-BioStat, Hasselt University, 3590 Diepenbeek, Belgium
    International Centre for Reproductive Health, Ghent University, 9000 Ghent, Belgium)

Abstract

(1) Background: Calibration of Simpact Cyan can help to improve estimates related to the transmission dynamics of the Human Immunodeficiency Virus (HIV). Age-mixing patterns in sexual partnerships, onward transmissions, and temporal trends of HIV incidence are determinants which can inform the design of efficient prevention, and linkage-to-care programs. Using an agent-based model (ABM) simulation tool, we investigated, through a simulation study, if estimates of these determinants can be obtained with high accuracy by combining summary features from different data sources. (2) Methods: With specific parameters, we generated the benchmark data, and calibrated the default model in three scenarios based on summary features for comparison. For calibration, we used Latin Hypercube Sampling approach to generate parameter values, and Approximation Bayesian Computation to choose the best fitting ones. In all calibration scenarios the mean square root error was used as a measure to depict the estimates accuracy. (3) Results: The accuracy measure showed relatively no difference between the three scenarios. Moreover, we found that in all scenarios, age and gender strata incidence trends were poorly estimated. (4) Conclusions: Using synthetic benchmarks, we showed that it is possible to infer HIV transmission dynamics using an ABM of HIV transmission. Our results suggest that any type of summary feature provides adequate information to estimate HIV transmission network determinants. However, it is advisable to check the level of accuracy of the estimates of interest using benchmark data.

Suggested Citation

  • David Niyukuri & Trust Chibawara & Peter Suwirakwenda Nyasulu & Wim Delva, 2021. "Inferring HIV Transmission Network Determinants Using Agent-Based Models Calibrated to Multi-Data Sources," Mathematics, MDPI, vol. 9(21), pages 1-33, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2645-:d:660402
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    References listed on IDEAS

    as
    1. David A Rasmussen & Erik M Volz & Katia Koelle, 2014. "Phylodynamic Inference for Structured Epidemiological Models," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-16, April.
    2. Jennifer Smith & Constance Nyamukapa & Simon Gregson & James Lewis & Sitholubuhle Magutshwa & Christina Schumacher & Phyllis Mushati & Tim Hallett & Geoff Garnett, 2014. "The Distribution of Sex Acts and Condom Use within Partnerships in a Rural Sub-Saharan African Population," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-12, February.
    3. C Marijn Hazelbag & Jonathan Dushoff & Emanuel M Dominic & Zinhle E Mthombothi & Wim Delva, 2020. "Calibration of individual-based models to epidemiological data: A systematic review," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-17, May.
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