Development and Calibration of a Dynamic HIV Transmission Model for 6 US Cities

Xiao Zang, Emanuel Krebs, Jeong E. Min, Ankur Pandya, Brandon D. L. Marshall, Bruce R. Schackman, Czarina N. Behrends, Daniel J. Feaster and Bohdan Nosyk
Additional contact information
Xiao Zang: BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
Emanuel Krebs: BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
Jeong E. Min: BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada
Ankur Pandya: Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA
Brandon D. L. Marshall: Department of Epidemiology, School of Public Health, Brown University, Providence, RI, USA
Bruce R. Schackman: Department of Healthcare Policy and Research, Weill Cornell Medical College, New York City, NY, USA
Czarina N. Behrends: Department of Healthcare Policy and Research, Weill Cornell Medical College, New York City, NY, USA
Daniel J. Feaster: Department of Epidemiology and Public Health, Leonard M. Miller School of Medicine, University of Miami, Miami, FL, USA
Bohdan Nosyk: BC Centre for Excellence in HIV/AIDS, Vancouver, BC, Canada

Medical Decision Making, 2020, vol. 40, issue 1, 3-16

Abstract: Background. Heterogeneity in HIV microepidemics across US cities necessitates locally oriented, combination implementation strategies to prioritize resources. We calibrated and validated a dynamic, compartmental HIV transmission model to establish a status quo treatment scenario, holding constant current levels of care for 6 US cities. Methods. Built off a comprehensive evidence synthesis, we adapted and extended a previously published model to replicate the transmission, progression, and clinical care for each microepidemic. We identified a common set of 17 calibration targets between 2012 and 2015 and used the Morris method to select the most influential parameters for calibration. We then applied the Nelder-Mead algorithm to iteratively calibrate the model to generate 2000 best-fitting parameter sets. Finally, model projections were internally validated with a series of robustness checks and externally validated against published estimates of HIV incidence, while the face validity of 25-year projections was assessed by a Scientific Advisory Committee (SAC). Results. We documented our process for model development, calibration, and validation to maximize its transparency and reproducibility. The projected outcomes demonstrated a good fit to calibration targets, with a mean goodness-of-fit ranging from 0.0174 (New York City [NYC]) to 0.0861 (Atlanta). Most of the incidence predictions were within the uncertainty range for 5 of the 6 cities (ranging from 21% [Miami] to 100% [NYC]), demonstrating good external validity. The face validity of the long-term projections was confirmed by our SAC, showing that the incidence would decrease or remain stable in Atlanta, Los Angeles, NYC, and Seattle while increasing in Baltimore and Miami. Discussion. This exercise provides a basis for assessing the incremental value of further investments in HIV combination implementation strategies tailored to urban HIV microepidemics.

Keywords: dynamic transmission model; epidemiological projection; HIV/AIDS; model calibration; model validation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X19889356 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:1:p:3-16

DOI: 10.1177/0272989X19889356

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().