Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention

Bulla, Ingo; Spickanll, Ian H.; Gromov, Dmitry; Romero-Severson, Ethan Obie

doi:10.1371/journal.pone.0204741

Title: Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention

Journal Article · Thu Oct 18 00:00:00 EDT 2018 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0204741· OSTI ID:1479907

Bulla, Ingo ^[1]; Spickanll, Ian H. ^[2]; Gromov, Dmitry ^[3];

^[4]

University of Greifswald (Germany)
Centers for Disease Control and Prevention, Atlanta, GA (United States)
Saint Petersburg State University (Russia)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Predicting the population-level effects of an infectious disease intervention that incorporate multiple modes of intervention is complicated by the joint non-linear dynamics of both infection transmission and the intervention itself. In this paper, we consider the sensitivity of Dynamic Optimal Control Profiles (DOCPs) for the optimal joint investment in both a contagiousness and susceptibility-based control of HIV to bio-behavioral, economic, and programmatic assumptions. The DOCP is calculated using recently developed numerical algorithms that allow controls to be represented by a set of piecewise constant functions that maintain a constant yearly budget. Our transmission model assumes multiple stages of HIV infection corresponding to acute and chronic infection and both within- and between-individual behavioral heterogeneity. We parameterize a baseline scenario from a longitudinal study of sexual behavior in MSM and consider sensitivity of the DOCPs to deviations from that baseline scenario. In the baseline scenario, the primary determinant of the dominant control were programmatic factors, regardless of budget. In sensitivity analyses, the qualitative aspects of the optimal control policy were often robust to significant deviation in assumptions regarding transmission dynamics. In addition, we found several conditions in which long-term joint investment in both interventions was optimal. Our results suggest that modeling in the service of decision support for intervention design can improve population-level effects of a limited set of economic resources. We found that economic and programmatic factors were as important as the inherent transmission dynamics in determining population-level intervention effects. Given our finding that the DOCPs were robust to alternative biological and behavioral assumptions it may be possible to identify DOCPs even when the data are not sufficient to identify a transmission model.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1479907

Report Number(s):: LA-UR-18-30123

Journal Information:: PLoS ONE, Vol. 13, Issue 10; ISSN 1932-6203

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 2 works

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