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

Abstract

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 inmore » 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.« less

Authors:
 [1];  [2];  [3]; ORCiD logo [4]
  1. University of Greifswald (Germany)
  2. Centers for Disease Control and Prevention, Atlanta, GA (United States)
  3. Saint Petersburg State University (Russia)
  4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1479907
Report Number(s):
LA-UR-18-30123
Journal ID: ISSN 1932-6203
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 13; Journal Issue: 10; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Biological Science

Citation Formats

Bulla, Ingo, Spickanll, Ian H., Gromov, Dmitry, and Romero-Severson, Ethan Obie. Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention. United States: N. p., 2018. Web. doi:10.1371/journal.pone.0204741.
Bulla, Ingo, Spickanll, Ian H., Gromov, Dmitry, & Romero-Severson, Ethan Obie. Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention. United States. doi:10.1371/journal.pone.0204741.
Bulla, Ingo, Spickanll, Ian H., Gromov, Dmitry, and Romero-Severson, Ethan Obie. Thu . "Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention". United States. doi:10.1371/journal.pone.0204741. https://www.osti.gov/servlets/purl/1479907.
@article{osti_1479907,
title = {Sensitivity of joint contagiousness and susceptibility-based dynamic optimal control strategies for HIV prevention},
author = {Bulla, Ingo and Spickanll, Ian H. and Gromov, Dmitry and Romero-Severson, Ethan Obie},
abstractNote = {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.},
doi = {10.1371/journal.pone.0204741},
journal = {PLoS ONE},
issn = {1932-6203},
number = 10,
volume = 13,
place = {United States},
year = {2018},
month = {10}
}

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Figures / Tables:

Fig 1. Fig 1.: Illustration of the transmission model. The transmission model divides the population into 9 states ($\underline{T}$reated $\underline{H}$igh Risk, $\underline{T}$reated $\underline{L}$ow Risk, $\underline{C}$hronic $\underline{H}$igh Risk, $\underline{C}$hronic $\underline{L}$ow Risk, $\underline{A}$ctue $\underline{H}$igh Risk, $\underline{A}$cute $\underline{L}$ow Risk, $\underline{S}$usceptible $\underline{H}$igh Risk, $\underline{S}$usceptible $\underline{L}$ow Risk, and $\underline{P}$rotected) represented by boxes. Flows between states are representedmore » as arrows. Symbols represent rate coefficients and model parameters. The term $ϕ$H and $ϕ$L are complex terms involving both sexual mixing and contact rate terms.« less

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Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.