Duwal, S. and Winkelmann, S. and Schütte, Ch. and von Kleist, M.
(2015)
*Optimal treatment strategies in the context of 'treatment for prevention' against HIV-1 in resource-poor settings.*
PLOS Computational Biology, 11
.
e1004200.

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Official URL: http://journals.plos.org/ploscompbiol/article?id=1...

## Abstract

An estimated 2.7 million new HIV-1 infections occurred in 2010. `Treatment-for-prevention' may strongly prevent HIV-1 transmission. The basic idea is that immediate treatment initiation rapidly decreases virus burden, which reduces the number of potentially transmitted viruses and thereby the probability of infection. However, HIV inevitably develops drug resistance, which leads to virus rebound and nullifies the effect of `treatment-for-prevention' for the time it remains unrecognized. While timely conducted treatment changes may avert periods of viral rebound, necessary treatment options and diagnostics may not be affordable in resource-constraint settings. Within this work, we assess distinct treatment- and diagnostic strategies against HIV-1 and evaluate their use in terms of patient health, economic means and reduction in HIV-1 onward transmission exemplarily for South Africa. We utilize optimal control theory to compute two distinct strategies: (i) Individualized treatment based on infrequent diagnostic schedules, typically encountered in medical applications (the diagnostic-guided strategy) and (ii) cohort-based treatment, which allows pro-active treatment adaptation (the pro-active strategy). Both strategies are compared to the current standard of care. We use a coarse-grained stochastic model of within-host HIV dynamics to assess all therapeutic strategies and provide pseudo-codes for solving the respective control problems. Both optimal strategies (i)-(ii) perform better that the standard of care and no treatment in terms of economic means, life prolongation and reduction in HIV-transmission. The diagnostic-driven optimal diagnostic-guided strategy suggests very rare diagnostics and performs marginally better than the optimal pro-active strategy. This indicates that current diagnostics (i.e. drug resistance tests) may be too costly to be fully utilized. Cross-sectional approaches may be used with marginal loss in performance until cost-efficient diagnostics become broadly available. The developed mathematical frameworks can be adapted to many related medical phenomena.

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