Optimal Resource Allocation for HIV Prevention and Control
When dealing with economically and socially significant infectious diseases, in particular AIDS and tuberculosis, the central problem is to optimally distribute the limited resources among different treatment and prophylaxis programs. The main difficulty in doing so is that while the individual-level effect of these interventions can be determined using controlled trials, their effectiveness as public health interventions cannot be ascertained with certainty. This is due to the fact that affected populations are different not only in terms of the disease transmission dynamics, but also in the efficacy of available instruments given a specific population structure. Identifying the optimal strategy of resource allocation must be based on a (dynamic) model of the underlying medical, biological, and social processes that captures the relevant features of the population.
This project has been funded in whole or in part with Federal funds from the Centers for Disease Control and Prevention/OID/NCHHSTP/DSTDP, Department of Health and Human Services, under Interagency Agreement No. 17FED1710397.
Dmitry Gromov thanks to the International Union of Biological Sciences (IUBS) for partial support of living expenses in Moscow, during the 17th BIOMAT International Symposium, October 29–November 04, 2017.
- 1.S.S. Alistar, D.K. Owens, M.L. Brandeau, Effectiveness and cost effectiveness of oral pre-exposure prophylaxis in a portfolio of prevention programs for injection drug users in mixed HIV epidemics. PLoS One 9(1), e86584 (2014)Google Scholar
- 4.S.E. Bellan, J. Dushoff, A.P. Galvani, L.A. Meyers, Reassessment of HIV-1 acute phase infectivity: accounting for heterogeneity and study design with simulated cohorts. PLoS Med. 12(3), e1001801 (2015)Google Scholar
- 6.O. Diekmann, J.A.P. Heesterbeek, Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation. Mathematical and Computational Biology, vol. 5 (Wiley, New York, 2000)Google Scholar
- 7.D. Donnell, J.M. Baeten, J. Kiarie, K.K. Thomas, W. Stevens, C.R. Cohen, J. McIntyre, J.R. Lingappa, C. Celum, Partners in Prevention HSV/HIV Transmission Study Team et al., Heterosexual HIV-1 transmission after initiation of antiretroviral therapy: a prospective cohort analysis. Lancet 375(9731), 2092–2098 (2010)Google Scholar
- 8.A. Gábor, J.R. Banga, Robust and efficient parameter estimation in dynamic models of biological systems. BMC Syst. Biol. 9(1), 74 (2015)Google Scholar
- 9.D. Gromov, I. Bulla, O.S. Serea, E.O. Romero-Severson, Numerical optimal control for HIV prevention with dynamic budget allocation. Math. Med. Biol. J. IMA dqx015 (2017). https://doi.org/10.1093/imammb/dqx015
- 14.A.V. Rao, A survey of numerical methods for optimal control. Adv. Astronaut. Sci. 135(1), 497–528 (2009)Google Scholar
- 17.A. Smith, I. Miles, B. Le, T. Finlayson, A. Oster, E. DiNenno, Prevalence and awareness of HIV infection among men who have sex with men – 21 Cities, US 2008. Morb. Mortal. Wkly Rep. 59(37), 1201–1227 (2010)Google Scholar