4.5 Article

Optimal control problems of epidemic systems with parameter uncertainties: Application to a malaria two-age-classes transmission model with asymptomatic carriers

期刊

MATHEMATICAL BIOSCIENCES
卷 261, 期 -, 页码 1-12

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.mbs.2014.11.005

关键词

Malaria; Nonlinear ODE models; Optimal control; Parameter uncertainty; MCMC

资金

  1. World Bank through Ministry of Education and Vocational training of United Republic of Tanzania
  2. Finnish Center of Excellence in Inverse Problems
  3. Chair of Excellence at the Carlos III University in Madrid Spain

向作者/读者索取更多资源

The main scope of this paper is to study the optimal control practices of malaria, by discussing the implementation of a catalog of optimal control strategies in presence of parameter uncertainties, which is typical of infectious diseases data. In this study we focus on a deterministic mathematical model for the transmission of malaria, including in particular asymptomatic carriers and two age classes in the human population. A partial qualitative analysis of the relevant ODE system has been carried out, leading to a realistic threshold parameter. For the deterministic model under consideration, four possible control strategies have been analyzed: the use of Long-lasting treated mosquito nets, indoor residual spraying, screening and treatment of symptomatic and asymptomatic individuals. The numerical results show that using optimal control the disease,can be brought to a stable disease free equilibrium when all four controls are used. The Incremental Cost-Effectiveness Ratio (ICER) for all possible combinations of the disease-control measures is determined. The numerical simulations of the optimal control in the presence of parameter uncertainty demonstrate the robustness of the optimal control: the main conclusions of the, optimal control remain unchanged, even if inevitable variability remains in the control profiles. The results provide a promising framework for the designing of cost-effective strategies for disease controls with multiple interventions, even under considerable uncertainty of model parameters. (C) 2014 Elsevier Inc. All rights reserved.

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