Resource allocation for control of infectious diseases in multiple independent populations: beyond cost-effectiveness analysis

Brandeau, Margaret L.; Zaric, Gregory S.; Richter, Anke

doi:10.1016/s0167-6296(03)00043-2

Cited by 138 publications

(119 citation statements)

References 30 publications

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“…For T ≤ 10, all budget is spent almost uniformly in the interval [0, T ] and for T > 10, the optimal solution has a similar structure to that in Figure 5. u (1) u (2) u (3) u (4) u (5) J(u) = 4.85130793476742 (c) The optimal trajectory u for T = 50 years. …”

Section: Varying Time Horizon Tmentioning

confidence: 99%

“…The changes in the optimal solution are depicted in Figure 8. u (2) u (3) u (4) u (5) J(u) = 2.61758340827 (a) All budgets are decreased two-fold. u (2) u (3) u (4) u (5) J(u) = 1.79836075178 (b) All budgets are increased five-fold.…”

Section: Varying Budget K (R)mentioning

confidence: 99%

“…u (2) u (3) u (4) u (5) J(u) = 2.61758340827 (a) All budgets are decreased two-fold. u (2) u (3) u (4) u (5) J(u) = 1.79836075178 (b) All budgets are increased five-fold. u (2) u (3) u (4) u (5) J(u) = 1.16626877104 (c) All budgets are increased ten-fold.…”

Section: Varying Budget K (R)mentioning

confidence: 99%

“…The control is the percentage effect of chemotherapy on the viral production. The control problem consists here of distributing a limited number of resources over a fixed time horizon, which is a recurring theme in many epidemic intervention studies; see, for example, [3,5,23].…”

Section: Introductionmentioning

confidence: 99%

“…However, the degree of complexity increases exponentially in T , and both approaches are infeasible when either or both the epidemic model and the control function are formulated as (non-linear) continuous-time functions. Such problems have been studied in, for example, [3,5,23]. In [5,23], it is shown that it is not generally possible to derive the optimal solution of such complex problem in a closed form and therefore one needs resort to approximation techniques.…”

Section: Introductionmentioning

confidence: 99%

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Controlling the number of HIV infectives in a mobile population

Sani

Kroese

2008

Mathematical Biosciences

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Abstract. The spread of the human immunodeficiency virus (HIV) depends prominently on the migration of people between different regions. An important consequence of this population mobility is that HIV control strategies that are optimal in a regional sense may not be optimal in a national sense. We formulate various mathematical control problems for HIV spread in mobile heterosexual populations, and show how optimal regional control strategies can be obtained that minimize the national spread of HIV. We apply the cross-entropy method to solve these highly multi-modal and non-linear optimization problems. We demonstrate the effectiveness of the method via a range of experiments and illustrate how the form of the optimal control function depends on the mathematical model used for the HIV spread.

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