Ch. 8. Stochastic processes in epidemic modelling and simulation

Greenhalgh, David

doi:10.1016/s0169-7161(03)21010-5

Cited by 2 publications

(2 citation statements)

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“…For a more recent application see, for example, Clancy and Piunovsky [6], who consider an optimal isolation policy for a deterministic epidemic model by minimizing the cost of isolating infectives from the susceptible population. A similar study is by Greenhalgh [11], who considers an epidemic spreading through a homogeneously mixing population. The control consists here of immunizing susceptible people and isolating and removing infected people from the rest of the population.…”

Section: Introductionmentioning

confidence: 91%

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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Section: Introductionmentioning

confidence: 91%

Controlling the number of HIV infectives in a mobile population

Sani

Kroese

2008

Mathematical Biosciences

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“…If the probability of infection is independent of the number of cases a susceptible individual meets, the disease can be modelled using a Greenwood model (Greenwood 1931). Where the probability of a susceptible individual becoming infected is proportional to the number of infectious cases met, then a ReedFrost model provides a better fit of the data (Greenhalgh 2003). Where multiple cases are considered, where the disease may confer some immunity, and where multiple samples are taken from the same animals over time to determine the disease status, the disease is considered to have started and ended half way between the adjacent sampling dates where the status has changed .…”

Section: Introductionmentioning

confidence: 99%