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Disease elimination and re-emergence in differential-equation models.

ABSTRACT: Traditional differential equation models of disease transmission are often used to predict disease trajectories and evaluate the effectiveness of alternative intervention strategies. However, such models cannot account explicitly for probabilistic events, such as those that dominate dynamics when disease prevalence is low during the elimination and re-emergence phases of an outbreak. To account for the dynamics at low prevalence, i.e. the elimination and risk of disease re-emergence, without the added analytical and computational complexity of a stochastic model, we develop a novel application of control theory. We apply our approach to analyze historical data of measles elimination and re-emergence in Iceland from 1923 to 1938, predicting the temporal trajectory of local measles elimination and re-emerge as a result of disease migration from Copenhagen, Denmark.

SUBMITTER: Greenhalgh S

PROVIDER: S-EPMC4655889 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Disease elimination and re-emergence in differential-equation models.

Greenhalgh Scott S Galvani Alison P AP Medlock Jan J

Journal of theoretical biology 20151022

Traditional differential equation models of disease transmission are often used to predict disease trajectories and evaluate the effectiveness of alternative intervention strategies. However, such models cannot account explicitly for probabilistic events, such as those that dominate dynamics when disease prevalence is low during the elimination and re-emergence phases of an outbreak. To account for the dynamics at low prevalence, i.e. the elimination and risk of disease re-emergence, without the ...[more]

PMID: 26471072

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Dataset Information

Disease elimination and re-emergence in differential-equation models.

Publications

Disease elimination and re-emergence in differential-equation models.

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