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Natural, Persistent Oscillations in a Spatial Multi-Strain Disease System with Application to Dengue

Overview of attention for article published in PLoS Computational Biology, October 2013
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Title
Natural, Persistent Oscillations in a Spatial Multi-Strain Disease System with Application to Dengue
Published in
PLoS Computational Biology, October 2013
DOI 10.1371/journal.pcbi.1003308
Pubmed ID
Authors

José Lourenço, Mario Recker

Abstract

Many infectious diseases are not maintained in a state of equilibrium but exhibit significant fluctuations in prevalence over time. For pathogens that consist of multiple antigenic types or strains, such as influenza, malaria or dengue, these fluctuations often take on the form of regular or irregular epidemic outbreaks in addition to oscillatory prevalence levels of the constituent strains. To explain the observed temporal dynamics and structuring in pathogen populations, epidemiological multi-strain models have commonly evoked strong immune interactions between strains as the predominant driver. Here, with specific reference to dengue, we show how spatially explicit, multi-strain systems can exhibit all of the described epidemiological dynamics even in the absence of immune competition. Instead, amplification of natural stochastic differences in disease transmission, can give rise to persistent oscillations comprising semi-regular epidemic outbreaks and sequential dominance of dengue's four serotypes. Not only can this mechanism explain observed differences in serotype and disease distributions between neighbouring geographical areas, it also has important implications for inferring the nature and epidemiological consequences of immune mediated competition in multi-strain pathogen systems.

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Mendeley readers

The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
United Kingdom 1 <1%
Portugal 1 <1%
Vietnam 1 <1%
Unknown 109 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 26%
Student > Ph. D. Student 28 24%
Student > Master 11 10%
Professor > Associate Professor 6 5%
Student > Bachelor 5 4%
Other 18 16%
Unknown 17 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 29%
Mathematics 15 13%
Medicine and Dentistry 14 12%
Engineering 6 5%
Biochemistry, Genetics and Molecular Biology 4 3%
Other 21 18%
Unknown 22 19%