@article{debarre_effect_2007,
Author = {Débarre, Florence and Bonhoeffer, Sebastian and
Regoes, Roland R.},
Title = {The effect of population structure on the emergence of
drug resistance during influenza pandemics},
Journal = {Journal of the Royal Society, Interface / the Royal
Society},
Volume = {4},
Number = {16},
Pages = {893--906},
Keywords = {Antiviral Agents, Drug Resistance, Viral, Humans,
Influenza A Virus, H5N1 Subtype, Influenza, Human,
Models, Biological, Models, Statistical, Oseltamivir,
Population Dynamics, Time Factors},
abstract = {The spread of H5N1 avian influenza and the recent high
numbers of confirmed human cases have raised
international concern about the possibility of a new
pandemic. Therefore, antiviral drugs are now being
stockpiled to be used as a first line of defence. The
large-scale use of antivirals will however exert a
strong selection pressure on the virus, and may lead to
the emergence of drug-resistant strains. A few
mathematical models have been developed to assess the
emergence of drug resistance during influenza
pandemics. These models, however, neglected the spatial
structure of large populations and the stochasticity of
epidemic and demographic processes. To assess the
impact of population structure and stochasticity, we
modify and extend a previous model of influenza
epidemics into a metapopulation model which takes into
account the division of large populations into smaller
units, and develop deterministic and stochastic
versions of the model. We find that the dynamics in a
fragmented population is less explosive, and, as a
result, prophylaxis will prevent more infections and
lead to fewer resistant cases in both the deterministic
and stochastic model. While in the deterministic model
the final level of resistance during treatment is not
affected by fragmentation, in the stochastic model it
is. Our results enable us to qualitatively extrapolate
the prediction of deterministic, homogeneous-mixing
models to more realistic scenarios.},
doi = {10.1098/rsif.2007.1126},
issn = {1742-5689},
language = {eng},
month = oct,
pmcid = {PMC2394556},
pmid = {17609176},
year = 2007
}
Author = {Débarre, Florence and Bonhoeffer, Sebastian and
Regoes, Roland R.},
Title = {The effect of population structure on the emergence of
drug resistance during influenza pandemics},
Journal = {Journal of the Royal Society, Interface / the Royal
Society},
Volume = {4},
Number = {16},
Pages = {893--906},
Keywords = {Antiviral Agents, Drug Resistance, Viral, Humans,
Influenza A Virus, H5N1 Subtype, Influenza, Human,
Models, Biological, Models, Statistical, Oseltamivir,
Population Dynamics, Time Factors},
abstract = {The spread of H5N1 avian influenza and the recent high
numbers of confirmed human cases have raised
international concern about the possibility of a new
pandemic. Therefore, antiviral drugs are now being
stockpiled to be used as a first line of defence. The
large-scale use of antivirals will however exert a
strong selection pressure on the virus, and may lead to
the emergence of drug-resistant strains. A few
mathematical models have been developed to assess the
emergence of drug resistance during influenza
pandemics. These models, however, neglected the spatial
structure of large populations and the stochasticity of
epidemic and demographic processes. To assess the
impact of population structure and stochasticity, we
modify and extend a previous model of influenza
epidemics into a metapopulation model which takes into
account the division of large populations into smaller
units, and develop deterministic and stochastic
versions of the model. We find that the dynamics in a
fragmented population is less explosive, and, as a
result, prophylaxis will prevent more infections and
lead to fewer resistant cases in both the deterministic
and stochastic model. While in the deterministic model
the final level of resistance during treatment is not
affected by fragmentation, in the stochastic model it
is. Our results enable us to qualitatively extrapolate
the prediction of deterministic, homogeneous-mixing
models to more realistic scenarios.},
doi = {10.1098/rsif.2007.1126},
issn = {1742-5689},
language = {eng},
month = oct,
pmcid = {PMC2394556},
pmid = {17609176},
year = 2007
}