{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/328046"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/328046","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"A cross-scale model for the evolution of influenza within a single season","abstract":"In this thesis we develop a mathematical cross-scale model for the evolution of influenza within a single season. We model evolution as the emergence and spread of a mutant strain in a population that is already invaded by a parent resident strain. This allows us to investigate both the emergence dynamics of a mutant strain as well as the subsequent competition dynamics between the two strains. Our main research goal is to study the effects of a homologous vaccine against the resident strain on the epidemiological and evolutionary dynamics of the disease. Due to the complexity of cross-scale models, we first develop a simpler population-level SIR-type model for the evolution of influenza. Assuming an outbreak that is initiated by a single resident strain, we study the significance of the mutant’s emergence time by introducing it in the population at different times. We then also derive a probability density function for the emergence of the mutant in the population. Finally we incorporate vaccination to our model, and arrive at the conclusion that intermediate levels of vaccine- induced immuno-protection are the most beneficial for the emergence and spread of the mutant strain. We then start building towards a cross-scale model by developing a dynamical within-host model for the evolution of influenza. Our goal is for emergence to be a stochastic event, so we derive a probability density for the within-host emergence of a mutant strain. We also incorporate vaccination to our model and assess its impact on the viral loads of the two strains. Having analyzed our within-host model, we then couple it with a between-host SI model. The links between the two scales are the population-level transmission rates, which we assume are linear functions of the within-host viral load. We first investigate how varying the within-host parameters affects the population-level fitness of the two strains, and then we study our model’s results under different forms of the within-host emergence density. Finally we add vaccination to our cross-scale model, and arrive at the same conclusion that intermediate values of immuno-protection are the most inducive to the emergence and spread of a mutant strain in the population.","abstract_html":"In this thesis we develop a mathematical cross-scale model for the evolution of influenza within a single season. We model evolution as the emergence and spread of a mutant strain in a population that is already invaded by a parent resident strain. This allows us to investigate both the emergence dynamics of a mutant strain as well as the subsequent competition dynamics between the two strains. Our main research goal is to study the effects of a homologous vaccine against the resident strain on the epidemiological and evolutionary dynamics of the disease. Due to the complexity of cross-scale models, we first develop a simpler population-level SIR-type model for the evolution of influenza. Assuming an outbreak that is initiated by a single resident strain, we study the significance of the mutant’s emergence time by introducing it in the population at different times. We then also derive a probability density function for the emergence of the mutant in the population. Finally we incorporate vaccination to our model, and arrive at the conclusion that intermediate levels of vaccine- induced immuno-protection are the most beneficial for the emergence and spread of the mutant strain. We then start building towards a cross-scale model by developing a dynamical within-host model for the evolution of influenza. Our goal is for emergence to be a stochastic event, so we derive a probability density for the within-host emergence of a mutant strain. We also incorporate vaccination to our model and assess its impact on the viral loads of the two strains. Having analyzed our within-host model, we then couple it with a between-host SI model. The links between the two scales are the population-level transmission rates, which we assume are linear functions of the within-host viral load. We first investigate how varying the within-host parameters affects the population-level fitness of the two strains, and then we study our model’s results under different forms of the within-host emergence density. Finally we add vaccination to our cross-scale model, and arrive at the same conclusion that intermediate values of immuno-protection are the most inducive to the emergence and spread of a mutant strain in the population.","abstract_has_math":false,"creators":["Karamitsou, Venetia"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Gog, Julia"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-07-11","date_published":"2021-07-11","updated_at":"2026-07-22T22:24:10Z","subjects":["influenza","math model","cross-scale model","multi-strain model"],"languages":["eng"],"rights":[],"rights_urls":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/c9f12727-6762-4dcf-9166-917f8d7addf5/download","https://www.rioxx.net/licenses/all-rights-reserved/"],"identifier_entries":[{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000312407214"],"render_values":[{"text":"0000-0003-1240-7214","href":"https://orcid.org/0000-0003-1240-7214","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.75497","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Gog, Julia"]},{"key":"dc:creator","label":"Author","values":["Karamitsou, Venetia"]},{"key":"dc:creator.authoridentifier","label":"Author Identifier","values":["0000000312407214"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2021-07-11"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/328046"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["influenza","math model","cross-scale model","multi-strain model"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/c9f12727-6762-4dcf-9166-917f8d7addf5/download","https://www.rioxx.net/licenses/all-rights-reserved/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["10.17863/CAM.75497"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/0957e518-23de-4018-aeb5-0f4bfcc11e93/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis we develop a mathematical cross-scale model for the evolution of influenza within a single season. 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Finally we incorporate vaccination to our model, and arrive at the conclusion that intermediate levels of vaccine- induced immuno-protection are the most beneficial for the emergence and spread of the mutant strain. We then start building towards a cross-scale model by developing a dynamical within-host model for the evolution of influenza. Our goal is for emergence to be a stochastic event, so we derive a probability density for the within-host emergence of a mutant strain. We also incorporate vaccination to our model and assess its impact on the viral loads of the two strains. Having analyzed our within-host model, we then couple it with a between-host SI model. The links between the two scales are the population-level transmission rates, which we assume are linear functions of the within-host viral load. We first investigate how varying the within-host parameters affects the population-level fitness of the two strains, and then we study our model’s results under different forms of the within-host emergence density. Finally we add vaccination to our cross-scale model, and arrive at the same conclusion that intermediate values of immuno-protection are the most inducive to the emergence and spread of a mutant strain in the population."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["6e22fab8d0dcf864bbb13cc708cb7dc7","353adac0d1ebdfd65ab16480263c3c87"]},{"key":"dc:title","label":"Title","values":["A cross-scale model for the evolution of influenza within a single season"]}]}],"canonical_facts":{"dc:contributor.advisor":["Gog, Julia"],"dc:creator":["Karamitsou, Venetia"],"dc:creator.authoridentifier":["0000000312407214"],"dc:date.issued":["2021-07-11"],"dc:description.abstract":["In this thesis we develop a mathematical cross-scale model for the evolution of influenza within a single season. We model evolution as the emergence and spread of a mutant strain in a population that is already invaded by a parent resident strain. 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We then start building towards a cross-scale model by developing a dynamical within-host model for the evolution of influenza. Our goal is for emergence to be a stochastic event, so we derive a probability density for the within-host emergence of a mutant strain. We also incorporate vaccination to our model and assess its impact on the viral loads of the two strains. Having analyzed our within-host model, we then couple it with a between-host SI model. The links between the two scales are the population-level transmission rates, which we assume are linear functions of the within-host viral load. We first investigate how varying the within-host parameters affects the population-level fitness of the two strains, and then we study our model’s results under different forms of the within-host emergence density. 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