{"id":{"repo_id":"anu","oai_identifier":"oai:openresearch-repository.anu.edu.au:1885/733724776"},"canonical_url":"https://search.dev.ndltd.org/etd/anu/oai:openresearch-repository.anu.edu.au:1885/733724776","repository":{"repo_id":"anu","name":"Australian National University","base_url":"https://openresearch-repository.anu.edu.au/server/oai/request"},"display":{"title":"Influence of network structure on disease spread and persistence","abstract":"Mechanistic models are essential for understanding disease dynamics, predicting outcomes, and informing policy and intervention decisions. Contact plays a central role in the spread of disease between individuals, and networks offer a convenient representation of contact patterns. This thesis explores the effects of network structure on transmission dynamics using two diseases as case studies: African swine fever (ASF), a fast-spreading viral disease found in wild and domestic pigs, and lymphatic filariasis (LF), a slow-spreading vector-borne filarial disease in humans. With these case studies, I explore three themes: modelling diseases with complex transmission cycles, the impact of network structure on critical thresholds and disease persistence, and how network structure influences surveillance and interventions. Under the first theme, for ASF in wild pigs, I showed that the timing of birth and death processes affects disease persistence and that stochasticity and network structure affect a model's ability to reproduce previous outbreak dynamics. For LF, I found it essential to include variable daytime and nighttime transmission to capture known clustering. I also found that in a low-prevalence setting, the model of larvae maturation in the mosquito vector may have limited impact. For the second theme, I found that ASF models with high spatial heterogeneity had a decreased likelihood of long-term persistence in Baltic wild boar populations. In Australia, while ASF persistence was highly dependent on local ecological conditions and metapopulation topology, I found that long-term persistence in feral pigs was unlikely. For LF, the interplay of spatial and individual heterogeneity was complex. Unlike well-mixed models, spatially heterogeneous models displayed limited population-level critical threshold behaviour, which has implications for intervention strategies. In the third theme, for ASF, network structure affected the relative importance of spread from pig carcasses, influencing the efficacy of potential interventions. In northern Australia, surveillance is hampered by short carcass decay periods and fast spread, while in southern regions, longer decay and slower spread could enhance surveillance sensitivity. For LF, increased spatial heterogeneity decreased intervention efficacy. It also dampened infection rebound, and residual infections remained in more spatially localised areas, which could reduce the ability of current surveys to detect continued transmission. In a low-prevalence, highly focal setting, successful targeted surveillance and treatment strategies required significant testing compared to the existing blanket treatment approaches. Both case studies highlight the complex relationship between network structure and disease and population characteristics, which influences long-term persistence, intervention, and surveillance strategies.","abstract_html":"Mechanistic models are essential for understanding disease dynamics, predicting outcomes, and informing policy and intervention decisions. Contact plays a central role in the spread of disease between individuals, and networks offer a convenient representation of contact patterns. This thesis explores the effects of network structure on transmission dynamics using two diseases as case studies: African swine fever (ASF), a fast-spreading viral disease found in wild and domestic pigs, and lymphatic filariasis (LF), a slow-spreading vector-borne filarial disease in humans. With these case studies, I explore three themes: modelling diseases with complex transmission cycles, the impact of network structure on critical thresholds and disease persistence, and how network structure influences surveillance and interventions. Under the first theme, for ASF in wild pigs, I showed that the timing of birth and death processes affects disease persistence and that stochasticity and network structure affect a model's ability to reproduce previous outbreak dynamics. For LF, I found it essential to include variable daytime and nighttime transmission to capture known clustering. I also found that in a low-prevalence setting, the model of larvae maturation in the mosquito vector may have limited impact. For the second theme, I found that ASF models with high spatial heterogeneity had a decreased likelihood of long-term persistence in Baltic wild boar populations. In Australia, while ASF persistence was highly dependent on local ecological conditions and metapopulation topology, I found that long-term persistence in feral pigs was unlikely. For LF, the interplay of spatial and individual heterogeneity was complex. Unlike well-mixed models, spatially heterogeneous models displayed limited population-level critical threshold behaviour, which has implications for intervention strategies. In the third theme, for ASF, network structure affected the relative importance of spread from pig carcasses, influencing the efficacy of potential interventions. In northern Australia, surveillance is hampered by short carcass decay periods and fast spread, while in southern regions, longer decay and slower spread could enhance surveillance sensitivity. For LF, increased spatial heterogeneity decreased intervention efficacy. It also dampened infection rebound, and residual infections remained in more spatially localised areas, which could reduce the ability of current surveys to detect continued transmission. In a low-prevalence, highly focal setting, successful targeted surveillance and treatment strategies required significant testing compared to the existing blanket treatment approaches. Both case studies highlight the complex relationship between network structure and disease and population characteristics, which influences long-term persistence, intervention, and surveillance strategies.","abstract_has_math":false,"creators":["Shaw, Callum"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024","date_published":"2024","updated_at":"2026-07-24T00:55:07Z","subjects":[],"languages":["en_AU"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1885/733724776","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Shaw, Callum"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-11-15T09:46:54Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-11-15T09:46:54Z"]},{"key":"dc:date.issued","label":"Date","values":["2024"]},{"key":"dc:type","label":"Dc Type","values":["Thesis (PhD)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_AU"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1885/733724776"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Mechanistic models are essential for understanding disease dynamics, predicting outcomes, and informing policy and intervention decisions. Contact plays a central role in the spread of disease between individuals, and networks offer a convenient representation of contact patterns. This thesis explores the effects of network structure on transmission dynamics using two diseases as case studies: African swine fever (ASF), a fast-spreading viral disease found in wild and domestic pigs, and lymphatic filariasis (LF), a slow-spreading vector-borne filarial disease in humans. With these case studies, I explore three themes: modelling diseases with complex transmission cycles, the impact of network structure on critical thresholds and disease persistence, and how network structure influences surveillance and interventions. Under the first theme, for ASF in wild pigs, I showed that the timing of birth and death processes affects disease persistence and that stochasticity and network structure affect a model's ability to reproduce previous outbreak dynamics. For LF, I found it essential to include variable daytime and nighttime transmission to capture known clustering. I also found that in a low-prevalence setting, the model of larvae maturation in the mosquito vector may have limited impact. For the second theme, I found that ASF models with high spatial heterogeneity had a decreased likelihood of long-term persistence in Baltic wild boar populations. In Australia, while ASF persistence was highly dependent on local ecological conditions and metapopulation topology, I found that long-term persistence in feral pigs was unlikely. For LF, the interplay of spatial and individual heterogeneity was complex. Unlike well-mixed models, spatially heterogeneous models displayed limited population-level critical threshold behaviour, which has implications for intervention strategies. In the third theme, for ASF, network structure affected the relative importance of spread from pig carcasses, influencing the efficacy of potential interventions. In northern Australia, surveillance is hampered by short carcass decay periods and fast spread, while in southern regions, longer decay and slower spread could enhance surveillance sensitivity. For LF, increased spatial heterogeneity decreased intervention efficacy. It also dampened infection rebound, and residual infections remained in more spatially localised areas, which could reduce the ability of current surveys to detect continued transmission. In a low-prevalence, highly focal setting, successful targeted surveillance and treatment strategies required significant testing compared to the existing blanket treatment approaches. Both case studies highlight the complex relationship between network structure and disease and population characteristics, which influences long-term persistence, intervention, and surveillance strategies."]},{"key":"dc:title","label":"Title","values":["Influence of network structure on disease spread and persistence"]}]}],"canonical_facts":{"dc:creator":["Shaw, Callum"],"dc:date.accessioned":["2024-11-15T09:46:54Z"],"dc:date.available":["2024-11-15T09:46:54Z"],"dc:date.issued":["2024"],"dc:description.abstract":["Mechanistic models are essential for understanding disease dynamics, predicting outcomes, and informing policy and intervention decisions. Contact plays a central role in the spread of disease between individuals, and networks offer a convenient representation of contact patterns. This thesis explores the effects of network structure on transmission dynamics using two diseases as case studies: African swine fever (ASF), a fast-spreading viral disease found in wild and domestic pigs, and lymphatic filariasis (LF), a slow-spreading vector-borne filarial disease in humans. With these case studies, I explore three themes: modelling diseases with complex transmission cycles, the impact of network structure on critical thresholds and disease persistence, and how network structure influences surveillance and interventions. Under the first theme, for ASF in wild pigs, I showed that the timing of birth and death processes affects disease persistence and that stochasticity and network structure affect a model's ability to reproduce previous outbreak dynamics. For LF, I found it essential to include variable daytime and nighttime transmission to capture known clustering. I also found that in a low-prevalence setting, the model of larvae maturation in the mosquito vector may have limited impact. For the second theme, I found that ASF models with high spatial heterogeneity had a decreased likelihood of long-term persistence in Baltic wild boar populations. In Australia, while ASF persistence was highly dependent on local ecological conditions and metapopulation topology, I found that long-term persistence in feral pigs was unlikely. For LF, the interplay of spatial and individual heterogeneity was complex. Unlike well-mixed models, spatially heterogeneous models displayed limited population-level critical threshold behaviour, which has implications for intervention strategies. In the third theme, for ASF, network structure affected the relative importance of spread from pig carcasses, influencing the efficacy of potential interventions. In northern Australia, surveillance is hampered by short carcass decay periods and fast spread, while in southern regions, longer decay and slower spread could enhance surveillance sensitivity. For LF, increased spatial heterogeneity decreased intervention efficacy. It also dampened infection rebound, and residual infections remained in more spatially localised areas, which could reduce the ability of current surveys to detect continued transmission. In a low-prevalence, highly focal setting, successful targeted surveillance and treatment strategies required significant testing compared to the existing blanket treatment approaches. Both case studies highlight the complex relationship between network structure and disease and population characteristics, which influences long-term persistence, intervention, and surveillance strategies."],"dc:identifier.uri":["https://hdl.handle.net/1885/733724776"],"dc:language.iso":["en_AU"],"dc:title":["Influence of network structure on disease spread and persistence"],"dc:type":["Thesis (PhD)"]},"updated_at":"2026-07-24T00:55:07Z"}