{"id":{"repo_id":"tu-berlin","oai_identifier":"oai:depositonce.tu-berlin.de:11303/24338"},"canonical_url":"https://search.dev.ndltd.org/etd/tu-berlin/oai:depositonce.tu-berlin.de:11303/24338","repository":{"repo_id":"tu-berlin","name":"Technische Universität Berlin","base_url":"https://api-depositonce.tu-berlin.de/server/oai/request"},"display":{"title":"From traffic to transmission: adapting an agent-based transport model to simulate the spreading of infectious diseases","abstract":"The main idea of this thesis originated from the onset of the COVID-19 pandemic in 2020, driven by the need for computer models to assist policymakers in making decisions about mitigation strategies. The thesis presents an approach to simulate the spreading of infectious diseases, such as COVID-19, by integrating transport modeling with epidemiological methods. The work builds upon a MATSim transport model which captures movement patterns of a synthetic population based on mobile phone data. These patterns are used to identify contacts between synthetic persons. The contacts can be attributed to certain contexts as the MATSim model is activity-based, meaning that different activity types such as work, school or leisure are included in the model. Thus, this serves as a good starting point for constructing an agent-based epidemiological model, as the transport model inherently includes a synthetic population and potential infection pathways. Building on this foundation, the thesis presents a model that integrates the approaches from transport modeling with a mechanistic infection model and a disease progression model to simulate the spreading of infectious diseases. The model is validated against infection and hospital numbers in Berlin and Cologne. The model was developed during the COVID-19 pandemic to advise the German government. Major benefits of the model are its ability to quantify the contributions of different activity types to the overall infection dynamics and to assess the outcomes of different intervention scenarios. For example, the results show that applying moderate contact restrictions across all activity types is more effective than completely shutting down some activities while leaving others entirely unrestricted. During the pandemic, the model was continuously refined to incorporate evolving societal and po- litical questions. Examples include the wearing of masks, contact tracing, rapid testing and activity- specific restrictions. The emergence of multiple virus mutations, combined with the availability of vaccinations, introduced further levels of complexity to the model. To represent the influence of im- munity on infection dynamics, an extension was developed to calculate the protection against infection for each synthetic person, based on their individual immunization history. The model adopts a data-based approach, with the most important input being population activity participation over time derived from cell-based mobile phone data. This data source provides inherent information on how the population changed its mobility behavior in the course of the pandemic. The cell-based mobile phone data revealed, for example, that the German population noticeably reduced its activity participation well before the government implemented restrictions during the first COVID- 19 wave. One limitation of cell-based data is its inability to differentiate activity participation by activity types. It only allows for a distinction between “home” and “not at home”. In this context, a further contribution of this thesis is to propose an approach to compensate for this deficiency by using GPS-based mobile phone data in combination with OpenStreetMap to identify activity participation by activity types. Results show that GPS-based data is significantly more effective in distinguishing activity types, enabling, for instance, the differentiation of school-related from work or leisure activities.","abstract_html":"The main idea of this thesis originated from the onset of the COVID-19 pandemic in 2020, driven by the need for computer models to assist policymakers in making decisions about mitigation strategies. The thesis presents an approach to simulate the spreading of infectious diseases, such as COVID-19, by integrating transport modeling with epidemiological methods. The work builds upon a MATSim transport model which captures movement patterns of a synthetic population based on mobile phone data. These patterns are used to identify contacts between synthetic persons. The contacts can be attributed to certain contexts as the MATSim model is activity-based, meaning that different activity types such as work, school or leisure are included in the model. Thus, this serves as a good starting point for constructing an agent-based epidemiological model, as the transport model inherently includes a synthetic population and potential infection pathways. Building on this foundation, the thesis presents a model that integrates the approaches from transport modeling with a mechanistic infection model and a disease progression model to simulate the spreading of infectious diseases. The model is validated against infection and hospital numbers in Berlin and Cologne. The model was developed during the COVID-19 pandemic to advise the German government. Major benefits of the model are its ability to quantify the contributions of different activity types to the overall infection dynamics and to assess the outcomes of different intervention scenarios. For example, the results show that applying moderate contact restrictions across all activity types is more effective than completely shutting down some activities while leaving others entirely unrestricted. During the pandemic, the model was continuously refined to incorporate evolving societal and po- litical questions. Examples include the wearing of masks, contact tracing, rapid testing and activity- specific restrictions. The emergence of multiple virus mutations, combined with the availability of vaccinations, introduced further levels of complexity to the model. To represent the influence of im- munity on infection dynamics, an extension was developed to calculate the protection against infection for each synthetic person, based on their individual immunization history. The model adopts a data-based approach, with the most important input being population activity participation over time derived from cell-based mobile phone data. This data source provides inherent information on how the population changed its mobility behavior in the course of the pandemic. The cell-based mobile phone data revealed, for example, that the German population noticeably reduced its activity participation well before the government implemented restrictions during the first COVID- 19 wave. One limitation of cell-based data is its inability to differentiate activity participation by activity types. It only allows for a distinction between “home” and “not at home”. In this context, a further contribution of this thesis is to propose an approach to compensate for this deficiency by using GPS-based mobile phone data in combination with OpenStreetMap to identify activity participation by activity types. Results show that GPS-based data is significantly more effective in distinguishing activity types, enabling, for instance, the differentiation of school-related from work or leisure activities.","abstract_has_math":false,"creators":["Müller, Sebastian Alexander"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Nagel, Kai"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025","date_published":"2025","updated_at":"2026-07-27T21:28:47Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":["https://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.14279/depositonce-23152"],"render_values":[{"text":"https://doi.org/10.14279/depositonce-23152","href":"https://doi.org/10.14279/depositonce-23152","code":true}]}]},"links":{"outbound_url":"https://depositonce.tu-berlin.de/handle/11303/24338","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nagel, Kai"]},{"key":"dc:creator","label":"Author","values":["Müller, Sebastian Alexander"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-04-16T09:10:57Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-04-16T09:10:57Z"]},{"key":"dc:date.issued","label":"Date","values":["2025"]},{"key":"dc:type","label":"Dc Type","values":["Doctoral Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://depositonce.tu-berlin.de/handle/11303/24338","https://doi.org/10.14279/depositonce-23152"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The main idea of this thesis originated from the onset of the COVID-19 pandemic in 2020, driven by the need for computer models to assist policymakers in making decisions about mitigation strategies. The thesis presents an approach to simulate the spreading of infectious diseases, such as COVID-19, by integrating transport modeling with epidemiological methods. The work builds upon a MATSim transport model which captures movement patterns of a synthetic population based on mobile phone data. These patterns are used to identify contacts between synthetic persons. The contacts can be attributed to certain contexts as the MATSim model is activity-based, meaning that different activity types such as work, school or leisure are included in the model. Thus, this serves as a good starting point for constructing an agent-based epidemiological model, as the transport model inherently includes a synthetic population and potential infection pathways. Building on this foundation, the thesis presents a model that integrates the approaches from transport modeling with a mechanistic infection model and a disease progression model to simulate the spreading of infectious diseases. The model is validated against infection and hospital numbers in Berlin and Cologne. The model was developed during the COVID-19 pandemic to advise the German government. Major benefits of the model are its ability to quantify the contributions of different activity types to the overall infection dynamics and to assess the outcomes of different intervention scenarios. For example, the results show that applying moderate contact restrictions across all activity types is more effective than completely shutting down some activities while leaving others entirely unrestricted. During the pandemic, the model was continuously refined to incorporate evolving societal and po- litical questions. Examples include the wearing of masks, contact tracing, rapid testing and activity- specific restrictions. The emergence of multiple virus mutations, combined with the availability of vaccinations, introduced further levels of complexity to the model. To represent the influence of im- munity on infection dynamics, an extension was developed to calculate the protection against infection for each synthetic person, based on their individual immunization history. The model adopts a data-based approach, with the most important input being population activity participation over time derived from cell-based mobile phone data. This data source provides inherent information on how the population changed its mobility behavior in the course of the pandemic. The cell-based mobile phone data revealed, for example, that the German population noticeably reduced its activity participation well before the government implemented restrictions during the first COVID- 19 wave. One limitation of cell-based data is its inability to differentiate activity participation by activity types. It only allows for a distinction between “home” and “not at home”. In this context, a further contribution of this thesis is to propose an approach to compensate for this deficiency by using GPS-based mobile phone data in combination with OpenStreetMap to identify activity participation by activity types. Results show that GPS-based data is significantly more effective in distinguishing activity types, enabling, for instance, the differentiation of school-related from work or leisure activities.","Die Kernidee dieser Arbeit entstand zu Beginn der COVID-19 Pandemie im Jahr 2020 und dem daraus resultierenden Bedarf an Computermodellen, die den politischen Entscheidungsträger*innen bei der Festlegung von Eindämmungsstrategien helfen. Die Arbeit präsentiert eine Methodik zur Simulation der Ausbreitung von Infektionskrankheiten wie COVID-19, bei der Ansätze aus der Verkehrsmodellierung mit epidemiologischen Methoden kombiniert werden. Die Arbeit baut auf einem MATSim- Verkehrsmodell auf, das die Bewegungsprofile einer synthetischen Bevölkerung auf der Grundlage von Mobilfunkdaten erfasst. Diese Bewegungsprofile werden verwendet, um Kontakte zwischen synthetischen Personen zu identifizieren. Die Kontakte können bestimmten Kontexten zugeordnet werden, da das MATSim-Modell Aktivitäten-basiert ist. Das bedeutet, dass verschiedene Aktivitätentypen wie Arbeit, Schule oder Freizeit in das Modell einbezogen werden. Dies ist ein guter Ausgangspunkt für die Erstellung eines agentenbasierten epidemiologischen Modells, da es bereits eine synthetische Bevölkerung und potenzielle Infektionswege umfasst. Aufbauend auf dieser Grundlage wird in dieser Arbeit ein Modell vorgestellt, das die Ansätze aus der Verkehrsmodellierung mit einem mechanischen Infektionsmodell und einem Krankheitsverlaufsmodell integriert, um die Ausbreitung von Infektionskrankheiten zu simulieren. Das Modell wird anhand von Infektions- und Krankenhauszahlen in Berlin und Köln validiert. Das Modell wurde während der COVID-19 Pandemie zur Beratung der deutschen Politik entwickelt. Der Hauptnutzen des Modells besteht darin, dass es in der Lage ist, den Beitrag der verschiedenen Aktivitätstypen zur Gesamtinfektionsdynamik zu quantifizieren und die Ergebnisse verschiedener Interventionsszenarien zu bewerten. Die Ergebnisse zeigen beispielsweise, dass die Anwendung moderater Kontaktbeschränkungen für alle Aktivitätentypen wirksamer ist als die vollständige Schließung einiger Aktivitäten, während andere vollständig geöffnet bleiben. Während der Pandemie wurde das Modell kontinuierlich weiterentwickelt, um die sich entwickelnden gesellschaftlichen und politischen Fragen einzubeziehen. Beispiele hierfür sind das Tragen von Masken, die Kontaktnachverfolgung, Schnelltests und Aktivitäten-basierte Beschränkungen. Das Auftreten mehrerer Virusmutationen in Verbindung mit der Verfügbarkeit von Impfungen führte zu einer weiteren Komplexitätsstufe im Modell. Um den Einfluss der Immunität auf die Infektionsdynamik im Modell abzubilden, wurde eine Erweiterung entwickelt, um den Schutz vor einer Infektion für jede synthetische Person auf der Grundlage ihrer individuellen Immunisierungshistorie zu berechnen. Das Modell basiert auf einem datenbasierten Ansatz, wobei der wichtigste Input die Bevölkerungsaktivität im zeitlichen Verlauf ist. Diese wird aus zellbasierten Mobilfunkdaten abgeleitet. Die zellbasierten Mobilfunkdaten zeigten beispielsweise in der ersten COVID-19-Welle, dass die deutsche Bevölkerung ihre Aktivität deutlich reduzierte, bevor die Regierung Einschränkungen einführte. Ein Nachteil der zellbasierten Daten besteht darin, dass sie nicht in der Lage sind, die Bevölkerungsaktivität nach Aktivitätentypen zu differenzieren. Sie erlauben lediglich eine Unterscheidung zwischen “zu Hause” und “nicht zu Hause”. Daher besteht ein weiterer Beitrag dieser Arbeit darin, einen Ansatz zu präsentieren, der diesen Nachteil ausgleicht, indem GPS-basierte Mobilfunkdaten in Kombination mit OpenStreetMap verwendet werden, um die Teilnahme an Aktivitäten nach Aktivitätstypen zu identifizieren. Die Ergebnisse zeigen, dass GPS-basierte Daten wesentlich effektiver bei der Unterscheidung von Aktivitätstypen sind und beispielsweise die Unterscheidung zwischen schulischen und beruflichen oder Freizeitaktivitäten ermöglichen."]},{"key":"dc:title","label":"Title","values":["From traffic to transmission: adapting an agent-based transport model to simulate the spreading of infectious diseases"]}]}],"canonical_facts":{"dc:contributor.advisor":["Nagel, Kai"],"dc:creator":["Müller, Sebastian Alexander"],"dc:date.accessioned":["2025-04-16T09:10:57Z"],"dc:date.available":["2025-04-16T09:10:57Z"],"dc:date.issued":["2025"],"dc:description.abstract":["The main idea of this thesis originated from the onset of the COVID-19 pandemic in 2020, driven by the need for computer models to assist policymakers in making decisions about mitigation strategies. The thesis presents an approach to simulate the spreading of infectious diseases, such as COVID-19, by integrating transport modeling with epidemiological methods. The work builds upon a MATSim transport model which captures movement patterns of a synthetic population based on mobile phone data. These patterns are used to identify contacts between synthetic persons. The contacts can be attributed to certain contexts as the MATSim model is activity-based, meaning that different activity types such as work, school or leisure are included in the model. Thus, this serves as a good starting point for constructing an agent-based epidemiological model, as the transport model inherently includes a synthetic population and potential infection pathways. Building on this foundation, the thesis presents a model that integrates the approaches from transport modeling with a mechanistic infection model and a disease progression model to simulate the spreading of infectious diseases. The model is validated against infection and hospital numbers in Berlin and Cologne. The model was developed during the COVID-19 pandemic to advise the German government. Major benefits of the model are its ability to quantify the contributions of different activity types to the overall infection dynamics and to assess the outcomes of different intervention scenarios. For example, the results show that applying moderate contact restrictions across all activity types is more effective than completely shutting down some activities while leaving others entirely unrestricted. During the pandemic, the model was continuously refined to incorporate evolving societal and po- litical questions. Examples include the wearing of masks, contact tracing, rapid testing and activity- specific restrictions. The emergence of multiple virus mutations, combined with the availability of vaccinations, introduced further levels of complexity to the model. To represent the influence of im- munity on infection dynamics, an extension was developed to calculate the protection against infection for each synthetic person, based on their individual immunization history. The model adopts a data-based approach, with the most important input being population activity participation over time derived from cell-based mobile phone data. This data source provides inherent information on how the population changed its mobility behavior in the course of the pandemic. The cell-based mobile phone data revealed, for example, that the German population noticeably reduced its activity participation well before the government implemented restrictions during the first COVID- 19 wave. One limitation of cell-based data is its inability to differentiate activity participation by activity types. It only allows for a distinction between “home” and “not at home”. In this context, a further contribution of this thesis is to propose an approach to compensate for this deficiency by using GPS-based mobile phone data in combination with OpenStreetMap to identify activity participation by activity types. Results show that GPS-based data is significantly more effective in distinguishing activity types, enabling, for instance, the differentiation of school-related from work or leisure activities.","Die Kernidee dieser Arbeit entstand zu Beginn der COVID-19 Pandemie im Jahr 2020 und dem daraus resultierenden Bedarf an Computermodellen, die den politischen Entscheidungsträger*innen bei der Festlegung von Eindämmungsstrategien helfen. Die Arbeit präsentiert eine Methodik zur Simulation der Ausbreitung von Infektionskrankheiten wie COVID-19, bei der Ansätze aus der Verkehrsmodellierung mit epidemiologischen Methoden kombiniert werden. Die Arbeit baut auf einem MATSim- Verkehrsmodell auf, das die Bewegungsprofile einer synthetischen Bevölkerung auf der Grundlage von Mobilfunkdaten erfasst. Diese Bewegungsprofile werden verwendet, um Kontakte zwischen synthetischen Personen zu identifizieren. Die Kontakte können bestimmten Kontexten zugeordnet werden, da das MATSim-Modell Aktivitäten-basiert ist. Das bedeutet, dass verschiedene Aktivitätentypen wie Arbeit, Schule oder Freizeit in das Modell einbezogen werden. Dies ist ein guter Ausgangspunkt für die Erstellung eines agentenbasierten epidemiologischen Modells, da es bereits eine synthetische Bevölkerung und potenzielle Infektionswege umfasst. Aufbauend auf dieser Grundlage wird in dieser Arbeit ein Modell vorgestellt, das die Ansätze aus der Verkehrsmodellierung mit einem mechanischen Infektionsmodell und einem Krankheitsverlaufsmodell integriert, um die Ausbreitung von Infektionskrankheiten zu simulieren. Das Modell wird anhand von Infektions- und Krankenhauszahlen in Berlin und Köln validiert. Das Modell wurde während der COVID-19 Pandemie zur Beratung der deutschen Politik entwickelt. Der Hauptnutzen des Modells besteht darin, dass es in der Lage ist, den Beitrag der verschiedenen Aktivitätstypen zur Gesamtinfektionsdynamik zu quantifizieren und die Ergebnisse verschiedener Interventionsszenarien zu bewerten. Die Ergebnisse zeigen beispielsweise, dass die Anwendung moderater Kontaktbeschränkungen für alle Aktivitätentypen wirksamer ist als die vollständige Schließung einiger Aktivitäten, während andere vollständig geöffnet bleiben. Während der Pandemie wurde das Modell kontinuierlich weiterentwickelt, um die sich entwickelnden gesellschaftlichen und politischen Fragen einzubeziehen. Beispiele hierfür sind das Tragen von Masken, die Kontaktnachverfolgung, Schnelltests und Aktivitäten-basierte Beschränkungen. Das Auftreten mehrerer Virusmutationen in Verbindung mit der Verfügbarkeit von Impfungen führte zu einer weiteren Komplexitätsstufe im Modell. Um den Einfluss der Immunität auf die Infektionsdynamik im Modell abzubilden, wurde eine Erweiterung entwickelt, um den Schutz vor einer Infektion für jede synthetische Person auf der Grundlage ihrer individuellen Immunisierungshistorie zu berechnen. Das Modell basiert auf einem datenbasierten Ansatz, wobei der wichtigste Input die Bevölkerungsaktivität im zeitlichen Verlauf ist. Diese wird aus zellbasierten Mobilfunkdaten abgeleitet. Die zellbasierten Mobilfunkdaten zeigten beispielsweise in der ersten COVID-19-Welle, dass die deutsche Bevölkerung ihre Aktivität deutlich reduzierte, bevor die Regierung Einschränkungen einführte. Ein Nachteil der zellbasierten Daten besteht darin, dass sie nicht in der Lage sind, die Bevölkerungsaktivität nach Aktivitätentypen zu differenzieren. Sie erlauben lediglich eine Unterscheidung zwischen “zu Hause” und “nicht zu Hause”. Daher besteht ein weiterer Beitrag dieser Arbeit darin, einen Ansatz zu präsentieren, der diesen Nachteil ausgleicht, indem GPS-basierte Mobilfunkdaten in Kombination mit OpenStreetMap verwendet werden, um die Teilnahme an Aktivitäten nach Aktivitätstypen zu identifizieren. Die Ergebnisse zeigen, dass GPS-basierte Daten wesentlich effektiver bei der Unterscheidung von Aktivitätstypen sind und beispielsweise die Unterscheidung zwischen schulischen und beruflichen oder Freizeitaktivitäten ermöglichen."],"dc:identifier.uri":["https://depositonce.tu-berlin.de/handle/11303/24338","https://doi.org/10.14279/depositonce-23152"],"dc:language.iso":["en"],"dc:rights.uri":["https://creativecommons.org/licenses/by/4.0/"],"dc:title":["From traffic to transmission: adapting an agent-based transport model to simulate the spreading of infectious diseases"],"dc:type":["Doctoral Thesis"]},"updated_at":"2026-07-27T21:28:47Z"}