{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140612"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140612","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Computational Analysis and Network-based Modeling of Cross-Species Transmissions","abstract":"Zoonotic spillover of pathogens is the dominant cause of emerging infectious diseases.Cross-species transmission (CST) risks are accelerated by climate change, which alters animal habitats and aggregates new combinations of host species at high population density and elevations. Modeling CST dynamics is essential in ecology and computational epidemiology to enhance preparedness and resilience against future outbreaks. However, accurate prediction remains challenging due to biased pathogen sampling in existing CST databases and complex interactions among viral host range. This dissertation addresses three main challenges: (1) optimizing CST testing set selection through graph entropy frameworks; (2) modeling infectious pathways and biodiversity shifts with climate change scenarios; and (3) developing an accessible knowledge-based question and answering (QA) framework using Retrieval Augmented Generation (RAG) technology. Current viral databases consist mostly of pathogens in humans and domesticated animals, while the remaining vertebrate genera account for a mere 9%. Testing resources are limited for assessing indeterminate 800,000 to 1.5 million mammalian viruses with zoonotic potential. Furthermore, climate change will expose host species to novel ecological interactions and complicate efforts to identify infectious pathways. I leveraged information theory and graph entropy, where high entropy implies a more informative, diverse, and unpredictable network structure, to guide testing set selection, aiming to maximize the entropy and improve diversity of CST database. A graph representation constructed based on animal habitats, climate classification, and future climate scenarios, identifies biodiversity patterns in climate classifications and vulnerable hosts and viruses. Lastly, this dissertation introduces a knowledge graph-based CST information system for question answering (QA) using RAG, comparing multiple external database architectures, including Knowledge graphs, node embeddings, and vector databases. The evaluation framework integrates reasoning, summarization, and hallucination detection using curated unanswerable queries. Through computational modeling and graph-based analysis of CSTs, it identifies potential missing links and delivers an accessible and accurate CST information framework, facilitating early detection of CST risks and improving preparedness for future emerging infectious diseases.","abstract_html":"Zoonotic spillover of pathogens is the dominant cause of emerging infectious diseases.Cross-species transmission (CST) risks are accelerated by climate change, which alters animal habitats and aggregates new combinations of host species at high population density and elevations. Modeling CST dynamics is essential in ecology and computational epidemiology to enhance preparedness and resilience against future outbreaks. However, accurate prediction remains challenging due to biased pathogen sampling in existing CST databases and complex interactions among viral host range. This dissertation addresses three main challenges: (1) optimizing CST testing set selection through graph entropy frameworks; (2) modeling infectious pathways and biodiversity shifts with climate change scenarios; and (3) developing an accessible knowledge-based question and answering (QA) framework using Retrieval Augmented Generation (RAG) technology. Current viral databases consist mostly of pathogens in humans and domesticated animals, while the remaining vertebrate genera account for a mere 9%. Testing resources are limited for assessing indeterminate 800,000 to 1.5 million mammalian viruses with zoonotic potential. Furthermore, climate change will expose host species to novel ecological interactions and complicate efforts to identify infectious pathways. I leveraged information theory and graph entropy, where high entropy implies a more informative, diverse, and unpredictable network structure, to guide testing set selection, aiming to maximize the entropy and improve diversity of CST database. A graph representation constructed based on animal habitats, climate classification, and future climate scenarios, identifies biodiversity patterns in climate classifications and vulnerable hosts and viruses. Lastly, this dissertation introduces a knowledge graph-based CST information system for question answering (QA) using RAG, comparing multiple external database architectures, including Knowledge graphs, node embeddings, and vector databases. The evaluation framework integrates reasoning, summarization, and hallucination detection using curated unanswerable queries. Through computational modeling and graph-based analysis of CSTs, it identifies potential missing links and delivers an accessible and accurate CST information framework, facilitating early detection of CST risks and improving preparedness for future emerging infectious diseases.","abstract_has_math":false,"creators":["Kim, Yoonjin"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Computer Science & Applications","degree_department":"Computer Science and#38; Applications","school":null,"contributors":[],"advisors":[],"committee_chairs":["Heath, Lenwood S."],"committee_members":["Karpatne, Anuj","Viswanath, Bimal","Warren, Andrew Scott","Li, Song"],"year":2026,"date_issued":"2026-01-06","date_published":"2026-01-06","updated_at":"2026-07-22T22:20:43Z","subjects":["Graph analysis","computational biology","cross-species transmissions","computational epidemiology"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45314"],"render_values":[{"text":"vt_gsexam:45314","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140612","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Heath, Lenwood S."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Karpatne, Anuj","Viswanath, Bimal","Warren, Andrew Scott","Li, Song"]},{"key":"dc:contributor.department","label":"Department","values":["Computer Science and#38; Applications"]},{"key":"dc:creator","label":"Author","values":["Kim, Yoonjin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-07T09:00:41Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-07T09:00:41Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-01-06"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science & Applications"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Graph analysis","computational biology","cross-species transmissions","computational epidemiology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45314"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140612"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Zoonotic spillover of pathogens is the dominant cause of emerging infectious diseases.Cross-species transmission (CST) risks are accelerated by climate change, which alters animal habitats and aggregates new combinations of host species at high population density and elevations. Modeling CST dynamics is essential in ecology and computational epidemiology to enhance preparedness and resilience against future outbreaks. However, accurate prediction remains challenging due to biased pathogen sampling in existing CST databases and complex interactions among viral host range. This dissertation addresses three main challenges: (1) optimizing CST testing set selection through graph entropy frameworks; (2) modeling infectious pathways and biodiversity shifts with climate change scenarios; and (3) developing an accessible knowledge-based question and answering (QA) framework using Retrieval Augmented Generation (RAG) technology. Current viral databases consist mostly of pathogens in humans and domesticated animals, while the remaining vertebrate genera account for a mere 9%. Testing resources are limited for assessing indeterminate 800,000 to 1.5 million mammalian viruses with zoonotic potential. Furthermore, climate change will expose host species to novel ecological interactions and complicate efforts to identify infectious pathways. I leveraged information theory and graph entropy, where high entropy implies a more informative, diverse, and unpredictable network structure, to guide testing set selection, aiming to maximize the entropy and improve diversity of CST database. A graph representation constructed based on animal habitats, climate classification, and future climate scenarios, identifies biodiversity patterns in climate classifications and vulnerable hosts and viruses. Lastly, this dissertation introduces a knowledge graph-based CST information system for question answering (QA) using RAG, comparing multiple external database architectures, including Knowledge graphs, node embeddings, and vector databases. The evaluation framework integrates reasoning, summarization, and hallucination detection using curated unanswerable queries. Through computational modeling and graph-based analysis of CSTs, it identifies potential missing links and delivers an accessible and accurate CST information framework, facilitating early detection of CST risks and improving preparedness for future emerging infectious diseases."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["The majority of emerging infectious diseases (EIDs) that affect humans originate from non-human animals. The process known as zoonotic spillover, or cross-species transmission (CST), happens when pathogens are transmitted from one species to another. Climate change increases the risk of CST by forcing species into overlapping habitats and increasing contacts in certain regions, such as high elevation or highly dense areas. These environmental shifts the probability of viral spillover events and complicate global efforts to predict and mitigate future outbreaks. This dissertation focuses on developing computational methods to model and analyze CST dynamics, addressing three major challenges in data imbalance, bias, and incompleteness of current CST data sets. It integrates information theory and graph entropy, knowledge graphs (KGs), and retrieval augmented generation (RAG) to improve graph diversity, optimize testing strategies, and provide accessible and accurate information. There are three major research challenges in this work: 1) optimizing CST testing set selection using entropy based graph framework to maximize information gain, 2) construct heterogeneous graphs to model biodiversities per climate regions, infectious path changes under climate change scenarios, climate change scenarios, hosts, and viruses, and 3) developing question answering (QA) with RAG technique in the domain of CST. These computational approaches provide a scalable framework to understand the impact and potential risks in zoonotic emergence. Through modeling and computational analysis in CSTs, this research aims to enable early detection of zoonotic risks, raise preparedness for future outbreaks, and generate more trustworthy information for infectious diseases."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Computational Analysis and Network-based Modeling of Cross-Species Transmissions"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Heath, Lenwood S."],"dc:contributor.committeemember":["Karpatne, Anuj","Viswanath, Bimal","Warren, Andrew Scott","Li, Song"],"dc:contributor.department":["Computer Science and#38; Applications"],"dc:creator":["Kim, Yoonjin"],"dc:date.accessioned":["2026-01-07T09:00:41Z"],"dc:date.available":["2026-01-07T09:00:41Z"],"dc:date.issued":["2026-01-06"],"dc:description.abstract":["Zoonotic spillover of pathogens is the dominant cause of emerging infectious diseases.Cross-species transmission (CST) risks are accelerated by climate change, which alters animal habitats and aggregates new combinations of host species at high population density and elevations. Modeling CST dynamics is essential in ecology and computational epidemiology to enhance preparedness and resilience against future outbreaks. However, accurate prediction remains challenging due to biased pathogen sampling in existing CST databases and complex interactions among viral host range. This dissertation addresses three main challenges: (1) optimizing CST testing set selection through graph entropy frameworks; (2) modeling infectious pathways and biodiversity shifts with climate change scenarios; and (3) developing an accessible knowledge-based question and answering (QA) framework using Retrieval Augmented Generation (RAG) technology. Current viral databases consist mostly of pathogens in humans and domesticated animals, while the remaining vertebrate genera account for a mere 9%. Testing resources are limited for assessing indeterminate 800,000 to 1.5 million mammalian viruses with zoonotic potential. Furthermore, climate change will expose host species to novel ecological interactions and complicate efforts to identify infectious pathways. I leveraged information theory and graph entropy, where high entropy implies a more informative, diverse, and unpredictable network structure, to guide testing set selection, aiming to maximize the entropy and improve diversity of CST database. A graph representation constructed based on animal habitats, climate classification, and future climate scenarios, identifies biodiversity patterns in climate classifications and vulnerable hosts and viruses. Lastly, this dissertation introduces a knowledge graph-based CST information system for question answering (QA) using RAG, comparing multiple external database architectures, including Knowledge graphs, node embeddings, and vector databases. The evaluation framework integrates reasoning, summarization, and hallucination detection using curated unanswerable queries. Through computational modeling and graph-based analysis of CSTs, it identifies potential missing links and delivers an accessible and accurate CST information framework, facilitating early detection of CST risks and improving preparedness for future emerging infectious diseases."],"dc:description.abstractgeneral":["The majority of emerging infectious diseases (EIDs) that affect humans originate from non-human animals. The process known as zoonotic spillover, or cross-species transmission (CST), happens when pathogens are transmitted from one species to another. Climate change increases the risk of CST by forcing species into overlapping habitats and increasing contacts in certain regions, such as high elevation or highly dense areas. These environmental shifts the probability of viral spillover events and complicate global efforts to predict and mitigate future outbreaks. This dissertation focuses on developing computational methods to model and analyze CST dynamics, addressing three major challenges in data imbalance, bias, and incompleteness of current CST data sets. It integrates information theory and graph entropy, knowledge graphs (KGs), and retrieval augmented generation (RAG) to improve graph diversity, optimize testing strategies, and provide accessible and accurate information. There are three major research challenges in this work: 1) optimizing CST testing set selection using entropy based graph framework to maximize information gain, 2) construct heterogeneous graphs to model biodiversities per climate regions, infectious path changes under climate change scenarios, climate change scenarios, hosts, and viruses, and 3) developing question answering (QA) with RAG technique in the domain of CST. These computational approaches provide a scalable framework to understand the impact and potential risks in zoonotic emergence. Through modeling and computational analysis in CSTs, this research aims to enable early detection of zoonotic risks, raise preparedness for future outbreaks, and generate more trustworthy information for infectious diseases."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45314"],"dc:identifier.uri":["https://hdl.handle.net/10919/140612"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Graph analysis","computational biology","cross-species transmissions","computational epidemiology"],"dc:title":["Computational Analysis and Network-based Modeling of Cross-Species Transmissions"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Computer Science & Applications"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:43Z"}