{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/395423"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/395423","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"The spread of Nipah and chikungunya viruses: implications for control","abstract":"Infectious diseases have the potential to cause a substantial burden on public health. Nipah and chikungunya have been identified as two emerging viruses that pose a significant threat with pandemic potential. This has led to considerable investment in the development of vaccines against the two pathogens, especially by the Coalition for Epidemic Preparedness Innovation (CEPI). However, the threat from these viruses and the potential of new vaccines, including the way they should be deployed, remain poorly quantified. Nipah virus is a Paramyxovirus that circulates in Pteropus bats across South and Southeast Asia. Most outbreaks in humans are rare and localized, but infection is often lethal and human-to-human transmission has been observed. Chikungunya virus is an Alphavirus transmitted by Aedes mosquitoes that can cause chronic arthralgia and death. Most tropical and subtropical regions, representing 1.3 billion people, are at risk of transmission. What’s more, the potential expansion of the vectors’ geographic range due to climate change is expected to significantly increase the population at risk in coming decades. In the first half of this thesis, I focus on Nipah virus. Several Nipah vaccine and monoclonal antibody candidates are currently in development. However, they have primarily been derived from only two existing viral strains, raising questions as to what the unobserved spread and diversity of the virus might be in bat populations at different spatial scales. Using the most comprehensive genome set to date, I reconstructed a time-resolved phylogeny of Nipah virus and investigated its underlying spatial and genetic structure. I developed an analytical approach to infer the presence and spatial characteristics of genetic clusters based on observed viral diversity. I showed that current levels of surveillance might be missing up to 80% of Nipah viral diversity. Once medical countermeasures against Nipah virus become available, the scale and sporadicity of outbreaks will challenge the design of optimal deployment strategies. To help guide future response efforts, I developed a stochastic model to simulate outbreaks according to different epidemiological scenarios, incorporating different vaccine and monoclonal antibody rollout strategies, estimating how many infections, cases, and deaths could be averted. The second half of this thesis focuses on chikungunya virus. The first chikungunya vaccine was licensed in an endemic country, Brazil, in April 2025. However, it is still unclear how best to use it due to our poor understanding of the virus’s epidemiology. I developed a mathematical model to robustly reconstruct chikungunya circulation in each of the 27 states of Brazil since the first chikungunya cases were detected there in 2013. Pooling information from national surveillance data and publicly available serological surveys, I quantified spatiotemporal heterogeneities in chikungunya burden. I investigated sex- and age-dependent differences in disease detection and in mortality, finding that infections in females and in older age groups have a higher risk of reporting severe disease outcomes. Finally, leveraging state-specific estimates of accumulated immunity and past chikungunya attack rates, I projected the potential impact of different vaccination campaign strategies over the next five years. This thesis has contributed to filling critical knowledge gaps in two complex and understudied disease systems that represent a serious risk to human health. It has demonstrated how mathematical and statistical models can shed light on a broad range of key epidemiological questions, at a pivotal time for the prevention and control of these viruses. Most of this work has been conducted in collaboration with CEPI and has been communicated to key interested parties in the development and use of medical countermeasures against Nipah and chikungunya viruses, underscoring the public health relevance of its findings.","abstract_html":"Infectious diseases have the potential to cause a substantial burden on public health. Nipah and chikungunya have been identified as two emerging viruses that pose a significant threat with pandemic potential. This has led to considerable investment in the development of vaccines against the two pathogens, especially by the Coalition for Epidemic Preparedness Innovation (CEPI). However, the threat from these viruses and the potential of new vaccines, including the way they should be deployed, remain poorly quantified. Nipah virus is a Paramyxovirus that circulates in Pteropus bats across South and Southeast Asia. Most outbreaks in humans are rare and localized, but infection is often lethal and human-to-human transmission has been observed. Chikungunya virus is an Alphavirus transmitted by Aedes mosquitoes that can cause chronic arthralgia and death. Most tropical and subtropical regions, representing 1.3 billion people, are at risk of transmission. What’s more, the potential expansion of the vectors’ geographic range due to climate change is expected to significantly increase the population at risk in coming decades. In the first half of this thesis, I focus on Nipah virus. Several Nipah vaccine and monoclonal antibody candidates are currently in development. However, they have primarily been derived from only two existing viral strains, raising questions as to what the unobserved spread and diversity of the virus might be in bat populations at different spatial scales. Using the most comprehensive genome set to date, I reconstructed a time-resolved phylogeny of Nipah virus and investigated its underlying spatial and genetic structure. I developed an analytical approach to infer the presence and spatial characteristics of genetic clusters based on observed viral diversity. I showed that current levels of surveillance might be missing up to 80% of Nipah viral diversity. Once medical countermeasures against Nipah virus become available, the scale and sporadicity of outbreaks will challenge the design of optimal deployment strategies. To help guide future response efforts, I developed a stochastic model to simulate outbreaks according to different epidemiological scenarios, incorporating different vaccine and monoclonal antibody rollout strategies, estimating how many infections, cases, and deaths could be averted. The second half of this thesis focuses on chikungunya virus. The first chikungunya vaccine was licensed in an endemic country, Brazil, in April 2025. However, it is still unclear how best to use it due to our poor understanding of the virus’s epidemiology. I developed a mathematical model to robustly reconstruct chikungunya circulation in each of the 27 states of Brazil since the first chikungunya cases were detected there in 2013. Pooling information from national surveillance data and publicly available serological surveys, I quantified spatiotemporal heterogeneities in chikungunya burden. I investigated sex- and age-dependent differences in disease detection and in mortality, finding that infections in females and in older age groups have a higher risk of reporting severe disease outcomes. Finally, leveraging state-specific estimates of accumulated immunity and past chikungunya attack rates, I projected the potential impact of different vaccination campaign strategies over the next five years. This thesis has contributed to filling critical knowledge gaps in two complex and understudied disease systems that represent a serious risk to human health. It has demonstrated how mathematical and statistical models can shed light on a broad range of key epidemiological questions, at a pivotal time for the prevention and control of these viruses. Most of this work has been conducted in collaboration with CEPI and has been communicated to key interested parties in the development and use of medical countermeasures against Nipah and chikungunya viruses, underscoring the public health relevance of its findings.","abstract_has_math":false,"creators":["Cortes Azuero, Oscar"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Salje, Henrik"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-08-12","date_published":"2025-08-12","updated_at":"2026-07-22T22:24:01Z","subjects":["arboviruses","paramyxoviruses","chikungunya","nipah","phylogenetics","mathematical modelling","epidemiology","infectious diseases"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/9c052ee4-2ce8-44f4-9c5d-489ac73c5f6d/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.124940","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Salje, Henrik"]},{"key":"dc:creator","label":"Author","values":["Cortes Azuero, Oscar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-08-12"]},{"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/1810/395423"]},{"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":["arboviruses","paramyxoviruses","chikungunya","nipah","phylogenetics","mathematical modelling","epidemiology","infectious diseases"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/9c052ee4-2ce8-44f4-9c5d-489ac73c5f6d/download","http://purl.org/NET/rdflicense/allrightsreserved"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.124940"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/e97c6803-b5e5-42b7-a623-29aabad0fc02/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Infectious diseases have the potential to cause a substantial burden on public health. 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What’s more, the potential expansion of the vectors’ geographic range due to climate change is expected to significantly increase the population at risk in coming decades. In the first half of this thesis, I focus on Nipah virus. Several Nipah vaccine and monoclonal antibody candidates are currently in development. However, they have primarily been derived from only two existing viral strains, raising questions as to what the unobserved spread and diversity of the virus might be in bat populations at different spatial scales. Using the most comprehensive genome set to date, I reconstructed a time-resolved phylogeny of Nipah virus and investigated its underlying spatial and genetic structure. I developed an analytical approach to infer the presence and spatial characteristics of genetic clusters based on observed viral diversity. I showed that current levels of surveillance might be missing up to 80% of Nipah viral diversity. Once medical countermeasures against Nipah virus become available, the scale and sporadicity of outbreaks will challenge the design of optimal deployment strategies. To help guide future response efforts, I developed a stochastic model to simulate outbreaks according to different epidemiological scenarios, incorporating different vaccine and monoclonal antibody rollout strategies, estimating how many infections, cases, and deaths could be averted. The second half of this thesis focuses on chikungunya virus. The first chikungunya vaccine was licensed in an endemic country, Brazil, in April 2025. However, it is still unclear how best to use it due to our poor understanding of the virus’s epidemiology. I developed a mathematical model to robustly reconstruct chikungunya circulation in each of the 27 states of Brazil since the first chikungunya cases were detected there in 2013. Pooling information from national surveillance data and publicly available serological surveys, I quantified spatiotemporal heterogeneities in chikungunya burden. I investigated sex- and age-dependent differences in disease detection and in mortality, finding that infections in females and in older age groups have a higher risk of reporting severe disease outcomes. Finally, leveraging state-specific estimates of accumulated immunity and past chikungunya attack rates, I projected the potential impact of different vaccination campaign strategies over the next five years. This thesis has contributed to filling critical knowledge gaps in two complex and understudied disease systems that represent a serious risk to human health. It has demonstrated how mathematical and statistical models can shed light on a broad range of key epidemiological questions, at a pivotal time for the prevention and control of these viruses. 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This has led to considerable investment in the development of vaccines against the two pathogens, especially by the Coalition for Epidemic Preparedness Innovation (CEPI). However, the threat from these viruses and the potential of new vaccines, including the way they should be deployed, remain poorly quantified. Nipah virus is a Paramyxovirus that circulates in Pteropus bats across South and Southeast Asia. Most outbreaks in humans are rare and localized, but infection is often lethal and human-to-human transmission has been observed. Chikungunya virus is an Alphavirus transmitted by Aedes mosquitoes that can cause chronic arthralgia and death. Most tropical and subtropical regions, representing 1.3 billion people, are at risk of transmission. What’s more, the potential expansion of the vectors’ geographic range due to climate change is expected to significantly increase the population at risk in coming decades. In the first half of this thesis, I focus on Nipah virus. Several Nipah vaccine and monoclonal antibody candidates are currently in development. However, they have primarily been derived from only two existing viral strains, raising questions as to what the unobserved spread and diversity of the virus might be in bat populations at different spatial scales. Using the most comprehensive genome set to date, I reconstructed a time-resolved phylogeny of Nipah virus and investigated its underlying spatial and genetic structure. I developed an analytical approach to infer the presence and spatial characteristics of genetic clusters based on observed viral diversity. I showed that current levels of surveillance might be missing up to 80% of Nipah viral diversity. Once medical countermeasures against Nipah virus become available, the scale and sporadicity of outbreaks will challenge the design of optimal deployment strategies. To help guide future response efforts, I developed a stochastic model to simulate outbreaks according to different epidemiological scenarios, incorporating different vaccine and monoclonal antibody rollout strategies, estimating how many infections, cases, and deaths could be averted. The second half of this thesis focuses on chikungunya virus. The first chikungunya vaccine was licensed in an endemic country, Brazil, in April 2025. However, it is still unclear how best to use it due to our poor understanding of the virus’s epidemiology. I developed a mathematical model to robustly reconstruct chikungunya circulation in each of the 27 states of Brazil since the first chikungunya cases were detected there in 2013. Pooling information from national surveillance data and publicly available serological surveys, I quantified spatiotemporal heterogeneities in chikungunya burden. I investigated sex- and age-dependent differences in disease detection and in mortality, finding that infections in females and in older age groups have a higher risk of reporting severe disease outcomes. Finally, leveraging state-specific estimates of accumulated immunity and past chikungunya attack rates, I projected the potential impact of different vaccination campaign strategies over the next five years. This thesis has contributed to filling critical knowledge gaps in two complex and understudied disease systems that represent a serious risk to human health. It has demonstrated how mathematical and statistical models can shed light on a broad range of key epidemiological questions, at a pivotal time for the prevention and control of these viruses. 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