{"id":{"repo_id":"uvic","oai_identifier":"oai:dspace.library.uvic.ca:1828/3629"},"canonical_url":"https://search.dev.ndltd.org/etd/uvic/oai:dspace.library.uvic.ca:1828/3629","repository":{"repo_id":"uvic","name":"University of Victoria (Canada)","base_url":"https://dspace.library.uvic.ca/server/oai/request"},"display":{"title":"Space-time surveillance of emerging infectious disease","abstract":"Emerging diseases are an increasingly important public health problem. This research investigates space-time disease surveillance for emerging infectious diseases. A system was developed in Sri Lanka monitoring clinical diagnoses in cattle, poultry and buffalo. Veterinarians submitted surveys using mobile phones and GPS. This surveillance system proved to be both feasible and acceptable and provided timely information on animal health patterns in Sri Lanka. A critical review of software and methods for space-time disease surveillance provides guidance on the selection and implementation of appropriate analytic methods for surveillance data. For the data collected in this research, a hidden Markov model is developed which estimates region-specific prevalence estimates after controlling for sentinel-level factors. The use of cluster detection methods in surveillance research is demonstrated using data from an outbreak of suspected leptospirosis in Sri Lanka in 2005-2009.","abstract_html":"Emerging diseases are an increasingly important public health problem. This research investigates space-time disease surveillance for emerging infectious diseases. A system was developed in Sri Lanka monitoring clinical diagnoses in cattle, poultry and buffalo. Veterinarians submitted surveys using mobile phones and GPS. This surveillance system proved to be both feasible and acceptable and provided timely information on animal health patterns in Sri Lanka. A critical review of software and methods for space-time disease surveillance provides guidance on the selection and implementation of appropriate analytic methods for surveillance data. For the data collected in this research, a hidden Markov model is developed which estimates region-specific prevalence estimates after controlling for sentinel-level factors. The use of cluster detection methods in surveillance research is demonstrated using data from an outbreak of suspected leptospirosis in Sri Lanka in 2005-2009.","abstract_has_math":false,"creators":["Robertson, Colin John"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Nelson, Trisalyn"],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-10-19","date_published":"2011-10-19","updated_at":"2026-08-21T16:50:31Z","subjects":["infectious diseases","Sri Lanka","leptospirosis"],"languages":["en","English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1828/3629","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://dspace.library.uvic.ca/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Adspace.library.uvic.ca%3A1828%2F3629","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.supervisor","label":"Supervisor","values":["Nelson, Trisalyn"]},{"key":"dc:creator","label":"Author","values":["Robertson, Colin John"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2011-10-19T15:22:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2011-10-19T15:22:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2011-10-19"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["infectious diseases","Sri Lanka","leptospirosis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1828/3629"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Emerging diseases are an increasingly important public health problem. This research investigates space-time disease surveillance for emerging infectious diseases. A system was developed in Sri Lanka monitoring clinical diagnoses in cattle, poultry and buffalo. Veterinarians submitted surveys using mobile phones and GPS. This surveillance system proved to be both feasible and acceptable and provided timely information on animal health patterns in Sri Lanka. A critical review of software and methods for space-time disease surveillance provides guidance on the selection and implementation of appropriate analytic methods for surveillance data. For the data collected in this research, a hidden Markov model is developed which estimates region-specific prevalence estimates after controlling for sentinel-level factors. 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