{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/68377"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/68377","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Modeling and study of infectious disease : stochastic modeling for antibiotic resistance and treatment strategies","abstract":"Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates.Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates.","abstract_html":"Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates.Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates.","abstract_has_math":false,"creators":["Lo, Monique (Monique Chun-Ying), 1978-"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Urban Studies and Planning.","school":null,"contributors":[],"advisors":["Eric Klopfer."],"committee_chairs":[],"committee_members":[],"year":2001,"date_issued":"2001","date_published":"2001","updated_at":"2026-07-22T22:22:30Z","subjects":["Urban Studies and Planning."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/68377","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Eric Klopfer."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Urban Studies and Planning."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. 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They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/68377"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2001.","Includes bibliographical references (leaf 46)."]},{"key":"dc:description.abstract","label":"Abstract","values":["Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates.Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.C.P."]},{"key":"dc:title","label":"Title","values":["Modeling and study of infectious disease : stochastic modeling for antibiotic resistance and treatment strategies"]}]}],"canonical_facts":{"dc:contributor.advisor":["Eric Klopfer."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Urban Studies and Planning."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Urban Studies and Planning."],"dc:creator":["Lo, Monique (Monique Chun-Ying), 1978-"],"dc:date.accessioned":["2012-01-12T19:22:32Z"],"dc:date.available":["2012-01-12T19:22:32Z"],"dc:date.issued":["2001"],"dc:description":["Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2001.","Includes bibliographical references (leaf 46)."],"dc:description.abstract":["Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates.Antibiotic-resistant bacteria pose a serious threat to immuno-compromised individuals in Intensive Care Units (ICU). This study examines several cycling treatments (7,14,30,60,120,240-day cycle) and random fraction treatment (50-50,60-40,80-20,100-0) strategies in ICU and finds that no single strategy will outperform all others. Human, hospital and pathogen conditions such as admission/departure rate, transmission rate, drug application rate, and incoming patients' characteristics influence the selection of the optimal treatment strategy. Random fraction treatment is generally favored when admission/departure rate is large. Cycling treatment is generally favored when admission/departure rate is small. When transmission rates are high, longer cycle period are preferred. When transmission rates are low, random fraction treatments are preferred. For cycling treatments, longer cycle periods is associated with lower drug application rates whereas shorter cycle periods are associated with larger drug application rates."],"dc:description.degree":["M.C.P."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/68377"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Urban Studies and Planning."],"dc:title":["Modeling and study of infectious disease : stochastic modeling for antibiotic resistance and treatment strategies"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:22:30Z"}