{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115181"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115181","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Estimation of hidden carriers of infectious diseases","abstract":"We consider the general problem of estimating missing information in a given dataset. We focus specifically on the problem of estimating the asymptomatic segment of the population that is COVID-19 infected, given datasets for which subjects have self-selected to be tested, that is, the data do not comprise a random sample. We present several methods to estimate the number of persons infected with COVID-19 that are not captured by traditional methods. We first present a simple comparison of incidence numbers between datasets with varying levels of completion, approximating different degrees of random sampling. We then use the Chao estimator to obtain a ratio of total cases to observed cases. Finally, we employ several other methods to compare against those results, such as a second order jackknife, a SAIRS epidemic model, and an incidence rate.","abstract_html":"We consider the general problem of estimating missing information in a given dataset. We focus specifically on the problem of estimating the asymptomatic segment of the population that is COVID-19 infected, given datasets for which subjects have self-selected to be tested, that is, the data do not comprise a random sample. We present several methods to estimate the number of persons infected with COVID-19 that are not captured by traditional methods. We first present a simple comparison of incidence numbers between datasets with varying levels of completion, approximating different degrees of random sampling. We then use the Chao estimator to obtain a ratio of total cases to observed cases. Finally, we employ several other methods to compare against those results, such as a second order jackknife, a SAIRS epidemic model, and an incidence rate.","abstract_has_math":false,"creators":["Hoff, Vincent"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Industrial Engineering","degree_department":null,"school":null,"contributors":["Beck, Carolyn L."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["COVID-19","pandemic","infectious diseases","chao estimator","bootstrap","jackknife","SAIRS"],"languages":["eng"],"rights":["Copyright 2022 Vincent Hoff"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115181","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Beck, Carolyn L."]},{"key":"dc:creator","label":"Author","values":["Hoff, Vincent"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-27","2022-10-31T12:51:17-05:00"]},{"key":"dc:type","label":"Dc Type","values":["dissertation/thesis","text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["COVID-19","pandemic","infectious diseases","chao estimator","bootstrap","jackknife","SAIRS"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Vincent Hoff"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115181"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["We consider the general problem of estimating missing information in a given dataset. We focus specifically on the problem of estimating the asymptomatic segment of the population that is COVID-19 infected, given datasets for which subjects have self-selected to be tested, that is, the data do not comprise a random sample. We present several methods to estimate the number of persons infected with COVID-19 that are not captured by traditional methods. We first present a simple comparison of incidence numbers between datasets with varying levels of completion, approximating different degrees of random sampling. We then use the Chao estimator to obtain a ratio of total cases to observed cases. Finally, we employ several other methods to compare against those results, such as a second order jackknife, a SAIRS epidemic model, and an incidence rate."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Estimation of hidden carriers of infectious diseases"]}]}],"canonical_facts":{"dc:contributor":["Beck, Carolyn L."],"dc:creator":["Hoff, Vincent"],"dc:date":["2022-05","2022-04-27","2022-10-31T12:51:17-05:00"],"dc:description":["We consider the general problem of estimating missing information in a given dataset. We focus specifically on the problem of estimating the asymptomatic segment of the population that is COVID-19 infected, given datasets for which subjects have self-selected to be tested, that is, the data do not comprise a random sample. We present several methods to estimate the number of persons infected with COVID-19 that are not captured by traditional methods. We first present a simple comparison of incidence numbers between datasets with varying levels of completion, approximating different degrees of random sampling. We then use the Chao estimator to obtain a ratio of total cases to observed cases. Finally, we employ several other methods to compare against those results, such as a second order jackknife, a SAIRS epidemic model, and an incidence rate."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115181"],"dc:language":["eng"],"dc:rights":["Copyright 2022 Vincent Hoff"],"dc:subject":["COVID-19","pandemic","infectious diseases","chao estimator","bootstrap","jackknife","SAIRS"],"dc:title":["Estimation of hidden carriers of infectious diseases"],"dc:type":["dissertation/thesis","text"],"thesis:degree_discipline":["Industrial Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}