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dc.contributor.authorAjitesh, Srivastava
dc.contributor.authorRamirez, Juan Marcos 
dc.contributor.authorDiaz Aranda, Sergio 
dc.contributor.authorAguilar, Jose 
dc.contributor.authorOrtega, Antonio
dc.contributor.authorFernández Anta, Antonio 
dc.contributor.authorLillo, Rosa Elvira
dc.date.accessioned2023-12-19T17:22:40Z
dc.date.available2023-12-19T17:22:40Z
dc.date.issued2024-03-01
dc.identifier.urihttps://hdl.handle.net/20.500.12761/1769
dc.description.abstractIndirect surveys, in which respondents provide information about other people they know, have been proposed for estimating (nowcasting) the size of a \emph{hidden population} where privacy is important or the hidden population is hard to reach. Examples include estimating casualties in an earthquake, conditions among female sex workers, and the prevalence of drug use and infectious diseases. The Network Scale-up Method (NSUM) is the classical approach to developing estimates from indirect surveys, but it was designed for one-shot surveys. Further, it requires certain assumptions and asking for or estimating the number of individuals in each respondent's network. In recent years, surveys have been increasingly deployed online and can collect data continuously (e.g., COVID-19 surveys on Facebook during much of the pandemic). Conventional NSUM can be applied to these scenarios by analyzing the data independently at each point in time, but this misses the opportunity of leveraging the temporal dimension. We propose to use the responses from indirect surveys collected over time and develop analytical tools (i) to prove that indirect surveys can provide better estimates for the trends of the hidden population over time, as compared to direct surveys and (ii) to identify appropriate temporal aggregations to improve the estimates. We demonstrate through extensive simulations that our approach outperforms traditional NSUM and direct surveying methods. We also empirically demonstrate the superiority of our approach on a real indirect survey dataset of COVID-19 cases.es
dc.description.sponsorshipSpanish State Research Agency - Spanish Ministry of Science and Innovationes
dc.language.isoenges
dc.titleNowcasting Temporal Trends Using Indirect Surveyses
dc.typeconference objectes
dc.conference.date20-27 February 2024es
dc.conference.placeVancouver, Canadaes
dc.conference.titleAnnual AAAI Conference on Artificial Intelligence*
dc.event.typeconferencees
dc.pres.typepaperes
dc.rights.accessRightsopen accesses
dc.relation.projectIDPID2019- 104901RB-I00es
dc.relation.projectNameSocialProbing (Técnicas de análisis y recopilación de datos escalables y asequibles para el sondeo social)es
dc.subject.keywordIndirect surveyses
dc.subject.keywordNowcastinges
dc.subject.keywordNSUMes
dc.description.refereedTRUEes
dc.description.statusinpresses


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