COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation

We provide a methodology by which an epidemiologist may arrive at an optimal design for a survey whose goal is to estimate the disease burden in a population. For serosurveys with a given budget of C rupees, a specified set of tests with costs, sensitivities, and specificities, we show the existence...

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Autores principales: Athreya, Siva, Babu, Giridhara R., Iyer, Aniruddha, S., Mohammed Minhaas B., Rathod, Nihesh, Shriram, Sharad, Sundaresan, Rajesh, Vaidhiyan, Nidhin Koshy, Yasodharan, Sarath
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8524406/
https://www.ncbi.nlm.nih.gov/pubmed/34690461
http://dx.doi.org/10.1007/s13571-021-00267-w
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author Athreya, Siva
Babu, Giridhara R.
Iyer, Aniruddha
S., Mohammed Minhaas B.
Rathod, Nihesh
Shriram, Sharad
Sundaresan, Rajesh
Vaidhiyan, Nidhin Koshy
Yasodharan, Sarath
author_facet Athreya, Siva
Babu, Giridhara R.
Iyer, Aniruddha
S., Mohammed Minhaas B.
Rathod, Nihesh
Shriram, Sharad
Sundaresan, Rajesh
Vaidhiyan, Nidhin Koshy
Yasodharan, Sarath
author_sort Athreya, Siva
collection PubMed
description We provide a methodology by which an epidemiologist may arrive at an optimal design for a survey whose goal is to estimate the disease burden in a population. For serosurveys with a given budget of C rupees, a specified set of tests with costs, sensitivities, and specificities, we show the existence of optimal designs in four different contexts, including the well known c-optimal design. Usefulness of the results are illustrated via numerical examples. Our results are applicable to a wide range of epidemiological surveys under the assumptions that the estimate’s Fisher-information matrix satisfies a uniform positive definite criterion.
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spelling pubmed-85244062021-10-20 COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation Athreya, Siva Babu, Giridhara R. Iyer, Aniruddha S., Mohammed Minhaas B. Rathod, Nihesh Shriram, Sharad Sundaresan, Rajesh Vaidhiyan, Nidhin Koshy Yasodharan, Sarath Sankhya B (2008) Article We provide a methodology by which an epidemiologist may arrive at an optimal design for a survey whose goal is to estimate the disease burden in a population. For serosurveys with a given budget of C rupees, a specified set of tests with costs, sensitivities, and specificities, we show the existence of optimal designs in four different contexts, including the well known c-optimal design. Usefulness of the results are illustrated via numerical examples. Our results are applicable to a wide range of epidemiological surveys under the assumptions that the estimate’s Fisher-information matrix satisfies a uniform positive definite criterion. Springer India 2021-10-19 2022 /pmc/articles/PMC8524406/ /pubmed/34690461 http://dx.doi.org/10.1007/s13571-021-00267-w Text en © Indian Statistical Institute 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Athreya, Siva
Babu, Giridhara R.
Iyer, Aniruddha
S., Mohammed Minhaas B.
Rathod, Nihesh
Shriram, Sharad
Sundaresan, Rajesh
Vaidhiyan, Nidhin Koshy
Yasodharan, Sarath
COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation
title COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation
title_full COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation
title_fullStr COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation
title_full_unstemmed COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation
title_short COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation
title_sort covid-19: optimal design of serosurveys for disease burden estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8524406/
https://www.ncbi.nlm.nih.gov/pubmed/34690461
http://dx.doi.org/10.1007/s13571-021-00267-w
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