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Methods in epidemiologypopulation si...

Rutherford, George.

Methods in epidemiologypopulation size estimation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Methods in epidemiologyedited by George Rutherford.
其他題名:	population size estimation /
其他作者:	Rutherford, George.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	vii, 72 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	PopulationMathematical models.
電子資源:	https://doi.org/10.1007/978-3-030-75464-8
ISBN:	9783030754648$q(electronic bk.)

Methods in epidemiologypopulation size estimation /
Methods in epidemiologypopulation size estimation /[electronic resource] :edited by George Rutherford. - Cham :Springer International Publishing :2021. - vii, 72 p. :ill. (some col.), digital ;24 cm. - Advances in experimental medicine and biology,v.13330065-2598 ;. - Advances in experimental medicine and biology ;946..

Review of size estimation methods -- Methods to estimate the average social network size -- Estimating the size of hidden groups -- Data smoothing, extrapolation, and triangulation.

This book describes the variety of direct and indirect population size estimation (PSE) methods available along with their strengths and weaknesses. Direct estimation methods, such as enumeration and mapping, involve contact with members of hard-to-reach groups. Indirect methods have practical appeal because they require no contact with members of hard-to-reach groups. One indirect method in particular, network scale-up (NSU), has several strengths over other PSE methods: It can be applied at a province/country level, it can estimate size of several hard-to-reach population in a single study, and it is implemented with members of the general population rather than members of hard-to-reach groups. The book discusses methods to collect, analyze, and adjust results and presents methods to triangulate and finalize PSEs.

ISBN: 9783030754648$q(electronic bk.)

Standard No.: 10.1007/978-3-030-75464-8doiSubjects--Topical Terms:

229216
Population
--Mathematical models.

LC Class. No.: HB849.51 / .M48 2021

Dewey Class. No.: 304.6015118

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