demographic estimation
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Author(s):  
Fabiola Becerra-Riera ◽  
Annette Morales-Gonzalez ◽  
Heydi Mendez-Vazquez ◽  
Jean-Luc Dugelay

2020 ◽  
Author(s):  
Elias Oziolor ◽  
Shawn Sullivan ◽  
Hayley Mangelson ◽  
Stephen M. Eacker ◽  
Michael Agostino ◽  
...  

AbstractThe cynomolgus macaque is a non-human primate model, heavily used in biomedical research, but with outdated genomic resources. Here we have used the latest long-read sequencing technologies in order to assemble a fully phased, chromosome-level assembly for the cynomolgus macaque. We have built a hybrid assembly with PacBio, 10x Genomics, and HiC technologies, resulting in a diploid assembly that spans a length of 5.1 Gb with a total of 16,741 contigs (N50 of 0.86Mb) contained in 370 scaffolds (N50 of 138 Mb) positioned on 42 chromosomes (21 homologous pairs). This assembly is highly homologous to former assemblies and identifies novel inversions and provides higher confidence in the genetic architecture of the cynomolgus macaque genome. A demographic estimation is also able to capture the recent genetic bottleneck in the Mauritius population, from which the sequenced individual originates. We offer this resource as an enablement for genetic tools to be built around this important model for biomedical research.


2018 ◽  
Vol 19 (3) ◽  
pp. 225-247 ◽  
Author(s):  
Carl P Schmertmann ◽  
Mathew E Hauer

We investigate a modern statistical approach to a classic deterministic demographic estimation technique. When vital event registration is missing or inadequate, it is possible to approximate a population's total fertility rate (TFR) from information about its distribution by age and sex. For example, if under-five child mortality is low then TFR is often close to seven times the child/woman ratio (CWR), the number of 0–4 year olds per 15–49-year-old woman. We analyse the formal relationship between CWR and TFR to identify sources of uncertainty in indirect estimates. We construct a Bayesian model for the statistical distribution of TFR conditional on the population's age–sex structure, in which unknown demographic quantities in the standard approximation are parameters with prior distributions. We apply the model in two case studies: to a small indigenous population in the Amazon region of Brazil that has extremely high fertility rates, and to the set of 159 counties in the US state of Georgia. A statistical approach yields important insights into the sources of error in indirect estimation, and their relative magnitudes.


2018 ◽  
Author(s):  
Carl Schmertmann ◽  
Marcos Roberto Gonzaga

We develop a Bayesian regression model for small-area mortality schedules that simultaneously addresses the problems of small local samples and underreporting of deaths. We combine a relational model for mortality schedules with probabilistic prior information on death registration coverage – derived from demographic estimation techniques such as Death Distribution Methods, and from field audits done by public health experts. We test the model on small-area data from Brazil. Incorporating external estimates of vital registration coverage though priors improves small-area mortality estimates by accounting for under-registration, and by automatically producing measures of uncertainty.


2017 ◽  
Vol 80 ◽  
pp. 297-310 ◽  
Author(s):  
SE. Bekhouche ◽  
A. Ouafi ◽  
F. Dornaika ◽  
A. Taleb-Ahmed ◽  
A. Hadid

2017 ◽  
Author(s):  
Bernardo L Queiroz ◽  
Everton Lima

In this paper, we analyze the evolution of the completeness of death counts coverage in Brazil and its regions since 1980. We review a series of studies on the quality of mortality registration for the country, states and small areas, compare and contrast different approaches and results. We also investigate the quality of the 2010 Census data regarding the information on household deaths in 2010 to results obtained using the Ministry of Health Mortality Information System. Finally, we produce estimates at the city level and discuss the limitation and importance of producing small areas demographic estimation for public health planning and population forecasts.


2017 ◽  
Author(s):  
Carl Schmertmann ◽  
Mathew Hauer

We investigate a modern statistical approach to a classic deterministic demographic estimation technique. When vital event registration is missing or inadequate, it is possible to approximate a population's total fertility (TFR) from information about its distribution by age and sex. For example, if under-five child mortality is low then TFR is often close to seven times the child/woman ratio (CWR), the number of 0--4 year olds per 15--49 year old woman. We analyze the formal relationship between CWR and TFR to identify sources of uncertainty in indirect estimates. We construct a Bayesian model for the statistical distribution of TFR conditional on the population's age-sex structure, in which unknown demographic quantities in the standard approximation are parameters with prior distributions. We apply the model in two case studies: to a small indigenous population in the Amazon region of Brazil that has extremely high fertility rates, and to the set of 159 counties in the US state of Georgia. A statistical approach yields important insights into the sources of error in indirect estimation, and their relative magnitudes.


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