Improved log-linear model estimators of abundance in capture-recapture experiments

2001 ◽  
Vol 29 (4) ◽  
pp. 555-572 ◽  
Author(s):  
Louis-Paul Rivest ◽  
Tina Lévesque
Keyword(s):  
Linear Model ◽  
Capture Recapture ◽  
Log Linear

The Importance of Numbers: Using Capture-recapture to make the Homeless Count in Adelaide

2007 ◽  
Vol 13 (1) ◽  
pp. 89 ◽  
Author(s):  
Katina D'Onise ◽  
Yan Wang ◽  
Robyn McDermott
Keyword(s):  
Confidence Interval ◽  
Linear Model ◽  
Service Sector ◽  
Homeless People ◽  
Australian Bureau ◽  
City Council ◽  
Homeless Population ◽  
Capture Recapture ◽  
Australian Bureau Of Statistics ◽  
Log Linear

An important problem for the homeless service sector is understanding the size of homeless populations, which has implications on planning services and social policy. The aim of this study is to apply capture-recapture methods to count the primary homeless population in the Adelaide city council area, to examine the use of an alternative method to the Australian Bureau of Statistics census. Capture-recapture techniques were used to analyse homeless registers from three different services to estimate the number of primary homeless people in the Adelaide city council area from 19 June to 19 September 2005. Log-linear model and the sample coverage method were employed to analyse the data. The log-linear model results gave a population estimate of 455 (95% confidence interval 299, 762), and the sample coverage method of 311 (95% confidence interval 229, 466), compared with 104 from the Australian Bureau of Statistics census. Multiple sources of information utilising different methodologies should be considered together when attempting to plan services for primary homeless people, as all available techniques have important limitations. Capture-recapture is an important method to supplement any attempt at enumeration of hidden, mobile or difficult-to-reach populations.

Capture-recapture study to estimate the true incidence of tuberculosis in Portugal, 2018

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
M Perez Duque ◽  
L Hansen ◽  
D Antunes ◽  
R Sá Machado
Keyword(s):  
Infectious Disease ◽  
Linear Model ◽  
Surveillance System ◽  
Epidemiological Surveillance ◽  
Accurate Estimation ◽  
Surveillance Systems ◽  
True Incidence ◽  
True Number ◽  
Capture Recapture ◽  
Log Linear

Abstract Accurate estimation of the true number of cases of an infectious disease is essential to plan and efficiently allocate available resources. This study aims to improve the Portuguese surveillance system for tuberculosis (TB) by identifying gaps in TB epidemiological surveillance at the national level. We estimated annual TB incidence using a capture-recapture method to assess the sensitivity of national TB surveillance. Using probabilistic record linkage between two data sources, the National Epidemiological Surveillance System (SINAVE) and National Tuberculosis Program Surveillance System (SVIG-TB), we extracted TB diagnosed cases data for calendar year 2018. All reported TB cases were included, classified as confirmed, probable or possible. A two-source capture-recapture analysis using a log-linear model was performed to estimate the number of unobserved TB cases in Portugal and of the proportion identified by the current TB surveillance system. Between the two datasets, we found 896 TB cases (of a total of 2170 cases) that could not be matched (37.5% SINAVE only, 62.5% SVIG only). Based on the log-linear model, it was estimated that there were 148 unobserved TB cases (95% confidence interval 127.96 - 171.31). Therefore, the estimated true number of TB cases in 2018 is 2318, so current surveillance has a sensitivity of 93.6%. Based on these findings, the TB incidence in Portugal is estimated to be 22.55 cases per 100 000 inhabitants. Capture-recapture methods are useful in estimating annual TB incidence in high-resource settings. Although the two TB surveillance systems capture the majority of TB cases in Portugal, we might still be underestimating the true number of TB cases. Because TB is a high impact infectious disease, precise incidence estimates are crucial to allocate treatment and prevention resources and guide health policies. Key messages CRC method showed that Portugal is a TB low incidence country. Epidemiological surveillance systems should have a high sensitivity in order to allocate efficiently resources available.

Prevalence of Multiple Sclerosis in the City of Volta Redonda - Rio De Janeiro, Brazil Using the Capture-Recapture Method

2016 ◽  
Vol 46 (2) ◽  
pp. 88-95 ◽  
Author(s):  
Ana Beatriz Clamon ◽  
Fernanda Pereira ◽  
Benoit Marin ◽  
Pierre-Marie Preux ◽  
Regina Papais Alvarenga
Keyword(s):  
Multiple Sclerosis ◽  
Latin America ◽  
Linear Model ◽  
Prevalence Rate ◽  
True Prevalence ◽  
The World ◽  
Capture Recapture ◽  
Log Linear ◽  
Incomplete Lists ◽  
The City

Background: Multiple sclerosis (MS) prevalence in Latin America was estimated in some regions and it was found to range from 0.75 to 30/100,000. The reasons for variation in rates of prevalence around the world still are not clear, but there are environmental and genetic explanations to this phenomenon. This study aimed at estimating the MS prevalence in Volta Redonda, Brazil. Method: Three sources of cases ascertainment were used and the method of capture-recapture was applied for assessing the corrected prevalence in the city of Volta Redonda in November 2012. The capture-recapture method uses data from incomplete lists and allows calculating the number of unregistered cases. Data were analyzed using a log-linear model. Results: A total of 40 MS cases was found by withdrawing overlaps of sources and it was estimated that a total number of 40 cases (95% CI 13.5-118.8) were not detected by the sources. The corrected prevalence of MS was, then, 30.7/100,000. Conclusion: Our study was the first in Brazil to use the capture-recapture method to assess the prevalence of MS, demonstrating the highest prevalence rate so far. It is necessary to perform other similar studies and in other regions of the country using the same method for a better evaluation of the true prevalence of MS our country.

scholarly journals Capture - recapture based study on the completeness of smear positive pulmonary tuberculosis reporting in southwest Iran during 2016

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Homayoun Amiri ◽  
Mohammad Javad Mohammadi ◽  
Seyed Mohammad Alavi ◽  
Shokrolah Salmanzadeh ◽  
Fatemeh Hematnia ◽  
...  
Keyword(s):  
Pulmonary Tuberculosis ◽  
Linear Model ◽  
Causes Of Death ◽  
Prescription Data ◽  
Rural Regions ◽  
Smear Positive Pulmonary Tuberculosis ◽  
Capture Recapture ◽  
Log Linear ◽  
Southwest Iran ◽  
Completeness Of Reporting

Abstract Background Tuberculosis (TB) is one of the ten leading causes of death in infectious diseases and one of the ten leading causes of death in the world. For any TB control program, a valid surveillance is essential. In order to assess the status of the assessment, the quality of the record and the completeness of reporting should be assessed. The purpose of this study was to investigate the completeness of smear positive pulmonary tuberculosis reporting in Ahvaz, south west of Iran. Methods This cross-sectional study was conducted in 2016 in Ahvaz, southwest Iran. The study was conducted through a three-source Capture recapture method by collecting laboratory, hospital, physician prescription data; including patient referral to the health care center, prescriptions of patients receiving anti-tuberculosis drugs and prescriptions of medical TB diagnostic laboratories, and laboratory prescriptions. Percentage, mean and standard deviation were used to describe the variables. Data analysis was performed using log-linear model in Rcapture package R software. Results Generally, 134 new cases of smear-positive pulmonary tuberculosis patients were reported through three sources from urban and rural regions during 2016. Pulmonary tuberculosis was reported through three sources from urban and rural regions during 2016. The most common age group was 25 to 44 years and 79.1% of the patient were man. The overall prevalence of new cases of smear-positive pulmonary tuberculosis was in persons that lived urban areas (97.8%). The completeness of reporting the disease estimated by log-linear model was 87.5% and the incidence rate was estimated to be 11.8 disease per 100,000 persons. Completeness of reporting of laboratory, hospital and physician resources were 79%, 30% and 16.3%, respectively. Conclusions The present study shows the necessity of evaluating the quality, completeness and linkage between data. Linking between data sources can improve the accuracy and completeness of TB surveillance.

Polytomous IRT models under log-linear model framework

2011 ◽  
Author(s):  
Zhushan Mandy Li
Keyword(s):  
Linear Model ◽  
Model Framework ◽  
Irt Models ◽  
Polytomous Irt Models ◽  
Log Linear

Leveraging Second-Order Log-Linear Model for Improved Deep Learning Based ASR Performance

2018 ◽  
Author(s):  
Ankit Raj ◽  
Shakti P Rath ◽  
Jithendra Vepa
Keyword(s):  
Deep Learning ◽  
Linear Model ◽  
Second Order ◽  
Log Linear

Asymptotic results for a conditional Poisson log-linear model

1989 ◽  
Vol 7 (4) ◽  
pp. 267-270 ◽  
Author(s):  
Douglas G Bonett ◽  
P.M Bentler ◽  
J.Arthur Woodward
Keyword(s):  
Linear Model ◽  
Asymptotic Results ◽  
Log Linear ◽  
Conditional Poisson

scholarly journals A Cascaded Unsupervised Model for PoS Tagging

2021 ◽  
Vol 20 (1) ◽  
pp. 1-23
Author(s):  
Necva Bölücü ◽  
Burcu Can
Keyword(s):  
Linear Model ◽  
Language Processing ◽  
Bayesian Model ◽  
Linear Models ◽  
Syntactic Category ◽  
Semantic Parsing ◽  
Pos Tagging ◽  
Part Of Speech ◽  
Sentence Level ◽  
Log Linear

Part of speech (PoS) tagging is one of the fundamental syntactic tasks in Natural Language Processing, as it assigns a syntactic category to each word within a given sentence or context (such as noun, verb, adjective, etc.). Those syntactic categories could be used to further analyze the sentence-level syntax (e.g., dependency parsing) and thereby extract the meaning of the sentence (e.g., semantic parsing). Various methods have been proposed for learning PoS tags in an unsupervised setting without using any annotated corpora. One of the widely used methods for the tagging problem is log-linear models. Initialization of the parameters in a log-linear model is very crucial for the inference. Different initialization techniques have been used so far. In this work, we present a log-linear model for PoS tagging that uses another fully unsupervised Bayesian model to initialize the parameters of the model in a cascaded framework. Therefore, we transfer some knowledge between two different unsupervised models to leverage the PoS tagging results, where a log-linear model benefits from a Bayesian model’s expertise. We present results for Turkish as a morphologically rich language and for English as a comparably morphologically poor language in a fully unsupervised framework. The results show that our framework outperforms other unsupervised models proposed for PoS tagging.

scholarly journals Application of log-linear model in inference on karyotypic evolution in chronic myelocytic leukemia.

1990 ◽  
Vol 87 ◽  
pp. 135-141 ◽  
Author(s):  
T Hashimoto ◽  
M Ohtaki ◽  
N Kamada ◽  
H Yamamoto ◽  
M Munaka
Keyword(s):  
Linear Model ◽  
Chronic Myelocytic Leukemia ◽  
Karyotypic Evolution ◽  
Myelocytic Leukemia ◽  
Log Linear
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