scholarly journals Population Size Estimation using Multiple Respondent-Driven Sampling Surveys

2019 ◽  
Author(s):  
Brian J Kim ◽  
Mark S Handcock
Keyword(s):  
Population Size ◽  
Population Size Estimation ◽  
Respondent Driven Sampling ◽  
Size Estimation ◽  
Successive Sampling ◽  
Sampling Process ◽  
National Hiv Behavioral Surveillance ◽  
Statistical Validity ◽  
Capture Recapture ◽  
Hard To Reach Populations

Abstract Respondent-driven sampling (RDS) is commonly used to study hard-to-reach populations since traditional methods are unable to efficiently survey members due to the typically highly stigmatized nature of the population. The number of people in these populations is of primary global health and demographic interest and is usually hard to estimate. However, due to the nature of RDS, current methods of population size estimation are insufficient. We introduce a new method of estimating population size that uses concepts from capture-recapture methods while modeling RDS as a successive sampling process. We assess its statistical validity using information from the CDC’s National HIV Behavioral Surveillance system in 2009 and 2012.

scholarly journals Population Size Estimation and Linkage Errors: the Multiple Lists Case

2018 ◽  
Vol 34 (4) ◽  
pp. 889-908 ◽  
Author(s):  
Loredana Di Consiglio ◽  
Tiziana Tuoto
Keyword(s):  
Data Integration ◽  
Population Size ◽  
Population Size Estimation ◽  
Population Estimates ◽  
Size Estimation ◽  
Multiple Sources ◽  
Official Statistics ◽  
Linkage Error ◽  
Capture Recapture ◽  
Error Probabilities

Abstract Data integration is now common practice in official statistics and involves an increasing number of sources. When using multiple sources, an objective is to assess the unknown size of the population. To this aim, capture-recapture methods are applied. Standard capture-recapture methods are based on a number of strong assumptions, including the absence of errors in the integration procedures. However, in particular when the integrated sources were not originally collected for statistical purposes, this assumption is unlikely and linkage errors (false links and missing links) may occur. In this article, the problem of adjusting population estimates in the presence of linkage errors in multiple lists is tackled; under homogeneous linkage error probabilities assumption, a solution is proposed in a realistic and practical scenario of multiple lists linkage procedure.

scholarly journals Using Unmanned Aerial Vehicles (UAV) for free-roaming dog population size estimation

2019 ◽  
Author(s):  
Charlotte Warembourg ◽  
Monica Berger-González ◽  
Danilo Alvarez ◽  
Filipe Maximiano Sousa ◽  
Alexis López Hernández ◽  
...  
Keyword(s):  
Statistical Model ◽  
Unmanned Aerial Vehicles ◽  
Population Size ◽  
Census Data ◽  
Population Size Estimation ◽  
Domestic Dog ◽  
Estimation Methods ◽  
Size Estimation ◽  
Aerial Vehicles ◽  
Capture Recapture

AbstractPopulation size estimation is performed for several reasons including disease surveillance and control, for example to design adequate control strategies such as vaccination programs or to estimate a vaccination campaign coverage. In this study, we aimed at assessing the benefits and challenges of using Unmanned Aerial Vehicles (UAV) to estimate the size of free-roaming domestic dog (FRDD) populations and compare the results with two regularly used methods for population estimations: a Bayesian statistical model based on capture-recapture data and the human:dog ratio estimation. Three studies sites of one square kilometer were selected in Petén department, Guatemala. UAV flight were conducted twice during two consecutive days per study site. The UAV’s camera was set to regularly take pictures and cover the entire surface of the selected areas. A door-to-door survey was conducted in the same areas, all available dogs were marked with a collar and owner were interviewed. Simultaneously to the UAV’s flight, transect walks were performed and the number of collared and non-collared dogs were recorded. Data collected during the interviews and the number of dogs counted during the transect walks informed a Bayesian statistical model. The number of dogs counted on the UAV’s pictures and the estimates given by the Bayesian statistical model, as well as the estimates derived from using a 5:1 human:dog ratio were compared to dog census data. FRDD could be detected using the UAV’s method. However, the method lacked of sensitivity, which could be overcome by choosing the flight timing and the study area wisely, or using infrared camera or automatic detection of the dogs. We also suggest to combine UAV and capture-recapture methods to obtain reliable FRDD population size estimated. This publication may provide helpful directions to design dog population size estimation methods using UAV.

scholarly journals Population Size Estimation of Venue-Based Female Sex Workers in Ho Chi Minh City, Vietnam: Capture-Recapture Exercise

10.2196/10906 ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. e10906 ◽  
Author(s):  
Giang Le ◽  
Nghia Khuu ◽  
Van Thi Thu Tieu ◽  
Phuc Duy Nguyen ◽  
Hoa Thi Yen Luong ◽  
...  
Keyword(s):  
Population Size ◽  
Sex Workers ◽  
Female Sex Workers ◽  
Population Size Estimation ◽  
Ho Chi Minh City ◽  
Size Estimation ◽  
Female Sex ◽  
Capture Recapture

shinyrecap: A Shiny Application for Population Size Estimation from Capture-Recapture Data (Preprint)

2021 ◽  
Author(s):  
Anne F. McIntyre ◽  
Ian E. Fellows ◽  
Steve Gutreuter ◽  
Wolfgang Hladik
Keyword(s):  
Population Size ◽  
Sex Workers ◽  
Female Sex Workers ◽  
Population Size Estimation ◽  
Sub Saharan Africa ◽  
Size Estimation ◽  
Female Sex ◽  
Populations At Risk ◽  
Population Size Estimates ◽  
Capture Recapture

BACKGROUND Capture-recapture is often used to estimate the size of populations at risk for HIV, including female sex workers, men who have sex with men, and people who inject drugs. These population size estimates are critical in determining resource allocation for HIV services geared toward these communities. OBJECTIVE Compared to the commonly used two-source capture-recapture, capture-recapture relying on three (or more) samples can provide more robust PSE but involve far more complex statistical analysis. shinyrecap is designed to provide a user-friendly interface for the field epidemiologist. METHODS shinyrecap is built on the Shiny web application framework for R. This allows it to seamlessly integrate with the sophisticated CRC statistical packages. Additionally, the application may be accessed online or run locally on the user’s machine. RESULTS The application enables users to engage in sample size calculation based on a simulation framework. It assists in the proper formatting of collected data by providing a tool to convert commonly used formats to that used by analysis software. A wide variety of methodologies are supported by the analysis tool, including log-linear, Bayesian model averaging, and Bayesian latent class models. For each methodology, diagnostics and model checking interfaces are provided. CONCLUSIONS Through a use case, we demonstrate the broad utility of this powerful tool with three-source capture-recapture data to produce population size estimation for female sex workers in a subnational unit of a country in sub-Saharan Africa.

scholarly journals Population Size Estimations Among Hidden Populations Using Respondent-Driven Sampling Surveys: Case Studies From Armenia (Preprint)

2018 ◽  
Author(s):  
Katherine R McLaughlin ◽  
Lisa G Johnston ◽  
Laura J Gamble ◽  
Trdat Grigoryan ◽  
Arshak Papoyan ◽  
...  
Keyword(s):  
Population Size ◽  
Measurement Errors ◽  
Network Size ◽  
Respondent Driven Sampling ◽  
Size Estimation ◽  
Hidden Populations ◽  
Care Needs ◽  
Personal Network ◽  
Successive Sampling ◽  
Population Size Estimates

BACKGROUND Estimates of the sizes of hidden populations, including female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID), are essential for understanding the magnitude of vulnerabilities, health care needs, risk behaviors, and HIV and other infections. OBJECTIVE This article advances the successive sampling-population size estimation (SS-PSE) method by examining the performance of a modification allowing visibility to be jointly modeled with population size in the context of 15 datasets. Datasets are from respondent-driven sampling (RDS) surveys of FSW, MSM, and PWID from three cities in Armenia. We compare and evaluate the accuracy of our imputed visibility population size estimates to those found for the same populations through other unpublished methods. We then suggest questions that are useful for eliciting information needed to compute SS-PSE and provide guidelines and caveats to improve the implementation of SS-PSE for real data. METHODS SS-PSE approximates the RDS sampling mechanism via the successive sampling model and uses the order of selection of the sample to provide information on the distribution of network sizes over the population members. We incorporate visibility imputation, a measure of a person’s propensity to participate in the study, given that inclusion probabilities for RDS are unknown and social network sizes, often used as a proxy for inclusion probability, are subject to measurement errors from self-reported study data. RESULTS FSW in Yerevan (2012, 2016) and Vanadzor (2016) as well as PWID in Yerevan (2014), Gyumri (2016), and Vanadzor (2016) had great fits with prior estimations. The MSM populations in all three cities had inconsistencies with expert prior values. The maximum low prior value was larger than the minimum high prior value, making a great fit impossible. One possible explanation is the inclusion of transgender individuals in the MSM populations during these studies. There could be differences between what experts perceive as the size of the population, based on who is an eligible member of that population, and what members of the population perceive. There could also be inconsistencies among different study participants, as some may include transgender individuals in their accounting of personal network size, while others may not. Because of these difficulties, the transgender population was split apart from the MSM population for the 2018 study. CONCLUSIONS Prior estimations from expert opinions may not always be accurate. RDS surveys should be assessed to ensure that they have met all of the assumptions, that variables have reached convergence, and that the network structure of the population does not have bottlenecks. We recommend that SS-PSE be used in conjunction with other population size estimations commonly used in RDS, as well as results of other years of SS-PSE, to ensure generation of the most accurate size estimation.

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