Thirty Years of The Network Scale-up Method

Abstract

The Network Scale-up Method (NSUM) uses social networks and answers to "How many X's do you know?" questions to estimate hard-to-reach population sizes. This paper focuses on two biases associated with the NSUM. First, different populations are known to have different average social network sizes, introducing degree ratio bias. This is especially true for marginalized populations like sex workers and drug users, where members tend to have smaller social networks than the average person. Second, large subpopulations are weighted more heavily than small subpopulations in current NSUM estimators, leading to poor size estimates of small subpopulations. We show how the degree ratio affects size estimates, provide a method to estimate degree ratios without collecting additional data, and demonstrate that rescaling size estimates improves the estimates for smaller subpopulations. We demonstrate that our adjustment procedures improve the accuracy of NSUM size estimates using simulations and data from two data sources.

Estimating Active Cases of COVID-19

“…Fatality-based estimation: and CCFR Fatalities. March 13 2020, a news article in Nature [23] brought attention to the high level of underdetection of COVID-19 and suggested the use of estimates of the case fatality rate (CFR) to infer the likely number of cases. Also, in March 2020, the Centre for the Mathematical Modelling of Infectious Diseases at the London School of Hygiene & Tropical Medicine made available a more detailed estimation of the level of under-detection of COVID-19 symptomatic cases [27,28].…”

“…As we mentioned above, if the and series are based on the con rmed data, they can underestimate the true number of cases [23]. The CFR-based techniques that we developed in [24] provide a series that estimates the number of cumulative cases.…”

“…The COVID-19 disease epidemic started having a deep impact in societies across the world as the virus spread globally in early 2020. During its initial months, the knowledge about the virus's properties and about the symptoms of COVID-19, and the ability to test for the disease were limited [23]. These factors made obtaining accurate estimates of cases challenging.…”

Nehezen elérhető csoportok és szegregáció vizsgálata a hálózati méretgenerátor módszerével: egy magyarországi vizsgálat tanulságai

“…A hálózati méretgenerátoros vagy összegző módszer szociológiai alkalmazása alapvetően három különböző kutatási területre terjed ki (Kmetty-Koltai 2015, Laga-Bao-Niu 2021, McCormick 2021: 1) az egóhálózatok méretének becslésére, 2) a nehezen elérhető vagy rejtett csoportok méretének becslésére, valamint 3) a társadalmon belüli szegregációs minták feltárására.…”

“…A méretgenerátoros technika alapfeltevése, hogy egy alpopuláció létszáma úgy aránylik a teljes populáció létszámához, ahogyan az alpopulációból ismert emberek száma a válaszadók teljes kapcsolathálójához (Killworth-McCarty et al 1998, Laga-Bao-Niu 2021. Azaz:…”