ABSTRACT
In this paper, we provide an empirical, descriptive analysis of the social networks of Hungarian society and illustrate how the network scale-up method estimates the size of hard-to-reach subpopulations and segregation of social groups. Based on a representative survey of 7000 respondents from Hungary (HS2021), we first estimate the average size of the respondents' personal networks. Then, we examine the social fault lines along various social groups and how accurately the network scale-up method estimates the size of these groups (e.g., unemployed, protesters, the Roma, Covid-infected). These estimates are then compared with data from other sources (census data, administrative data, surveys). Our results show that the network scale-up method estimates the size of visible social groups (e.g., the Roma, homeless people) quite well. The visibility of other social groups appears to be much lower. Social fault lines are greatest in the case of homeless people, protesters, and members of NGOs. Finally, we describe recent methodological advancements and summarize our suggestions for future research using this method. © 2022, Hungarian Sociological Association. All rights reserved.
ABSTRACT
Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate.