Nowcasting Temporal Trends Using Indirect Surveys
Abstract: Indirect surveys, in which respondents provide information about other people they know, have been proposed for estimating (nowcasting) the size of a \emph{hidden population} where privacy is important or the hidden population is hard to reach. Examples include estimating casualties in an earthquake, conditions among female sex workers, and the prevalence of drug use and infectious diseases. The Network Scale-up Method (NSUM) is the classical approach to developing estimates from indirect surveys, but it was designed for one-shot surveys. Further, it requires certain assumptions and asking for or estimating the number of individuals in each respondent's network. In recent years, surveys have been increasingly deployed online and can collect data continuously (e.g., COVID-19 surveys on Facebook during much of the pandemic). Conventional NSUM can be applied to these scenarios by analyzing the data independently at each point in time, but this misses the opportunity of leveraging the temporal dimension. We propose to use the responses from indirect surveys collected over time and develop analytical tools (i) to prove that indirect surveys can provide better estimates for the trends of the hidden population over time, as compared to direct surveys and (ii) to identify appropriate temporal aggregations to improve the estimates. We demonstrate through extensive simulations that our approach outperforms traditional NSUM and direct surveying methods. We also empirically demonstrate the superiority of our approach on a real indirect survey dataset of COVID-19 cases.
- Better to be indirect? Testing the accuracy and cost-savings of indirect surveys. World Development, 142: 105419.
- Astley, C. M.; et al. 2021. Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base. Proceedings of the National Academy of Sciences, 118(51).
- Estimating the Size of an Average Personal Network and of an Event Subpopulation. In The Small World, 159–175.
- A better bound on the variance. The american mathematical monthly, 107(4): 353–357.
- Using aggregated relational data to feasibly identify network structure without network data. American Economic Review, 110(8): 2454–84.
- Dietz, K. 1993. The estimation of the basic reproduction number for infectious diseases. Statistical methods in medical research, 2(1): 23–41.
- An interactive web-based dashboard to track COVID-19 in real time. The Lancet infectious diseases, 20(5): 533–534.
- Dunbar, R. 2010. How many friends does one person need? Dunbar’s number and other evolutionary quirks. Harvard University Press.
- Population size estimation of men who have sex with men through the network scale-up method in Japan. PloS one, 7(1): e31184.
- Generalizing the network scale-up method: a new estimator for the size of hidden populations. Sociological methodology, 46(1): 153–186.
- Garcia-Agundez, A.; et al. 2021. Estimating the COVID-19 prevalence in Spain with indirect reporting via open surveys. Frontiers in Public Health, 9: 658544.
- Geldsetzer, P. 2020. Knowledge and perceptions of COVID-19 among the general public in the United States and the United Kingdom: a cross-sectional online survey. Annals of internal medicine, 173(2): 157–160.
- Combining the randomized response technique and the network scale-up method to estimate the female sex worker population size: an exploratory study. Public health, 160: 81–86.
- The frequency of high-risk behaviors among Iranian college students using indirect methods: network scale-up and crosswise model. International journal of high risk behaviors & addiction, 5(3).
- A social network approach to estimating seroprevalence in the United States. Social networks, 20(1): 23–50.
- Estimation of seroprevalence, rape, and homelessness in the United States using a social network approach. Evaluation review, 22(2): 289–308.
- Thirty years of the network scale-up method. Journal of the American Statistical Association, 116(535): 1548–1559.
- Oliver, N.; et al. 2020. Assessing the impact of the COVID-19 pandemic in Spain: large-scale, online, self-reported population survey. Journal of medical Internet research, 22(9): e21319.
- Rossier, C. 2010. The anonymous third party reporting method. Methodologies for estimating abortion incidence and abortion-related morbidity: a review, 99–106.
- Salganik, M.; et al. 2010. Estimating the number of heavy drug users in Curitiba, Brazil using multiple methods. Technical report, Technical report. UNAIDS.
- Salganik, M. J.; et al. 2011. The game of contacts: estimating the social visibility of groups. Social networks, 33(1): 70–78.
- Salomon, J. A.; et al. 2021. The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination. Proceedings of the National Academy of Sciences, 118(51): e2111454118.
- Scheers, N. 1992. A review of randomized response techniques. Measurement and Evaluation in Counseling and Development.
- Estimating Temporal Trends using Indirect Surveys. arXiv preprint arXiv:2307.06643.
- Teo, A. K. J.; et al. 2019. Estimating the size of key populations for HIV in Singapore using the network scale-up method. Sexually transmitted infections, 95(8): 602–607.
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