Estimation of mask effectiveness perception for small domains using multiple data sources
Statistics in Transition New Series
; 23(1):1-20, 2022.
Article
in English
| Scopus | ID: covidwho-2099039
ABSTRACT
Understanding the impacts of pandemics on public health and related societal issues at granular levels is of great interest. COVID-19 is affecting everyone in the globe and mask wearing is one of the few precautions against it. To quantify people’s perception of mask effectiveness and to prevent the spread of COVID-19 for small areas, we use Understanding America Study’s (UAS) survey data on COVID-19 as our primary data source. Our data analysis shows that direct survey-weighted estimates for small areas could be highly unreliable. In this paper, we develop a synthetic estimation method to estimate proportions of perceived mask effectiveness for small areas using a logistic model that combines information from multiple data sources. We select our working model using an extensive data analysis facilitated by a new variable selection criterion for survey data and benchmarking ratios. We suggest a jackknife method to estimate the variance of our estimator. From our data analysis, it is evident that our proposed synthetic method outperforms the direct survey-weighted estimator with respect to commonly used evaluation measures. © Aditi Sen, Partha Lahiri.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
Statistics in Transition New Series
Year:
2022
Document Type:
Article
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