  All pandemics are local; so learning about 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 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 propose a
Jackknife method to estimate variance of our proposed estimator. From our data
analysis. it is evident that our proposed synthetic method outperforms direct
survey-weighted estimator with respect to commonly used evaluation measures.
