Estimation of mask effectiveness perception for small domains using multiple data sources

TL;DR

This paper introduces a synthetic estimation method combining multiple data sources to reliably estimate mask effectiveness perception in small areas during COVID-19, overcoming the unreliability of direct survey estimates.

Contribution

The paper develops a novel logistic model with a new variable selection criterion and a Jackknife variance estimation method for small-area mask perception analysis.

Findings

Synthetic estimates outperform direct survey-weighted estimates.

The proposed method provides more reliable small-area mask perception estimates.

Model selection and variance estimation techniques improve estimation accuracy.

Abstract

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.