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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2021
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2105.05290 |
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| _version_ | 1866929515193696256 |
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| author | Sen, Aditi Lahiri, Partha |
| author_facet | Sen, Aditi Lahiri, Partha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_05290 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Estimation of mask effectiveness perception for small domains using multiple data sources Sen, Aditi Lahiri, Partha Applications 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. |
| title | Estimation of mask effectiveness perception for small domains using multiple data sources |
| topic | Applications |
| url | https://arxiv.org/abs/2105.05290 |