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A Bayesian approach to improving spatial estimates of prevalence of COVID-19 after accounting for misclassification bias in surveillance data in Philadelphia, PA
Surveillance data obtained by public health agencies for COVID-19 are likely inaccurate due to undercounting and misdiagnosing. Using a Bayesian approach, we sought to reduce bias in the estimates of prevalence of COVID-19 in Philadelphia, PA at the ZIP code level. After evaluating various modeling...
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| Publicado no: | Spat Spatiotemporal Epidemiol |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Elsevier Ltd.
2021
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7833121/ https://ncbi.nlm.nih.gov/pubmed/33509436 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.sste.2021.100401 |
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