Result 1 - 4 of total 4
Annals of Epidemiology
| Keywords | covid19 |
|---|---|
| Publisher | WHO |
| Document type | Article |
| Note | WHO #Covidence: #813464 |
| Database | COVID19 |
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Annals of Epidemiology ; ISSN 1047-2797
2020
| Keywords | Epidemiology ; covid19 |
|---|---|
| Language | English |
| Publisher | Elsevier BV |
| Publishing country | us |
| Document type | Article ; Online |
| DOI | 10.1016/j.annepidem.2020.08.050 |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.
Spatial and spatio-temporal epidemiology
2021 Volume 36, Page(s) 100401
Abstract: Surveillance data obtained by public health agencies for COVID-19 are likely inaccurate due ... of prevalence of COVID-19 in Philadelphia, PA at the ZIP code level. After evaluating various modeling ... sensitivity and specificity from the models were similar, about 60% and more than 99%, respectively. Surveillance of COVID-19 ...
| Abstract | 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 approaches in a simulation study, we estimated true prevalence by ZIP code with and without conditioning on an area deprivation index (ADI). As of June 10, 2020, in Philadelphia, the observed citywide period prevalence was 1.5%. After accounting for bias in the surveillance data, the median posterior citywide true prevalence was 2.3% when accounting for ADI and 2.1% when not. Overall the median posterior surveillance sensitivity and specificity from the models were similar, about 60% and more than 99%, respectively. Surveillance of COVID-19 in Philadelphia tends to understate discrepancies in burden for the more affected areas, potentially misinforming mitigation priorities. |
|---|---|
| MeSH term(s) | Bayes Theorem ; Bias ; COVID-19/epidemiology ; Humans ; Philadelphia/epidemiology ; Population Surveillance ; Prevalence ; SARS-CoV-2 ; Sensitivity and Specificity ; Spatial Analysis |
| Language | English |
| Publishing date | 2021-01-08 |
| Publishing country | Netherlands |
| Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2515896-X |
| ISSN | 1877-5853 ; 1877-5845 |
| ISSN (online) | 1877-5853 |
| ISSN | 1877-5845 |
| DOI | 10.1016/j.sste.2021.100401 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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2020
Abstract: ... a Bayesian approach, we adjusted for misclassification to improve spatial estimation of COVID-19 in Philadelphia, PA ... Surveillance data obtained by public health agencies for COVID-19 are likely inaccurate. Using ... prevalence of 1.5%. After accounting for bias in the surveillance data, the posterior citywide true ...
| Abstract | Surveillance data obtained by public health agencies for COVID-19 are likely inaccurate. Using a Bayesian approach, we adjusted for misclassification to improve spatial estimation of COVID-19 in Philadelphia, PA at the ZIP code level. We modeled true prevalence as a function of area deprivation index and spatial random effects at the ZIP code level. There were 111,497 documented tests as of June 10, 2020 in Philadelphia, among whom 23,941 (21%) were classified by the laboratory as positive for SARS-CoV-2, translating to an observed prevalence of 1.5%. After accounting for bias in the surveillance data, the posterior citywide true prevalence was 2.8% (95% credible interval: 2.7%, 3.0%). Overall the posterior surveillance sensitivity and specificity were 58.9% (95% credible interval: 41.5%, 75.9%) and 99.8% (95% credible interval: 99.7%, 100%), respectively. Underreporting tends to understate discrepancies in burden for more affected areas, potentially leading to bias in setting priorities. |
|---|---|
| Keywords | SARS-CoV-2 ; COVID-19 ; surveillance ; misclassification ; Bayesian analysis ; covid19 |
| Subject code | 310 |
| Publishing date | 2020-07-08 |
| Publishing country | eu |
| Document type | Book ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.