| 000 | 02976nam a22003617a 4500 | ||
|---|---|---|---|
| 001 | D0001C000164 | ||
| 003 | DOH | ||
| 005 | 20210326113135.0 | ||
| 008 | 210326b ||||| |||| 00| 0 eng d | ||
| 245 | 0 | 0 |
_aSerological evidence of human infection with SARS-CoV-2 : a systematic review and meta-analysis _cXinhua Chen [and eleven others] |
| 520 | 3 | _aBackground: A rapidly increasing number of serological surveys for anti-SARS-CoV-2 antibodies have been reported worldwide. A synthesis of this large corpus of data is needed. Purpose: To evaluate the quality of serological studies and provide a global picture of seroprevalence across demographic and occupational groups, and to provide guidance for conducting better serosurveys. Data sources: PubMed, Embase, Web of Science, medRxiv, bioRxiv, SSRN and Wellcome were searched for English-language papers published from December 1, 2019 to August 28, 2020. Study selection: Serological studies that evaluated seroprevalence of SARS-CoV-2 infections in humans. Data extraction: Two investigators independently extracted data from included studies. Data synthesis: Most of 178 serological studies, representing tests in >800,000 individuals, identified were of low quality. Close contacts and high-risk healthcare workers had higher seroprevalence of 22.9% (95% CI: 11.1-34.7%) and 14.9% (4.8-25.0%), compared to low-risk healthcare workers and general population of 5.5% (4.6-6.4%) and 6.3% (5.5-7.1%). Generally, young people (0-20 yrs) were less likely to be seropositive compared to the middle-aged (21-55 yrs) populations (RR, 0.8, 95% CI: 0.7-0.8). Seroprevalence correlated with clinical COVID-19 reports with 10 (range: 2 to 34) infections per confirmed COVID-19 case. Limitations: Some heterogeneity cannot be well explained quantitatively. Conclusions: The overall quality of seroprevalence studies examined was low. The relatively low seroprevalence among general populations suggest that in most settings, antibody-mediated herd immunity is far from being reached. Given that ratio of infections to confirmed cases is on the same order of magnitude across different locales, reported case numbers may help provide insights into the proportion of the population infected. | |
| 580 | _amedRxiv 2020 Sep 13;2020.09.11.20192773. doi: 10.1101/2020.09.11.20192773 | ||
| 650 | 2 | _aCOVID-19 | |
| 650 | 2 | _aSerologic Tests | |
| 650 | 2 | _aCoronavirus Infections | |
| 650 | 2 | _aSystematic Review | |
| 650 | 2 | _aSARS-CoV-2 | |
| 650 | 2 | _aCorona Virus | |
| 700 | 1 | _aChen, Xinhua | |
| 700 | 1 | _aChen, Zhiyuan | |
| 700 | 1 | _aAzman, Andrew S | |
| 700 | 1 | _aDeng, Xiaowei | |
| 700 | 1 | _aChen, Xinghui | |
| 700 | 1 | _aLu, Wanying | |
| 700 | 1 | _aZhao, Zeyao | |
| 700 | 1 | _aYang, Juan | |
| 700 | 1 | _aViboud, Cecile | |
| 700 | 1 | _aAjelli, Marco | |
| 700 | 1 | _aLeung, Daniel T | |
| 700 | 1 | _aYu, Hongjie | |
| 856 | _uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7491537/ | ||
| 942 | _cCV | ||
| 999 |
_c3789 _d3789 |
||