Emma K. Accorsi|Xueting Qiu|Éva Rumpler|Lee Kennedy?Shaffer|Rebecca Kahn|Keya Joshi|Édward Goldstein|Mats Julius Stensrud|René Niehus|Müge Çevik|Marc Lipsitch
How to detect and reduce potential sources of biases in studies of SARS-CoV-2 and COVID-19
Center for Disease Dynamics, Economics & Policy|Center for Disease Dynamics, Economics & Policy|Center for Disease Dynamics, Economics & Policy|Center for Disease Dynamics, Economics & Policy|Center for Disease Dynamics, Economics & Policy|Center for Disease Dynamics, Economics & Policy|Center for Disease Dynamics, Economics & Policy|École Polytechnique Fédérale de Lausanne|Center for Disease Dynamics, Economics & Policy|University of St Andrews|Harvard University
response coronavirus disease covid19 pandemic public health scientists produced large rapidly expanding body literature aims answer critical questions proportion population geographic area infected transmissibility virus factors associated high infectiousness susceptibility infection groups risk infection morbidity mortality degree antibodies confer protection reinfection observational studies subject number different biases including confounding selection bias measurement error may threaten validity influence interpretation results assist critical evaluation vast body literature contribute future study design outline propose solutions biases occur across different categories observational studies covid19 consider potential biases could occur five categories studies 1 crosssectional seroprevalence 2 longitudinal seroprotection 3 risk factor studies inform interventions 4 studies estimate secondary attack rate 5 studies use secondary attack rates make inferences infectiousness susceptibility