Bayesian modelling for COVID-19 seroprevalence studies

Using test positivity and reported case rates to estimate state-level COVID-19 prevalence and seroprevalence in the United States

Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework

SARS-CoV-2 seroprevalence, cumulative infections, and immunity to symptomatic infection – A multistage national household survey and modelling study, Dominican Republic, June–October 2021 - The Lancet Regional Health – Americas

Using outbreak data to estimate the dynamic COVID-19 landscape in Eastern Africa, BMC Infectious Diseases

Pathogens, Free Full-Text

Bayesian network-based spatial predictive modelling reveals COVID-19 transmission dynamics in Eswatini

Estimating SARS-CoV-2 Seroprevalence in Canadian Blood Donors, April 2020 to March 2021: Improving Accuracy with Multiple Assays

Full article: Bayesian network analysis of Covid-19 data reveals higher infection prevalence rates and lower fatality rates than widely reported

Bayesian modelling for COVID-19 seroprevalence studies

Screening of COVID-19 cases through a Bayesian network symptoms model and psychophysical olfactory test - ScienceDirect

Inferring the COVID-19 infection fatality rate in the community-dwelling population: a simple Bayesian evidence synthesis of seroprevalence study data and imprecise mortality data, Epidemiology & Infection

Serological evidence of human infection with SARS-CoV-2: a systematic review and meta-analysis - The Lancet Global Health

Prioritising COVID-19 vaccination in changing social and epidemiological landscapes: a mathematical modelling study - The Lancet Infectious Diseases

Seroprevalence of SARS-CoV-2 Antibodies in Children and Adults in St. Louis, Missouri, USA