PMC:7738161 / 16261-16789 JSONTXT

{"project":"LitCovid-sentences","denotations":[{"id":"T118","span":{"begin":0,"end":180},"obj":"Sentence"},{"id":"T119","span":{"begin":181,"end":315},"obj":"Sentence"},{"id":"T120","span":{"begin":316,"end":380},"obj":"Sentence"},{"id":"T121","span":{"begin":381,"end":528},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"We used a Bayesian statistical model to condition our inference about R0b, f2, and expected case counts on the number of reported cases, where R0b is the basic reproductive number. We related the expected number of cases to the observations through a negative binomial observation model with dispersion parameter ϕ. We placed weakly informative priors on R0b, f2, and ϕ (S1 Text). Outside of BC, we estimated the start and end times of the ramp-up of social distancing using priors informed by policy and transit data (S1 Text)."}