Surveillance data captured during the COVID-19 pandemic may not be optimal to inform a public health response, because it is biased by imperfect test accuracy, differential access to testing, and uncertainty in date of infection.
We downloaded COVID-19 time-series surveillance data from the Colorado Department of Public Health & Environment by report and illness onset dates for 9 March 2020 to 30 September 2020. We used existing Bayesian methods to first adjust for misclassification in testing and surveillance, followed by deconvolution of date of infection. We propagated forward uncertainty from each step corresponding to 10,000 posterior time-series of doubly adjusted epidemic curves. The effective reproduction number (Rt), a parameter of principal interest in tracking the pandemic, gauged the impact of the adjustment on inference.
Observed period prevalence was 1.3%; median of the posterior of true (adjusted) prevalence was 1.7% (95% credible interval [CrI]: 1.4%, 1.8%). Sensitivity of surveillance declined over the course of the epidemic from a median of 88.8% (95% CrI: 86.3%, 89.8%) to a median of 60.8% (95% CrI: 60.1%, 62.6%). The mean (minimum, maximum) values of Rt were higher and more variable by report date, 1.12 (0.77, 4.13), compared to those following adjustment, 1.05 (0.89, 1.73). The epidemic curve by report date tended to overestimate Rt early on and be more susceptible to fluctuations in data.
Adjusting for epidemic curves based on surveillance data is necessary if estimates of missed cases and the effective reproduction number play a role in management of the COVID-19 pandemic.