Estimating COVID-19 Epidemiological Dynamics Using Serological Case Data in Maryland

In the early stages of the COVID-19 pandemic, uncertainty around the extent of SARS-CoV-2 spread hampered policymakers’ understanding of the epidemic’s extent. Mathematical models, which proved vital for aiding decision-making, relied primarily on reported cases that were unreliable due to significant underdetection and underreporting. While serological data was used to improve understanding of the epidemiology, it can be costly and difficult to implement without bias. To counter these issues, we integrated serological data from 7229 remnant serum samples collected in 15 Maryland emergency departments (EDs) in Maryland between August and December 2020 into a Bayesian modeling approach to derive an estimate of the incidence of infection and the case fatality rate during the pandemic’s initial wave. We estimated that 5.2% (95% CI, 3.7–7.2%) of the population of Maryland had been infected by late fall 2020. The inferred reporting rate that was estimated started low (<10% in March 2020) and increased to 32% (95% HDI = 26–41%) by the fall, while the estimated infection fatality rate was likely initially higher but fell to 0.51% (95% HDI = 0.43–0.68%) after 1 September 2020. These results demonstrate how existing ED infrastructure can be leveraged to generate less biased, more accurate estimates of the true prevalence of a disease, improving the ability to make decisions and allocate resources under uncertainty.