Skip to main content


Delamater, Paul L. & Woodul, Rachel L. (Preprint). NC-COVID: A Time-Varying Compartmental Model for Estimating SARS-CoV-2 Infection Dynamics in North Carolina, US. medRxiv. PMCID: PMC9628207


Efforts to track and model SARS-CoV-2 infection dynamics in the population have been complicated by certain aspects of the transmission characteristics, which include a pre-symptomatic infectious phase as well as asymptomatic infectious individuals. Another problem is that many models focus on case count, as there has been (and is) limited data regarding infection status of members of the population, which is the most important aspect for constructing transmission models. This paper describes and explains the parameterization, calibration, and revision of the NC-COVID model, a compartmental model to estimate SARS-CoV-2 infection dynamics for the state of North Carolina, US. The model was developed early in the pandemic to provide rapid, up-to-date state-level estimates of the number of people who were currently infected, were immune from a prior infection, and remained susceptible to infection. As a post modeling exercise, we assessed the veracity of the model by comparing its output to SARS-CoV-2 viral particle concentrations detected in wastewater data and to estimates of people infected using COVID-19 deaths. The NC-COVID model was highly correlated with these independently derived estimates, suggesting that it produced accurate estimates of SARS-CoV-2 infection dynamics in North Carolina.


Reference Type

Journal Article

Year Published


Journal Title



Delamater, Paul L.
Woodul, Rachel L.

Article Type





United States of America


North Carolina


Delamater - 0000-0003-3627-9739