Public health practitioners rely on timely surveillance data for planning and decision-making; however, surveillance data are often subject to delays. Epidemic trend categories, based on time-varying effective reproductive number (R(t)) estimates that use nowcasting methods, can mitigate reporting lags in surveillance data and detect changes in community transmission before reporting is completed. CDC analyzed the performance of epidemic trend categories for COVID-19 during summer 2024 in the United States and at the state level in New Mexico. COVID-19 epidemic trend categories were estimated and released in real time based on preliminary data, then retrospectively compared with final emergency department (ED) visit data to determine their ability to detect or confirm real-time changes in subsequent ED visits. Across the United States and in New Mexico, epidemic trend categories were an early indicator of increases in COVID-19 community transmission, signifying increases in COVID-19 community transmission in May, and a confirmatory indicator that decreasing COVID-19 ED visits reflected actual decreases in COVID-19 community transmission in September, rather than incomplete reporting. Public health decision-makers can use epidemic trend categories, in combination with other surveillance indicators, to understand whether COVID-19 community transmission and subsequent ED visits are increasing, decreasing, or not changing; this information can guide communications decisions.

Before and during the COVID-19 pandemic, an individual’s age and race/ethnicity have been highly predictive of their risk of infectious diseases and their health consequences. Disparities were evidenced in COVID-19 incidence rates and in hospitalization, severity and mortality metrics in the USA1 and in other countries.2,3 Identifying these disparate outcomes associated with demographic variables is valuable mainly if it prompts investigation into what mechanisms generate the disparities and inform how they can be reduced.4 A prominent report from the UK succinctly outlined that social determinants such as occupation, household characteristics, surrounding population density, urbanicity and social deprivation were all associated with increased risk of COVID-19 infection.3 Others have noted that social determinants can play a role in all stages of an outbreak, providing pathways for unequal exposure, transmission, susceptibility and treatment that produce and escalate disparities in health outcomes.5