Person:

Goldstein, Edward

Objective: To estimate temporary changes in the incidence of SARS-CoV-2-confirmed hospitalizations (by date of symptom onset) by age group during and after the national lockdown.

Materials and methods: For each age group g, we computed the proportion E(g) of individuals in that age group among all cases aged 10-59y during the early lock-down period (April 20-May 3, 2020), and the corresponding proportion L(g) during the late lockdown (May 18-31, 2020) and post-lockdown (June 15-28, 2020) periods and computed the prevalence ratio: PR(g)=L(g)/E(g).

Results: For the late lockdown and post-lockdown periods, the highest PR values were found in age groups 15-19y (late: PR=1.69, 95%CI 1.05,2.72; post-lockdown: PR=2.05, 1.30,3.24) and 20-24y (late: PR=1.43, 1.10,1.86; post-lockdown: PR=1.49, 1.15,1.93). These estimates were higher in individuals 15-24y compared to those ≥30y.

Conclusions: Adolescents and younger adults had an increased relative incidence of SARS-CoV-2 during late lockdown and post-lockdown periods. The role of these age groups should be considered when implementing future pandemic response efforts.

There is an urgent need to project how transmission of the novel betacoronavirus SARS-CoV-2 will unfold in coming years. These dynamics will depend on seasonality, the duration of immunity, and the strength of cross-immunity to/from the other human coronaviruses. Using data from the United States, we measured how these factors affect transmission of human betacoronaviruses HCoV-OC43 and HCoV-HKU1. We then built a mathematical model to simulate transmission of SARS-CoV-2 through the year 2025. We project that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after an initial pandemic wave. We summarize the full range of plausible transmission scenarios and identify key data still needed to distinguish between them, most importantly longitudinal serological studies to determine the duration of immunity to SARS-CoV-2.

Using data on coronavirus disease (COVID-19) cases in Germany from the Robert Koch Institute, we found a relative increase with time in the prevalence in 15–34 year-olds (particularly 20–24-year-olds) compared with 35–49- and 10–14-year-olds (we excluded older and younger ages because of different healthcare seeking behaviour). This suggests an elevated role for that age group in propagating the epidemic following the introduction of physical distancing measures.

In response to the coronavirus disease (COVID-19) pandemic, public health scientists have produced a large and rapidly expanding body of literature that aims to answer critical questions, such as the proportion of the population in a geographic area that has been infected; the transmissibility of the virus and factors associated with high infectiousness or susceptibility to infection; which groups are the most at risk of infection, morbidity and mortality; and the degree to which antibodies confer protection to re-infection. Observational studies are subject to a number of different biases, including confounding, selection bias, and measurement error, that may threaten their validity or influence the interpretation of their results. To assist in the critical evaluation of a vast body of literature and contribute to future study design, we outline and propose solutions to biases that can occur across different categories of observational studies of COVID-19. We consider potential biases that could occur in five categories of studies: (1) cross-sectional seroprevalence, (2) longitudinal seroprotection, (3) risk factor studies to inform interventions, (4) studies to estimate the secondary attack rate, and (5) studies that use secondary attack rates to make inferences about infectiousness and susceptibility.