Report 32: Age groups that sustain resurging COVID-19 epidemics in the United States

Abstract

Following initial declines, in mid 2020, a resurgence in transmission of novel coronavirus disease (COVID-19) has occurred in the United States and parts of Europe. Despite the wide implementation of non-pharmaceutical interventions, it is still not known how they are impacted by changing contact patterns, age and other demographics. As COVID-19 disease control becomes more localised, understanding the age demographics driving transmission and how these impacts the loosening of interventions such as school reopening is crucial. Considering dynamics for the United States, we analyse aggregated, age-specific mobility trends from more than 10 million individuals and link these mechanistically to age-specific COVID-19 mortality data. In contrast to previous approaches, we link mobility to mortality via age-specific contact patterns and use this rich relationship to reconstruct accurate transmission dynamics. Contrary to anecdotal evidence, we find little support for age-shifts in contact and transmission dynamics over time. We estimate that, until August, 63.4% [60.9%-65.5%] of SARS-CoV-2 infections in the United States originated from adults aged 20-49, while 1.2% [0.8%-1.8%] originated from children aged 0- 9. In areas with continued, community-wide transmission, our transmission model predicts that re-opening kindergartens and elementary schools could facilitate spread and lead to additional COVID-19 attributable deaths over a 90-day period. These findings indicate that targeting interventions to adults aged 20-49 are an important consideration in halting resurgent epidemics and preventing COVID-19-attributable deaths when kindergartens and elementary schools reopen.

Rapid review of social contact patterns during the COVID-19 pandemic

“…Post-relaxation, variation in the extent of relaxation may have contributed to further variation in contact rates and patterns across countries. Our observation that increases in contact rates post-relaxation were driven by working age adults are consistent with concomitant opening of workplaces and a rebound in mobility within this age group previously reported from cell phone data 50 . This observation supports the notion that contacts at work and in the community by the working population played a key role in driving and sustaining SARS-CoV-2 transmission in the summer of 2020 50,51 .…”

SARS-CoV-2 Seroprevalence in a University Community: A Longitudinal Study of the Impact of Student Return to Campus on Infection Risk Among Community Members

“…While in-person student instruction has been associated with an increase in per-capita COVID-19 incidence at the aggregate level, 12 these results suggest that outbreaks in the returning student and the community resident cohort we studied were asynchronous, implying limited between-cohort transmission. A recent analysis of age-specific movement and transmission patterns in the US suggested that individuals between the ages of 20-34 disproportionately contributed to spread of SARS-CoV-2; 28 however, despite close geographic proximity to a college-aged population, transmission in our community resident sample appears distinctly lagged; suggestive of the potential for health behaviours to prevent infection.…”

Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing

“…The copyright holder for this preprint this version posted October 20, 2020. ; https://doi.org/10.1101/2020. 10 ሺ ‫ݐ‬ ሻ ܰ…”

“…The copyright holder for this preprint this version posted October 20, 2020. ; https://doi.org/10.1101/2020. 10 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

“…Digital proxies of human mobility and physical mixing have been shown to provide useful insights into disease transmission [5][6][7][8][9][10][11] . Specifically, during the ongoing COVID-19 pandemic, data of human mobility and mixing have been used to monitor the effectiveness of social distancing interventions 5,9,10 . Here, using COVID-19 in Hong Kong as an example, we describe a framework that integrates such digital proxies into conventional epidemic models to (i) track transmissibility in near real time;…”