Abstract
Following initial declines, in mid 2020 a resurgence in transmission of novel coronavirus disease (COVID-19) occurred in the US and Europe. As COVID19 disease control efforts are re-intensified, understanding the age demographics driving transmission and how these affect the loosening of interventions is crucial. We analyze aggregated, age-specific mobility trends from more than 10 million individuals in the US and link these mechanistically to age-specific COVID-19 mortality data. We estimate that as of October 2020, individuals aged 20-49 are the only age groups sustaining resurgent SARS-CoV-2 transmission with reproduction numbers well above one, and that at least 65 of 100 COVID-19 infections originate from individuals aged 20-49 in the US. Targeting interventions – including transmission-blocking vaccines – to adults aged 20-49 is an important consideration in halting resurgent epidemics and preventing COVID-19-attributable deaths.
![Alexandra Blenkinsop](/author/alexandra-blenkinsop/avatar_hu2229c332889ae35166d7bf44c7865ef5_694973_270x270_fill_q75_lanczos_center.jpg)
Postdoctoral Research Associate
My research involves using viral sequence data and patient data to characterise HIV transmission and guide public health interventions.
![Yu Chen](/author/yu-chen/avatar_hu069e3b456fe29d86c79690dcf36037d4_726913_270x270_fill_q75_lanczos_center.jpg)
PhD student
I am a PhD student in Modern Statistics and Statistical Machine Learning at Imperial College London.
![Helen Coupland](/author/helen-coupland/avatar_hu8606bc4f5f152d7b976af9a5cfd1775a_2524867_270x270_fill_q75_lanczos_center.JPG)
PhD Student
Uses machine learning approaches to examine the dynamics of exposure events that give rise to health outcomes.
![Juliette Unwin](/author/juliette-unwin/avatar_hue98d31444251c0a24604054a682cc193_27377_270x270_fill_q75_lanczos_center.jpg)
Lecturer in Statistical Science
I develop methods to solve questions related to infectious disease outbreaks.
![Swapnil Mishra](/author/swapnil-mishra/avatar_hu53c8df9223bc15c139352553532a902f_615192_270x270_fill_q75_lanczos_center.jpg)
Assistant Professor of Machine Learning and Public Health
I primarily work at intersection of public health, machine learning and Bayesian modelling.
![Seth Flaxman](/author/seth-flaxman/avatar_hu4872f74b605dbd1fb60652f9171497a3_56151_270x270_fill_q75_lanczos_center.jpg)
Associate Professor
My research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and social science.
![Samir Bhatt](/author/samir-bhatt/avatar_hu5f93c326143a24e8087fbc255373e0c3_10938_270x270_fill_q75_lanczos_center.jpg)
Professor of Machine Learning, Statistics and Public Health
I focus on mathematical, statistical and computer science tools to answer questions in human health and biology.
![Oliver Ratmann](/author/oliver-ratmann/avatar_hu9f2f8ff2dfe402ac970a6ef047e4bf6b_17851_270x270_fill_q75_lanczos_center.jpg)
Reader in Statistics and Machine Learning for Public Good
I develop bespoke statistical methods for public good. My group and I are particularly interested in novel Bayesian methods that harness information in viral deep sequence data, mobile phone mobility data, and time-resolved patient data to characterise the spread of infectious diseases, and to guide public health interventions.