Sarafa A. Iyaniwura

TL;DR: A Bayesian epidemiological model in which a proportion of individuals are willing and able to participate in distancing is introduced, with the timing of distancing measures informed by survey data on attitudes to distancing and COVID-19 indicated.

TL;DR: It is found that physical distancing has had a strong impact, consistent with declines in reported cases and in hospitalization and intensive care unit numbers, and that intermittentDistancing measures - if sufficiently strong and robustly followed - could control COVID-19 transmission, but that if distancing measures are relaxed too much, the epidemic curve would grow to high prevalence.

TL;DR: In this article, the authors quantitatively estimated the relative benefits of COVID-19 vaccines, in terms of preventing infection and death, with a particular focus on effectiveness in elderly people.

TL;DR: A Bayesian epidemiological model estimates the leeway to reopen in a range of national and regional jurisdictions that have experienced different COVID-19 epidemics and the risks associated with different levels of reopening and the likely burden of new cases due to introductions from other jurisdictions.

TL;DR: Numerical simulations of the reduced system of ODEs and the full PDE-ODE model are consistent, and they predict a decrease in the spread of infection as the diffusion rate of pathogens increases.