English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We used Bayesian model inversion to estimate epidemic parameters from thereported case and death rates from seven countries using data from late January2020 to April 5th 2020. Two distinct generative model types were employed:first a continuous time dynamical-systems implementation of aSusceptible-Exposed-Infectious-Recovered (SEIR) model and second: a partiallyobservable Markov Decision Process (MDP) or hidden Markov model (HMM)implementation of an SEIR model. Both models parameterise the size of theinitial susceptible population (S0), as well as epidemic parameters. Parameterestimation (data fitting) was performed using a standard Bayesian scheme(variational Laplace) designed to allow for latent unobservable states anduncertainty in model parameters.  Both models recapitulated the dynamics of transmissions and disease as givenby case and death rates. The peaks of the current waves were predicted to be inthe past for four countries (Italy, Spain, Germany and Switzerland) and toemerge in 0.5-2 weeks in Ireland and 1-3 weeks in the UK. For France one modelestimated the peak within the past week and the other in the future in twoweeks. Crucially, Maximum a posteriori (MAP) estimates of S0 for each countryindicated effective population sizes of below 20% (of total population size),under both the continuous time and HMM models. With a Bayesian weighted averageacross all seven countries and both models, we estimated that 6.4% of the totalpopulation would be immune. From the two models the maximum percentage of theeffective population was estimated at 19.6% of the total population for the UK,16.7% for Ireland, 11.4% for Italy, 12.8% for Spain, 18.8% for France, 4.7% forGermany and 12.9% for Switzerland.  Our results indicate that after the current wave, a large proportion of thetotal population will remain without immunity.

Estimating required 'lockdown' cycles before immunity to SARS-CoV-2:  Model-based analyses of susceptible population sizes, 'S0', in seven European  countries including the UK and Ireland