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

Researchers have shown that even simple empirical models stemming frombiological growth modeling have the potential to provide useful information onthe development and severity of ongoing epidemics since they can be employed astools for carrying out projections on the size of the affected population,timing of turning points, as well as best- and worst-case scenarios.Nevertheless, they commonly exhibit considerable sensitivity to some inputparameters' variance which results in large fluctuations in the generatedprojections, thus rendering predictions difficult and even risky. In this workwe examine a novel meta-projections-based approach which allows us to evaluatethe model's current trends and assess whether generated projections are at atransient or stable state. Meta-projections can be extracted from graphs ofsuccessive estimations of model's parameters and resulting projections, over asequence of days being gradually added to the employed model. In other words,projections are carried out on truncated time series of cumulative numbers ofconfirmed cases with increased lengths at each successive evaluation. Thisallows us to trace the values of model parameters over a certain period of timeand examine their trends which may converge to specific values forsettled-growth cases or exhibit a changing or even an erratic behavior forcases that undergo epidemiological transitions and/or are inappropriatelydescribed by the current model instance(s). We have computed meta-projectionsand compared our findings for countries at different stages of the epidemicwith stable or unstable behaviors and increasing or decreasing numbers ofconfirmed cases. Our results indicate that meta-projections can aid researchersin assessing the appropriateness of their relevant models and in effectdecrease the uncertainty in their estimations of an epidemic's severity anddevelopment.

Qualitative and quantitative evaluation of COVID-19 outbreak severity with the use of meta-projections based on Richards' curve parameters