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

Recent outbreak of COVID-19 has led a rapid global spread around the world.Many countries have implemented timely intensive suppression to minimize theinfections, but resulted in high case fatality rate (CFR) due to criticaldemand of health resources. Other country-based factors such as socioculturalissues, ageing population etc., has also influenced practical effectiveness oftaking interventions to improve morality in early phase. To better understandthe relationship of these factors across different countries with COVID-19 CFRis of primary importance to prepare for potentially second wave of COVID-19infections. In the paper, we propose a novel regularized multi-task learningbased factor analysis approach for quantifying country-based factors affectingCFR in early phase of COVID-19 epidemic. We formulate the prediction of CFRprogression as a ML regression problem with observed CFR and othercountries-based factors. In this formulation, all CFR related factors werecategorized into 6 sectors with 27 indicators. We proposed a hybrid featureselection method combining filter, wrapper and tree-based models to calibrateinitial factors for a preliminary feature interaction. Then we adopted twotypical single task model (Ridge and Lasso regression) and one state-of-the-artMTFL method (fused sparse group lasso) in our formulation. The fused sparsegroup Lasso (FSGL) method allows the simultaneous selection of a common set ofcountry-based factors for multiple time points of COVID-19 epidemic and alsoenables incorporating temporal smoothness of each factor over the whole earlyphase period. Finally, we proposed one novel temporal voting feature selectionscheme to balance the weight instability of multiple factors in our MTFL model.

MFL_COVID19: Quantifying Country-based Factors affecting Case Fatality  Rate in Early Phase of COVID-19 Epidemic via Regularised Multi-task Feature  Learning