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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Studying the dynamics of COVID-19 is of paramount importance to understandingthe efficiency of restrictive measures and develop strategies to defend againstupcoming contagion waves. In this work, we study the spread of COVID-19 using asemi-supervised neural network and assuming a passive part of the populationremains isolated from the virus dynamics. We start with an unsupervised neuralnetwork that learns solutions of differential equations for different modelingparameters and initial conditions. A supervised method then solves the inverseproblem by estimating the optimal conditions that generate functions to fit thedata for those infected by, recovered from, and deceased due to COVID-19. Thissemi-supervised approach incorporates real data to determine the evolution ofthe spread, the passive population, and the basic reproduction number fordifferent countries.

Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread