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

The recent global outbreak of covid-19 is affecting many countries around theworld. Due to the growing number of newly infected individuals and thehealth-care system bottlenecks, it will be useful to predict the upcomingnumber of patients. This study aims to efficiently forecast the is used toestimate new cases, number of deaths, and number of recovered patients in Iranfor 180 days, using the official dataset of the Iranian Ministry of Health andMedical Education and the impact of control measures on the spread of COVID-19.Four different types of forecasting techniques, time series, and machinelearning algorithms, are developed and the best performing method for the givencase study is determined. Under the time series, we consider the fouralgorithms including Prophet, Long short-term memory, Autoregressive,Autoregressive Integrated Moving Average models. On comparing the differenttechniques, we found that deep learning methods yield better results than timeseries forecasting algorithms. More specifically, the least value of the errormeasures is observed in seasonal ANN and LSTM models. Our findings showed thatif precautionary measures are taken seriously, the number of new cases anddeaths will decrease, and the number of deaths in September 2021 will reachzero.

Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning