COVID-19 prevalence forecasting using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN): Case of Turkey.
By Gülhan Toğa,Berrin Atalay,M Duran Toksari Jun 14, 2021
A local outbreak of unknown pneumonia was detected in Wuhan (Hubei, China) in December 2019. It is determined to be caused by a severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) and called COVID-19 by scientists. The outbreak has since spread all over the world with a total of 120,815,512 cases and 2,673,308 deaths as of 16 March 2021. The health systems in the world collapsed in many countries due to the pandemic and many countries were negatively affected in the social life. In such situations, it is very important to predict the load that will occur in the health system of a country. In this study, the COVID-19 prevalence of Turkey is inspected. The infected cases, the number of deaths, and the recovered cases are predicted with Autoregressive Integrated Moving Average
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Conclusion (~1,700 words).
All backed up by over 200 references (~6,500 words).
We must stop crediting the wrong people for inventions made by others.
Instead let s heed the recent call in the journal
Nature: Let 2020 be the year in which we value those who ensure that
science is self-correcting [SV20].
Like those who know me can testify, finding and citing original sources of scientific and technological innovations is important to me, whether they are mine or other people s [DL1][DL2][HIN][NASC1-9]. The present page is offered as a resource for computer scientists who share this inclination.
By grounding research in its true intellectual foundations and crediting the original inventors,