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1.
International Journal of Information Technology and Decision Making ; 2021.
Article in English | Scopus | ID: covidwho-1394219

ABSTRACT

This paper presents a forecasting model for the mortality rates of COVID-19 in six of the top most affected countries depending on the hybrid Genetic Algorithm and Autoregressive Integrated Moving Average (GA-ARIMA). It was aimed to develop an advanced and reliable predicting model that provides future forecasts of possible confirmed cases and mortality rates (Total Deaths per 1 million Population of COVID-19) that could help the public health authorities to develop plans required to resolve the crisis of the pandemic threat in a timely and efficient manner. The study focused on predicting the mortality rates of COVID-19 because the mortality rate determines the prevalence of highly contagious diseases. The Genetic algorithm (GA) has the capability of improving the forecasting performance of the ARIMA model by optimizing the ARIMA model parameters. The findings of this study revealed the high prediction accuracy of the proposed (GA-ARIMA) model. Moreover, it has provided better and consistent predictions compared to the traditional ARIMA model and can be a reliable method in predicting expected death rates as well as confirmed cases of COVID-19. Hence, it was concluded that combining ARIMA with GA is further accurate than ARIMA alone and GA can be an alternative to find the parameters and model orders for the ARIMA model. © 2021 World Scientific Publishing Company.

2.
Eur Rev Med Pharmacol Sci ; 24(22): 11977-11981, 2020 11.
Article in English | MEDLINE | ID: covidwho-962034

ABSTRACT

Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARS-CoV-19 through existing data that reveal the SARS's cause. Artificial intelligence (AI) learning models can be created to predict drug structures that can be used to treat COVID-19. Despite the effectively demonstrated repurposed drugs, more repurposed drugs should be recognized. Furthermore, technological advancements have been helpful in the battle against COVID-19. Machine intelligence technology can support this procedure by rapidly determining adequate and effective drugs against COVID-19 and by overcoming any barrier between a large number of repurposed drugs, laboratory/clinical testing, and final drug authorization. This paper reviews the proposed vaccines and medicines for SARS-CoV-2 and the current application of AI in drug repurposing for COVID-19 treatment.


Subject(s)
Artificial Intelligence , COVID-19 Drug Treatment , Drug Development , Drug Repositioning , Adenosine Monophosphate/analogs & derivatives , Adenosine Monophosphate/therapeutic use , Alanine/analogs & derivatives , Alanine/therapeutic use , Antibodies, Monoclonal, Humanized/therapeutic use , Antiviral Agents/therapeutic use , Ascorbic Acid/therapeutic use , COVID-19/prevention & control , COVID-19 Vaccines/therapeutic use , Chloroquine/therapeutic use , Deep Learning , Drug Combinations , Humans , Hydroxychloroquine/therapeutic use , Immunosuppressive Agents/therapeutic use , Lopinavir/therapeutic use , Machine Learning , Ribavirin/therapeutic use , Ritonavir/therapeutic use , Vitamins/therapeutic use
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