COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq
TLDR
In this paper, a model consisting of three artificial neural networks' (ANN) functions was developed to predict COVID-19 separation in Iraq based on real infection data supplied by the public health department at the Iraqi Ministry of Health.Abstract:
The prediction of diseases caused by viral infections is a complex medical task where many real data that consists of different variables must be employed. As known, COVID-19 is the most dangerous disease worldwide; nowhere, an effective drug has been found yet. To limit its spread, it is essential to find a rational method that shows the spread of this virus by relying on many infected people’s data. A model consisting of three artificial neural networks’ (ANN) functions was developed to predict COVID-19 separation in Iraq based on real infection data supplied by the public health department at the Iraqi Ministry of Health. The performance efficiency of this model was evaluated, where its performance efficiency reached 81.6% when employed four statistical error criteria as mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe coefficient (NC). The severity of the virus’s spread across Iraq was assessed in a short term (in the next 6 months), where the results show that the spread severity will intensify in this short term by 17.1%, and the average death cases will increase by 8.3%. These results clarified by creating spatial distribution maps for virus spread are simulated by employing a Geographic Information System (GIS) environment to be used as a useful database for developing plans for combating viruses in Iraq.read more
Citations
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Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective.
TL;DR: Understanding is expanded of the relationship between COVID-19 cases and deaths by using a global dataset and the instrumental variable generalized method of moment’s model (IV-GMM) for the analysis that addresses endogeneity and omitted variable issues.
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Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review
Gihan Jayatilaka,Hassan J,Umar Marikkar,Rumali Perera,Suren Sritharan,Harshana Weligampola,M. P. B. Ekanayake,Roshan Godaliyadda,Parakrama Ekanayake,Herath,Godaliyadda Gmd,Anuruddhika Rathnayake,Samath D Dharmaratne,Samath D Dharmaratne,Janaka Ekanayake,Janaka Ekanayake +15 more
TL;DR: This review presents a comprehensive analysis of the use of AI techniques for spatio-temporal modeling and forecasting and impact modeling on diverse populations as it relates to COVID-19 and lists potential paths of research for which AI based techniques can be used for greater impact in tackling the pandemic.
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Roles of Artificial Intelligence and Extended Reality Development in the Post-COVID-19 Era
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TL;DR: In this paper, the influence of meteorological (temperature, wind speed, humidity and specific enthalpy) and human mobility variables in six cities (Barranquilla, Bogota, Cali, Cartagena, Leticia and Medellin) from different biomes in Colombia on the coronavirus dissemination from March 25, 2020, to January 15, 2021.
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