COVID-19 prediction analysis using artificial intelligence procedures and GIS spatial analyst: a case study for Iraq

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...

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Bibliographic Details
Published in:Applied geomatics 2021-09, Vol.13 (3), p.481-491
Main Authors: Yahya, Bashar Moneer, Yahya, Farah Samier, Thannoun, Rayan Ghazi
Format: Article
Language:English
Subjects:
Analysis
Artificial intelligence
Case studies
Coronaviruses
COVID-19
Earth and Environmental Science
Geographic information systems
Geographical information systems
Geographical Information Systems/Cartography
Geography
Geophysics/Geodesy
Health aspects
Infection
Infections
Information systems
Measurement Science and Instrumentation
Original Paper
Public health
Remote sensing
Remote Sensing/Photogrammetry
Spatial analysis
Spatial distribution
Surveying
Viruses
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Summary: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 ( R 2 ), 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.
ISSN:1866-9298
1866-928X
DOI:10.1007/s12518-021-00365-4