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

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 ( 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.

A study on geospatially assessing the impact of COVID-19 in Maharashtra, India

“…Furthermore, the Getis-Ord spatial autocorrelation technique was adopted to predict spatial hotspots and coldspots for COVID-19. The results obtained from K-means clustering approach to observe spatially clustered patterns for spread of COVID-19 is consistent with some recent findings Azarafza et al, YYYY , Yahya et al, 2021 . However, to our knowledge, in view of COVID-19 outbreak this study for the first time leveraged Elbow method towards estimating optimal number of clusters and minimizing the ICV to visualize spatial pattern of spread across different districts of Maharashtra.…”

Modelling spatial variations of novel coronavirus disease (COVID-19): evidence from a global perspective

“…Other models and statistical tools have been employed to explored COVID-19 cases, deaths, or both. Yahya et al (2021) applied three artificial neural networks (ANN) functions to predict the spread of COVID-19 cases and found that the spread severity is expected to intensify in the next few months (six) by 17.1% while the average deaths by 8.3% with a model performance efficiency reaching 81.6%. The model provided the expected cases and deaths due to the virus for authorities to better plan for the management of the case and death counts in the future.…”

Artificial intelligence: Potential tool to subside SARS-CoV-2 pandemic

“…AI helps analyze and assess information from reported cases and uses that to predict the situation regarding COVID-19. Yahya et al used the geographic information system and developed an AI model with three artificial networks to predict the impact of COVID- 19 in Iraq [ 14 ]. In addition, they predict the severity in which the virus would spread within the next 6 months and the average death case increase during the upcoming days, which helped develop a plan to combat the social effects of COVID 19 in Iraq.…”