International Journal of Computer Techniques Volume 12 Issue 3 | A Prediction of Spatial Distribution for Epidemic Infectious Diseases Using a Proposed Deep Learning Framework Based on GIS (Case Study: COVID-19 in Egypt)
A Prediction of Spatial Distribution for Epidemic Infectious Diseases Using a Proposed Deep Learning Framework Based on GIS
Authors
Ismail S. Tawfik – PhD Candidate, Computer and Information Systems Department, Sadat Academy for Management Sciences, Cairo Egypt
Email: Ismail.tawfik@gmail.com
Prof. Dr. Christina Albert – Professor, Computer and Information Systems Department, Sadat Academy for Management Sciences, Cairo Egypt
Email: sams.christina.albert@gmail.com
Abstract
COVID-19 has significantly impacted over 170 countries, causing a sharp rise in infections and fatalities. This study presents a deep learning framework integrating Geographic Information Systems (GIS) to predict the spatial distribution of COVID-19 risk in Egypt…
Keywords
World Health Organization (WHO), Ministry of Health and Population (MOHP), Rectified Linear Unit (ReLU), Mean Squared Error (MSE)
Conclusion
The spatial distribution map of COVID-19 risks for Egypt’s governorates developed using GIS and deep learning closely aligns with official health data…
References
- WHO 2020, “Coronavirus disease 2019 (COVID-19): Situation Report”, World Health Organization, pp. 72,82.
- Eales, T., et al. (2020). “Social distancing, population density, and the spread of COVID-19 in urban areas”, Health & Place.
- Verity R., Okell L.C., Dorigatti I., et al., (2020), “Estimates of the severity of coronavirus disease 2019: A model-based analysis”, Lancet Infect. Dis. 20.
- Arti, M., Bhatnagar, K., (2020), “Modeling and predictions for COVID-19 spread in India”.
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