In this post, I’ll explore the role of cloud computing, AI, and next-generation networks in tackling COVID-19 and other infectious diseases

Context: Pandemic Information Gaps

The COVID-19 crisis exposed major gaps in our ability to:

  • Model disease spread and predict peaks
  • Monitor patients remotely at scale
  • Securely share sensitive health data
  • Rapidly deploy diagnostic AI

In his article, Al-Turjman [1] argues that integrating AI, big data, cloud computing, next-gen networks, and mobile devices could help fill these gaps.

Some Key Cloud-AI Use Cases Mentioned

1. Predictive Modeling at Scale [2]

  • Approach: Use cloud-hosted SIR/SIRS models with parameter estimation.
  • Impact: Predict peak infections and evaluate lockdown effects.

2. Deep Neural Time-Series Forecasting [3]

  • Techniques: LSTM, GRU, and RNN trained on Asia-Pacific COVID-19 data.
  • Outcome: > 90 % accuracy forecasting new cases 10 days ahead.

3. Automated Medical Imaging Diagnostics [4]

  • Example: AI-assisted CT analysis to screen COVID-19 lesions.
  • Results: Diagnosis time reduced from ~ 3.6 min to ~ 0.74 min; accuracy up to 97 %.

4. Secure Remote Monitoring via IoT [5]

  • Model: Cloud-IoT platform encrypts wearable device data with lightweight block ciphers.
  • Analytics: K-star classification predicts critical events with ~ 95 % accuracy.

Challenges & Future Directions

  • Data Privacy: Ensuring HIPAA/GDPR compliance in multi-center studies
  • Model Robustness: Adapting AI to virus variants and unseen populations
  • Interoperability: Standardizing FHIR/DICOM data exchange across cloud services

Al-Turjman highlights the need for cross-disciplinary collaboration—from epidemiologists to network engineers—to realize these cloud-AI solutions in clinical practice.

Conclusion

The fusion of cloud computing and AI has immense potential to transform infectious disease diagnosis and management. As the editorial underscores, building open, scalable, and secure platforms will be key to responding not just to COVID-19, but to future pandemics as well.

References

  • [1] F. Al‐Turjman, «COVID‐19 special issue: Intelligent solutions for computer communication‐assisted infectious disease diagnosis», Expert Systems, may 2020, doi: 10.1111/exsy.12574.
  • [2] V. Srivastava, S. Srivastava, G. Chaudhary, y F. Al-Turjman, «A systematic approach for COVID-19 predictions and parameter estimation», Personal And Ubiquitous Computing, vol. 27, n.o 3, pp. 675-687, nov. 2020, doi: 10.1007/s00779-020-01462-8.
  • [3] H. T. Rauf et al., «Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks», Personal And Ubiquitous Computing, vol. 27, n.o 3, pp. 733-750, ene. 2021, doi: 10.1007/s00779-020-01494-0.
  • [4] D. Zhang, X. Liu, M. Shao, Y. Sun, Q. Lian, y H. Zhang, «The value of artificial intelligence and imaging diagnosis in the fight against COVID-19», Personal And Ubiquitous Computing, vol. 27, n.o 3, pp. 783-792, feb. 2021, doi: 10.1007/s00779-021-01522-7.
  • [5] S. Akhbarifar, H. H. S. Javadi, A. M. Rahmani, y M. Hosseinzadeh, «A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment», Personal And Ubiquitous Computing, vol. 27, n.o 3, pp. 697-713, nov. 2020, doi: 10.1007/s00779-020-01475-3.