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AI Enhanced Real Time Monitoring of Airborne Microbes for Early Detection of Pandemics

Students & Supervisors

Student Authors
Md. Tamzid Rahman
Bachelor of Science in Computer Science & Engineering, FST
Adnan Akib
Bachelor of Science in Computer Science & Engineering, FST
Tawfiqul Hasan
Bachelor of Science in Computer Science & Engineering, FST
Maruf Ahammed
Bachelor of Science in Computer Science & Engineering, FST
Supervisors
Md. Mortuza Ahmmed
Associate Professor, Faculty, FST

Abstract

Bangladesh is one of the world's most populous nations and is highly susceptible to airborne and vector-borne pathogen pandemics. Present surveillance mechanisms, based on symptomatic presentation and laboratory confirmation, are reactive and are behind outbreak response. The integration of artificial intelligence with real-time airborne microbial sensing offers an unprecedented opportunity to create a proactive pandemic preparedness system. We created a combined dataset (2000–2023, monthly) for the Dhaka urban agglomeration by integrating microbial air burdens (Influenza A, SARS-CoV-2, Dengue RNA copies/m³), environmental factors (PM2.5, humidity, rainfall, temperature), health and mobility markers (influenza-like illness, hospitalization, anonymized mobility indices), and socio-economic drivers (poultry outbreak alerts, rice harvest seasons). Data sources include WHO, IEDCR, DoE, BMD, CDC, and national ICT divisions. Deep architectures (autoencoders, CNNs) were used to develop microbial baselines, detect outliers, and learn non-linear domain interactions. The result shows that upraised dengue RNA titers (1650 copies/m³, Sep 2023) preceded hospitalization peaks (4800 admissions), while COVID-19 waves (Mar 2020) were spurred by mobility shifts and excessive PM2.5 exposure. AI-driven anomaly detection compressed outbreak detection cycles from weeks to days with >85% predictive accuracy in simulation. IoT-based bioaerosol sampler deployment with 5G networks demonstrated good scalability in covering the whole country. Artificial Intelligence enabled airborne microbial surveillance with environmental, health, and socio-economic interventions provides a solid basis for the early identification of outbreaks in Bangladesh. It has the potential to augment One Health approaches, minimize socio-economic disruption, and serve as an emailable model for pandemic-proof cities in other low- and middle-income countries.

Keywords

AI in Public Health Airborne Microbes Pandemic Preparedness Bioaerosol Surveillance Data Fusion Bangladesh.

Publication Details

  • Type of Publication:
  • Conference Name: BURS 1st National Youth Research Summit 2025
  • Date of Conference: 18/10/2025 - 18/10/2025
  • Venue: University of Barishal
  • Organizer: Barishal University Research Society (BURS)
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