Successful Management of Public Health Projects Driven by AI in a BANI Environment
Abstract
1. Introduction
- 1.
- Quantify the impact of emergency-related factors, such as population displacement, healthcare disruptions, and periodic emergent events, on disease transmission, misinformation, and panic, using time-varying parameters to reflect long-term trends.
- 2.
- Conduct qualitative and quantitative analyses to identify critical risks (e.g., increased transmission, misinformation surges) and opportunities (e.g., AI-enabled fact-checking, infrastructure recovery) that influence public health outcomes.
- 3.
- Provide actionable insights for public health practitioners by evaluating the effectiveness of AI-driven interventions (e.g., predictive analytics, real-time monitoring, mental health support) in mitigating epidemic peaks and enhancing resilience.
- 4.
- Contribute to the broader understanding of AI’s role in managing public health crises in BANI environments, offering a scalable framework applicable to other conflicts or crisis settings.
2. Current Research Analysis
3. Materials and Methods
- 1.
- Disease spread (SEIR)—incorporates emergency-induced factors like displacement and healthcare disruption, which increase transmission rates.
- 2.
- Misinformation spread—models rapid misinformation dissemination via social media, amplified by targeted disinformation.
- 3.
- Panic spread—links panic to emergency events and misinformation, affecting disease transmission.
- 4.
- Emergency impact—introduces parameters for displacement, infrastructure damage, and disrupted interventions.
- Closed population (ignoring migration for simplicity).
- Homogeneous mixing within the city, though displacement increases contact rates.
- Emergency events are periodic, modelled as a constant factor for simplicity.
- Healthcare disruption and displacement are uniform across the population.
4. Results
- Enhanced disease spread due to displacement (increase in β to 0.36).
- Increased misinformation (increase in βm to 0.72).
- Increased panic (increase in βp to 0.6).
- Greater displacement (increase in δ to 0.24).
- More infrastructure damage (increase in κ to 0.36).
- Effective misinformation countering (increase in γm to 0.15).
- Effective panic calming (increase in γp to 0.3).
- Infrastructure recovery (decrease in κ to 0.15)
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
BANI | Brittle, anxious, non-linear, incomprehensible |
SEIR | Susceptible–exposed–infected–recovered |
AI | Artificial intelligence |
COVID-19 | Coronavirus disease 2019 |
NLP | Natural language processing |
ML | Machine learning |
LSTM | Long Short-Term Memory |
IDP | Internally displaced person |
PTSD | Post-traumatic stress disorder |
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Parameter | Description | Initial Value |
---|---|---|
β | baseline transmission rate | 0.3 |
Σ | progression rate from “Exposed” to “Infectious” | 0.2 |
γ | recovery rate | 0.1429 |
N | population | 500,000 |
δ | displacement factor | 0.2 |
κ | healthcare disruption factor | 0.3 |
βm | misinformation transmission rate | 0.6 |
γm | misinformation recovery rate | 0.1 |
R0m = βm/γm | misinformation reproduction number | 0 |
βp | panic transmission rate | 0.5 |
γp | panic recovery rate | 0.2 |
ω | emergency event frequency | 0.1 |
α | panic amplification factor for disease transmission | 0.5 |
ψ | emergency event impact on misinformation and panic | 0.3 |
Category | Risk | Description | Likelihood | Impact | Mitigation Measures |
---|---|---|---|---|---|
Technical | Data Quality | Inaccurate or incomplete data (EMR, IoT, social media) due to war disruptions. | High | High | Use AI for data cleaning, integrate multiple sources. |
Technical | Infrastructure Failures | Damage to servers or power grids from attacks. | Medium | High | Cloud platforms (AWS), backup systems. |
Organisational | Staff Shortages | Lack of AI specialists due to emigration. | High | Medium | International collaboration, remote training. |
Organisational | Resistance to Change | Distrust in AI among the medical staff or public. | Medium | Medium | Trust-building campaigns, algorithm transparency. |
Ethical | AI Bias | Algorithms may discriminate against vulnerable groups (IDPs). | Medium | High | Algorithm audits, WHO ethical standards. |
Ethical | Privacy Breaches | Data leaks due to cyberattacks. | High | High | Encryption, GDPR compliance. |
Regulatory | Non-Compliance | Violation of GDPR or WHO standards due to rapid AI deployment. | Low | High | Legal audits, regulatory consultations. |
Military | Increased Misinformation | Propaganda (Telegram) undermines health trust. | High | High | AI-NLP monitoring, counter-misinformation campaigns. |
Military | Population Displacement | IDP camps increase outbreak risks. | High | High | Mobile clinics, IoT monitoring. |
Category | Risk | Description | Likelihood | Impact | Mitigation Measures |
---|---|---|---|---|---|
Technical | AI Forecasting | Accurate outbreak predictions (LSTM, Random Forest). | High | High | Integrate EMR, IoT, social data. |
Technical | Automation | AI optimises logistics and planning. | Medium | High | Implement AI-PM tools. |
Organisational | International Support | Aid for infrastructure. | High | High | Direct funds to AI and health tech. |
Organisational | Public Trust | Transparent AI dashboards build trust. | Medium | Medium | Develop AR/VR visualisations. |
Ethical | Equity | AI ensures healthcare access for IDPs. | Medium | High | Algorithms targeting vulnerable groups. |
Regulatory | Global Standards | WHO compliance attracts investments. | Low | High | Certify AI systems. |
Military | Health Monitoring | IoT for real-time monitoring in IDP camps | Medium | High | Deploy wearable devices |
Scenario | Peak Infectious (% Change) | Peak Misinformation (% Change) | Peak Panic (% Change) |
---|---|---|---|
Baseline | 0.0 | 0.0 | 0.0 |
High β | 28.3 | 0.0 | 0.0 |
High βm | 5.2 | 22.7 | 18.9 |
High βp | 12.6 | 0.0 | 15.4 |
High δ | 20.8 | 0.0 | 0.0 |
High κ | 15.1 | 0.0 | 0.0 |
Scenario | Peak Infectious (% Change) | Peak Misinformation (% Change) | Peak Panic (% Change) |
---|---|---|---|
Baseline | 0.0 | 0.0 | 0.0 |
High γm | −4.8 | −18.2 | −15.6 |
High γp | −8.3 | 0.0 | −12.9 |
Low κ | −10.7 | 0.0 | 0.0 |
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Bushuyev, S.; Bushuyeva, N.; Nekrasov, I.; Chumachenko, I. Successful Management of Public Health Projects Driven by AI in a BANI Environment. Computation 2025, 13, 160. https://doi.org/10.3390/computation13070160
Bushuyev S, Bushuyeva N, Nekrasov I, Chumachenko I. Successful Management of Public Health Projects Driven by AI in a BANI Environment. Computation. 2025; 13(7):160. https://doi.org/10.3390/computation13070160
Chicago/Turabian StyleBushuyev, Sergiy, Natalia Bushuyeva, Ivan Nekrasov, and Igor Chumachenko. 2025. "Successful Management of Public Health Projects Driven by AI in a BANI Environment" Computation 13, no. 7: 160. https://doi.org/10.3390/computation13070160
APA StyleBushuyev, S., Bushuyeva, N., Nekrasov, I., & Chumachenko, I. (2025). Successful Management of Public Health Projects Driven by AI in a BANI Environment. Computation, 13(7), 160. https://doi.org/10.3390/computation13070160