Decision intelligence, AR and the integration of social capital for modern emergency planning

The world is facing unprecedented challenges (globally) in emergency and disaster management. From long-term consequences of chronic diseases such as ME/CFS, Long Covid and MCS, to increasing environmental disasters, toxic exposures and infrastructural weaknesses, to digital threats, pandemics and new medical requirements. One of the major weaknesses of traditional emergency concepts is the lack of consideration given to vulnerable groups, in particular the many chronically ill people, mothers, babies and emergency personnel who are affected themselves.

Conventional and traditional emergency plans are no longer sufficient! Standardized and “official” checklists, as well as countless static documents, a lack of flexibility and far too little practicality have nothing to do with the true reality in 2025. Without the enormous experience from military areas, wargaming, social capital (i.e. very intelligent relationship building and social networking) and personalized (scientific/medical) approaches, we will not be able to make targeted progress here.

Emergency and disaster concepts in their current form (May 2025) simply fall far too short. They also overlook the very important pillar of social capital. Without an intensive inclusion of this pillar (there is at least one other very important pillar missing - which I will talk about at the Pracademic Emergency Management and Homeland Security Summit 2025), the house of optimized disaster and emergency management (DEM) CANNOT stand. It simply will not work. Effective disaster management is not only based on official structures and digital innovations, but also on very intelligent relationship building and social networking. The power of social networks (here I am explicitly NOT talking about leaders) and the enormous influence of informal (local) actors is completely underestimated and thus also “slows down” valuable resource flows.

The formation of social capital and cross-sector collaboration should also be directly integrated into AI and AR developments, as true crisis management is not only based on data and technology, but on stable networks and trust between different actors.

Deeply divided societies, whether in Europe, America ... and also our chronically ill societies make this necessary and also required.

As of today (May 5, 2025), I recommend that even very innovative Decision Intelligence (DI) and Augmented Reality (AR) systems/models should definitely take these extensions into account.

A combination of Decision Intelligence (DI) and Augmented Reality (AR) creates a completely new approach to emergency, risk and disaster management. AR could complement the strengths of Decision Intelligence (DI) and offer completely new possibilities!

1. Data-based analyses in real time instead of blanket reactions.

2. Personalized health strategies for the chronically ill instead of rigid evacuation plans.

3. Active inclusion of the many informal networks of mothers, healthcare professionals, e.g. midwives, and also newly formed communities on “Long Covid / ME/CFS”. These social networks should be actively integrated into the Readiness Decision Impact Model. Mothers and their babies in particular must also be protected. It is essential to ensure that infants are provided with breast milk and special protection and sleeping areas, as well as protection from exposure to pollutants through specially separated areas.

4. Precise environmental monitoring & pollutant analyses for emergency services instead of inadequate protective measures.

5. Visualized risk zones & adaptive scenario simulations instead of possibly “outdated” planning documents.

6. Intelligent distribution of resources for medical care instead of possibly not optimally coordinated aid measures.

7. Combat Readiness Strategies often focus heavily on logistical and technical operational planning, while the Readiness Decision Impact Model assesses risks based on classic variables such as infrastructure and environmental conditions. The intensive consideration of social capital offers a great opportunity to go far beyond geospatial simulations and performance metrics. I am specifically thinking here of operationalizing social capital to make operations more effective and mobilize trust (i.e. key people and multipliers as strategic factors in operational planning and decision making, and informal networks and economic community structures as strong resources for security and crisis response). Future versions of Combat Readiness Strategies should consider social capital formation as a mission-critical factor. If possible, the Readiness Decision Impact Model should integrate informal networks as a risk factor in disaster simulations, i.e. social unrest, multipliers and informal crisis responses could already be predicted on the basis of data. I see this not just as a “nice-to-have”, but as a necessary safety-critical adaptation.

8. AI should not only assess infrastructure risks, but also analyze social stability to anticipate where problems might arise (of course, I am also thinking here of the Readiness Decision Impact Model with a long-term build-up of trust networks) - i.e. integration of social capital formation into the AI model

- Long-term analysis of social networks - Where are there strong structures of trust that cope with disasters more quickly?

- Recommendations for community networking - AI could suggest where relationships need to be strengthened to better mitigate future crises.

- Response teams must learn to work with informal multipliers in order to communicate effectively with the population, because those who engage in intensive social capital building before a crisis can act much faster in critical moments. Appropriate multipliers clearly increase the speed of deployment and also improve resource mobilization. This means identifying many key people (I am NOT thinking of leaders here!) within the affected region for better operational control and also integrating these multipliers into tactical planning to speed up evacuations and logistical processes.

Example: Imagine an emergency team arrives in a crisis area, but the population does not respond to official instructions. BUT a well-connected (local) doctor or a local community representative convinces the population to cooperate. The response team with early social capital building can work much more effectively than an isolated response command.

Or a decision-maker should determine which regions receive priority for disaster relief. The model shows you that socially well-connected cities/communities would need less external aid than isolated communities, i.e. investing in social networks always improves resilience to crises in the long term. Social movements and resistance should definitely be integrated into the models, because physical and logistical factors alone are unfortunately not enough, as uncontrolled social crisis movements also increase the risk of collapse and resource bottlenecks.

Please always remember that traditional risk analyses often focus on physical dangers and ignore socially networked crisis movements.

- Simulated network effects on different emergency responses - For example, how well prepared is a city based on its social capital? (A city with high social networking will receive help faster because city residents support each other - AI could “anticipate” this and a community without stable social networks is likely to be more disorganized in a crisis - AI could also already identify this and suggest what actions should be taken). AI and AR should actively incorporate social capital formation in order to truly revolutionize modern crisis management and be able to withstand the extremely complex challenges. AI could map social networks in order to mobilize resources more effectively and AR could then train the emergency services for cross-sector collaboration and trust building. Readiness decision impact models should definitely integrate social stability as a key indicator for crisis preparedness, because social capital formation is not just a supplement - it is the decisive basis for functioning, sustainable crisis management. AI should not only process infrastructure data, but also recognize social trust networks in order to manage even more effectively, because in a disaster, not only the technical response is crucial, but also social mobilization - here AI should combine both in an optimized way. In my opinion, this also includes a dynamic prioritization of communication strategies (trust-based crisis communication by AI, i.e. an automatic evaluation of the credibility of information sources - which messages have the potential to prevent or trigger panic? Targeted message selection for social groups - AI could help determine which messages should be disseminated via local networks. AI could also help to create a link between official bodies and informal groups - who helps to disseminate crucial warnings?)

Three examples for a better understanding: Disaster response with “precisely fitting” informal networks in a city. A massive flood hits a city X. The AI then not only analyzes the infrastructure, but also recognizes that a local social center plays a crucial role in the evacuation. The city administration plans its traditional and official rescue measures, but the AI suggests that the city administration work with the social center first to speed up the mobilization.

Example 2: Optimizing medical aid through social network mapping. A virus epidemic is spreading rapidly and state hospitals are already overloaded. However, the AI recognizes that informal networks of doctors and pharmacist groups could provide medication much faster than large institutions. Instead of waiting for centralized hospital capacities, the AI coordinates decentralized care via local medical contacts (community networking for disaster prevention).

Example 3: A simulation of future crisis scenarios with the influence of social networks. In other words, how would a city react if its social networks collapse? Which investments in social capital formation reduce long-term risks? Data-based simulations show where trust-building has the greatest influence on crisis management.

What if ... for example, the H5N1 bird flu epidemic could be played out using innovative AR- and AI-supported mechanisms for social capital formation. We analyze how cross-sector collaboration, crisis communication and long-term networks could optimize the handling of this pandemic. In the event of an H5N1 pandemic, the fire department, police, health authorities, food control and emergency and crisis management must be coordinated in real time. Communication via traditional channels such as radio systems or meetings is simply too slow here - AR could provide a live coordination platform. Informal networks such as community leaders and health volunteers should be actively integrated into the AR simulation in order to accelerate social mobilization. A virtual command center for ALL teams - firefighters, police and doctors can see the current infection hotspots and strategic supply lines in AR. Live mapping of case numbers and infection areas - Drone data and mobile sensors are used to simulate the course of the epidemic. Intensive coordination of local helpers via augmented reality, i.e. incident commanders could delegate tasks marked directly via AR to the relevant community networks. AR shows the fire department, police and health authorities the current movement patterns of people (H5N1 outbreak in a city district) in real time, as rapid quarantine measures are now necessary and emergency services must give direct instructions to local helpers to ensure evacuation and food supplies. The simulation of multipliers for public communication in AR is crucial here in order to control possible disinformation and also to optimize trust building, because many people ignore warnings about a pandemic if they do not come from trustworthy multipliers. AR could provide training on how official bodies can work sensibly with local opinion leaders to prevent panic and disseminate targeted health information. Miscommunication can lead to mass panic, medicine shortages or even resistance to quarantine - AI/AR could prepare for this.

What else should you pay attention to if possible? Biometric feedback for reliable decision-making under pressure. In a pandemic, emergency services are exposed to high levels of stress - making the wrong decisions under pressure could cost lives. AR could use real-time biometric feedback to show how emergency services work under pressure and where improvements are needed. Firefighters, police and doctors should not only rely on technical solutions, but also actively regulate their mental and physical stress, i.e. AR mechanisms for stress analysis in emergency services. Real-time cortisol measurements for stress reactions in terms of when our emergency services experience overload and how they could be mentally stabilized. Training modules for breathing techniques and concentration under pressure - AR simulates stressful situations and then shows how mental strategies could be used.

Many other facets need to be considered and taken into account: Pollutants and pesticides, sensory disorders, orthostatic hypotension, long Covid, ME/CFS and MCS, muscle changes and impairments, Myopia, Noise sensitivity, Anxiety and depression and important tactile support, Checklists that do not cover the needs of vulnerable groups (they have completely different preparation requirements), PGx, Deep social divisions and social capital, Consideration of emergency provisions and food, Music in shelters, Visual tactile support and much more

As of May 2025, many of these components are still missing.

While DI performs complex scenario simulations and analyzes the real-time data, AR could make the information even more visually tangible and help responders make (even better) strategic decisions immediately. This approach could combine medical research, real-time data analysis and advanced simulation technology in an optimized way. In 2025, we need holistic, dynamic and life-saving solutions

Please strive to integrate Decision Intelligence (DI) and Augmented Reality especially for vulnerable populations.

People with chronic illnesses are particularly at risk during disasters, as they are dependent on regular medical care, stable infrastructure and functioning power grids. The combination of Decision Intelligence (DI) and Augmented Reality (AR) could help to precisely manage these risks and save lives.

Many people with Long Covid, MCS (Multiple Chemical Sensitivity), ME/CFS (Chronic Fatigue Syndrome), orthostatic hypertension or loss of sense of smell are particularly vulnerable in emergency situations. Current emergency plans hardly take into account the specific challenges of these illnesses, which can lead to those affected not receiving adequate care in a crisis.

Why these diseases urgently need to be considered

Long Covid & ME/CFS - sufferers often have (very) low resilience and need access to specialist medical resources.

MCS (Multiple Chemical Sensitivity) - evacuation locations must be low in chemicals, otherwise the symptoms can worsen drastically and have serious consequences.
Orthostatic hypertension - people with circulatory problems need safe transportation and access to stable medical care.
Loss of sense of smell - This can lead to those affected not being sufficiently aware of dangers such as gas leaks or smoke development.
The vulnerable group of mothers and infants is often overlooked, but they represent an enormous and infinitely valuable resource for the resilience of our communities. Not only are they (massively) in need of protection - they also actively contribute to stability in times of crisis.

Should you be asking yourself why?

In my eyes, mothers are also natural crisis managers. They often organize care, provide security and also care within their families and communities. Their expertise should definitely be incorporated into emergency plans. Mothers are often (very) strongly networked, i.e. in their local communities, online and through many informal support systems (parent initiatives, midwife networks). These networks could be used as natural multipliers for crisis communication. The health and protection of infants should always be a high priority, because. Infants are particularly vulnerable and their care is a central component of any functioning emergency strategy. Inadequate consideration leads to avoidable deaths. And I am also convinced that communities with strong social capital around mothers recover much faster after disasters because they are better able to rely on mutual aid. However, this also means that (some of) the emergency responders would need to be specially trained to actively support the particular needs of this vulnerable population group, such as special protection concepts for breastfeeding care, safe places to sleep, hygienic conditions, sufficient (baby) food and medical care as well as psychological support.

A few thoughts on this, because chronic diseases must be integrated into disaster and emergency planning!

Main problems with disasters for chronically ill patients

Overloaded hospitals - emergency rooms could quickly become overcrowded and medication deliveries could be delayed.
Blocked roads - rescue transports for dialysis patients, insulin supplies or other medical emergencies could be hindered.
Power failures - Vital devices such as ventilators or infusion pumps depend on reliable power.
Interrupted supply of medication - pharmacies could then be unavailable or supply chains could be/become interrupted.
People with respiratory diseases are at high risk, e.g. in the event of a major fire in an industrial plant (DI could provide the elevated benzene levels here). Possible further long-term health effects due to chemical exposure and strategies for reducing pollutants and preventing long-term effects!
Recognize heat and weather-related risks for the chronically ill (DI could forecast extreme weather conditions and their impact on vulnerable groups)
The optimization of emergency medical care and personalized emergency strategies for chronically ill patients require state-of-the-art real-time data analysis for precise risk assessment - “outdated” statistical models no longer help here. Adaptive crisis management is required, i.e. sophisticated simulations for realistic operations.

Proactive planning saves lives - The analytical power of DI with the interactive visualization of AR

A combination of AI-driven decision-making systems and augmented reality could massively improve the care of the chronically ill during disasters. Real-time data analysis, visual support for emergency services and automated care strategies could create a completely new form of emergency aid.

1. Analyze real-time hospital capacities

• DI can calculate the real-time overload of clinics and emergency centers and suggest alternative medical points of contact. Automatic detection of supply bottlenecks for medicines and medical care.
• AR could then show the emergency services which hospitals still have free capacity and direct patients to them. AR could also directly show emergency shelters and which areas are chemical-free or where special care stations have been set up.

2. Identify roadblocks and safe transportation routes - Individual evacuation strategies for vulnerable groups

• DI could use traffic data to calculate exactly where the bottlenecks occur and immediately suggest alternative routes. DI could also calculate barrier-free escape routes and create optimized transport options.
• AR could then visually show affected people and emergency services which routes are passable or where mobile emergency stations are available for them. AR could visually show affected people and emergency services which routes are safe and where medical assistance is available.

3. Power failure simulation and emergency power prioritization

• DI could predict which parts of the city will have power shortages so that mobile generators can be provided at an early stage.
• AR could then help emergency teams to precisely coordinate emergency power supplies to protect medical equipment.

4. Optimize drug availability

• Decision Intelligence (DI) could automatically identify supply bottlenecks and then strategically reroute deliveries - DI analyzes real-time data on medical stocks, logistics networks and demand forecasts. The addition of augmented reality (AR) makes this information much more visible and directly actionable.
• DI then monitors the drug stocks and also the supply chains - it recognizes when pharmacies and clinics have supply problems. DI could also calculate alternative delivery routes if roads are blocked and suggest new routes.
• AR then visualizes this data for the emergency services and also the patients - it shows directly on a map which pharmacies are still open or where emergency dispensaries are located.

5. Real-time analysis of environmental factors, i.e. also dynamic particulate matter and chemical analysis

Clean drinking water is essential in any crisis. But after natural disasters, industrial accidents or chemical leaks, dangerous pollutants can enter the water system. PFAS, heavy metals, pesticides or even bacterial contaminants are invisible but deadly threats - especially for people with weakened immune systems and chronic diseases such as ME/CFS, Long Covid, MCS and diabetes. Their immune systems are often weakened and their bodies are more sensitive to pollutants, chemicals or a lack of essential medicines. Decision intelligence (DI) and augmented reality (AR) could help to identify these risks at an early stage and take targeted measures.
• DI could determine whether air quality or chemical exposure is dangerous for MCS, ME/CFS and Long Covid sufferers. DI could then also detect local pollution and chemical concentrations in soil, water and air to predict long-term consequences for MCS, ME/CFS and Long Covid sufferers. Dynamic risk maps then show zones at risk. Chemical reactions are simulated in advance to minimize the release of hazardous substances.
• AR could then show the emergency services live where protective measures or alternative shelters are needed. Example: DI detects high levels of PFAS and formaldehyde contamination after a large industrial fire, for example, and AR then shows firefighters safe deployment routes for decontamination.

6. Prioritized emergency power supply for medical devices and real-time control of energy supply & power grids

• DI analyses which hospitals or households need vital / essential equipment and prioritizes emergency power sources. DI could detect unstable power grids in disaster areas and prioritizes critical infrastructure. An intelligent emergency power distribution system then ensures secure energy in hospitals and emergency shelters.
• AR could then show the emergency services directly which buildings urgently need a power supply. AR could also show the emergency teams which power lines have failed and how mobile generators need to be deployed. Example: DI analyzes that a hospital in a flooded area is severely affected by a power outage and AR shows the firefighters the quickest route to the emergency power supply.

7. Chemical hazardous substances + Real-time health analysis + Preventive health care for emergency personnel from toxic exposure + Environmental monitoring & protective measures for MCS, ME/CFS and Long Covid sufferers

• DI could calculate the exposure risks for toxic substances such as benzene, formaldehyde, PFAS, PCB i.e. the detection of early symptoms of (possible) toxic exposure in the emergency services. DI could also calculate individual risk factors for firefighters and rescue workers based on their medical history, i.e. a combination of PGx data (pharmacogenetics) with real-time sensor data for an individual health strategy. DI should also measure particulate matter and chemical exposure in real time to identify critical zones for MCS, ME/CFS and Long Covid sufferers. Long-term monitoring of metabolic responses in emergency workers could detect exhaustion and toxic exposure at an early stage.
• AR then shows firefighters live data on air quality, while DI provides risk forecasts and AR then visualizes warnings in the event of increased exposure to particulate matter, smoke or chemicals so that safe retreats can be created. AR could also show firefighters their individual detox capability live so that they can take appropriate protective measures, i.e. DI recognizes that a firefighter has a reduced detox capability for e.g. benzene due to genetic GST deletions. AR provides a visual representation of contaminant exposure so that emergency managers can better understand the effects.
• Example: A firefighter considers taking off his mask. DI calculates the air quality and AR then visually warns him of dangerous pollutants, or DI detects excessive exposure to pollutants in a firefighter based on his genetic detox capabilities and AR directly displays the protective measures recommended for him, or DI detects high pesticide exposure in an evacuation zone and AR then helps to designate alternative locations for MCS, ME/CFS and Long Covid sufferers.

8. Evacuation planning for people at risk - Digital safety zones for evacuees & people in need of assistance

• DI analyzes in real time which groups of people need to be prioritized for evacuation, e.g. people with physical disabilities and chronic illnesses, senior citizens, families with small children and DI registers safe shelters and monitors capacities in real time. Intelligent matching systems for emergency assistance to efficiently care for people at risk.
• AI-controlled emergency communication then optimizes the instructions for different people affected.
• AR shows emergency services which buildings and areas still contain people with special needs and AR could show police and rescue teams directly on a map which locations are overcrowded and where there is still room.
• Example: DI then calculates safe routes for people with restricted mobility and AR visualizes these directly for the rescue teams.

9. Cybersecurity & digital threats during disasters

• DI detects and simulates cyber attacks on critical infrastructure, e.g. power grids, emergency call systems, water and gas plants.
• AI-controlled prevention prevents data loss or sabotage through digital attacks.
• AR then shows the emergency services live which systems have been compromised and how security measures can be optimized.
• Example: DI detects a cyberattack on the emergency communications network in real time and AR then provides visual alerts to help coordinate operations.

10. AI-supported real-time communication for emergency services

• DI analyzes all available communication channels and optimizes messages for rescue teams, including automatic language translation for international operations or multilingual regions.
• AR then displays live updates directly on the AR glasses or tablets of the fire and emergency services.
• Example: In a major disaster situation, DI can recognize that there are language barriers between emergency personnel and AR could then provide automated translations.

11. Real-time environmental monitoring & biological hazards

• DI monitors air pollution, chemical pollution and also biological risks such as the spread of viruses.
• AI then recognizes patterns of environmental toxins that could become dangerous at an early stage.
• AR then shows the emergency services live data on air quality so that they can implement their protective measures immediately.
• Example: A dangerous level of particulate matter is detected during the operation, i.e. DI then optimizes the evacuation strategies and AR displays dangerous zones directly.

12. AI-supported water and infrastructure monitoring after natural disasters

• DI predicts drinking water contamination after a flood or earthquake, i.e. real-time data on sewers, burst pipes and water pollution then helps to activate emergency measures more quickly.
• AR shows the emergency services in real time where the water pipes are damaged and which areas are under high pressure.
• Example: DI detects the pollutant levels in the drinking water of a disaster area and AR visualizes the affected regions for the rescue teams.

13. AI-supported risk analysis for buildings and infrastructure

• DI recognizes structural risks in real time, e.g. earthquake damage, fire propagation or even dangerous building materials. The simulations help to calculate the risk of collapse at an early stage.
• AR then provides the emergency services with visual indications of which buildings are safe to enter and which evacuation measures are required.
• Example: DI analyzes that a high-rise building has become unstable and AR then shows firefighters alternative rescue routes.

14. Stress management & resilience strategies for emergency services

• DI could analyze cortisol levels and heart rates of firefighters and other emergency personnel in real time, i.e. individual stress profiles through AI-supported health analyses.
• AR could then immediately indicate if, for example, a firefighter is suffering from overload.
• Example: DI uses biological sensors to detect a critical stress level, e.g. in a firefighter, and AR then visualizes the optimal measures.

15. Lithium-ion batteries & heat exposure in fire applications

• DI could identify which buildings are particularly prone to thermal “runaway” i.e. potential collapse zones and link this to weather data integration for wind directions, smoke and fire spread.
• AR then shows the emergency services the optimum extinguishing method for battery fires.
• Example: DI predicts increasing heat development in an area with lithium batteries and AR issues live warnings to the fire department.

16. Actively integrate the social capital of mothers (with infants and young children) into emergency strategies from the outset

Current emergency plans often ignore the many informal but extremely effective and valuable social networks of mothers. However, these networks are crucial for evacuation, care and also crisis management because they are already based on deep trust structures and form a first “line of defense” in times of crisis.
DI could analyze which informal networks of mothers are particularly active in a region and immediately identify these as key actors, i.e. also analyze which communities have a high number of families with young children and provide resources there in a targeted manner. DI could also predict their mobilization capacities in times of crisis. Real-time social media and communication data makes it possible to identify the most active networks and use them specifically as mediators in emergencies. Pattern recognition can show which groups are particularly well networked in order to enable prioritized assistance. By dynamically adjusting supply routes, it can ensure that critical goods arrive exactly where they are most urgently needed. The DI model could conduct long-term analyses of social networks to find out which communities are particularly resilient and it could simultaneously provide recommendations for reconstruction by analyzing which social structures need to be strengthened to better cope with future crises. i.e. automated risk analyses in the sense of social instabilities
I am also thinking here of AI-supported prediction of the effects of crises on mothers and babies. Previous disasters can already be used to analyze which supply bottlenecks have occurred most frequently and suggest appropriate countermeasures. Dynamic simulations could be used to identify weak points before a disaster occurs or socio-economic data could be used to identify regions with a particularly high need for support at an early stage. DI could automatically identify the right contacts in local communities, for example, and assess their willingness to cooperate (digital markers would then enable the emergency services to find the right contacts in the affected population directly). DI could also help to mark the emergency shelters for families with children in advance so that they can be specifically targeted.
Example: An algorithmic forecasting model could calculate weeks before an upcoming heatwave where breastfeeding rooms can be set up for mothers and their babies to ensure optimal care.
AR: Virtual disaster scenarios enable mothers and volunteers to learn how to optimize their role in safe training environments. AR-supported emergency scenarios could then be used to test how well mother-child networks can really be mobilized in the event of a crisis. In disaster situations, many mothers do not have direct and timely access to (their) doctors or medical staff and AR could help to provide life-saving information, e.g. real-time instructions for first aid, breastfeeding and nutritional support, virtual doctors via AR holograms/AR-supported telemedicine app and also hazard detection for infants. After a disaster, many mothers and children suffer from anxiety and stress. AR can help to create a more stable environment through special visual and auditory support. AR could be used to project calming scenarios that reduce stress (e.g. forest or seascapes). I.e. also interactive calming programs for infants (visual stimuli to calm infants and improve sleep). Virtual therapists could appear as consultants via AR and offer simple breathing exercises for stress management. AR games could help children to process the traumatic experience quickly and focus on something positive for a short time.

Disaster scenarios + DI-supported risk analysis

DI calculates the real-time risk profiles for modern threats such as lithium-ion batteries, new building materials or zoonotic risks such as H5N1 and creates a forecast of critical bottlenecks: Which areas are particularly susceptible to fires or health risks? AR then visualizes dangerous areas directly for emergency services.

Another example: DI recognizes that pharmacies in a flooded area are no longer receiving insulin deliveries. DI then calculates alternative distribution routes by accessing stocks in neighboring regions. AR then shows the affected patients which pharmacies are still open or where mobile emergency stations have been set up and the emergency services receive real-time data to distribute the supplies in a targeted manner.

Resource management & crowd control with DI + AR

DI could calculate in real time how crowds of people move under stress, for example - ideal for evacuations and panic situations. Dynamic traffic flows could be simulated, i.e. DI then shows where bottlenecks occur and AR indicates this panic zone to the emergency services and redirects those affected with safe routes. Strategic placement of rescue teams based on real-time data.

The analytical power of DI with the interactive visualization of AR - together they create an emergency system that not only reacts, but actively minimizes risks.

Together, DI and AR could better manage care crises and would be an optimal combination of data-driven decision support and interactive visualization and together they could significantly improve emergency management.

People with chronic illnesses must not be overlooked in emergency planning! Decision intelligence could ensure that specific medical needs are recognized and cared for in good time, while augmented reality makes this information tangible and directly usable.

As someone who likes to conceptualize new 360-degree strategies, I definitely recommend taking a look at the 4Cast Decision Intelligence platform and also getting in touch with social capital experts Tristan Claridge (Director, Institute for Social Capital) and Jeff Donaldson (Adjunct Professor and Entrepreneur in Emergency Management).