The flashes of images at the sound of STEM—Science, Technology, Engineering, and Mathematics—are a conglomeration of lab coats, conical flasks with luminous chemicals, computer screens and futuristic gadgets often come to mind. Yet, we rarely recall that in moments of crisis, STEM shifts from being an academic pursuit to a literal lifeline!

Disasters, whether natural or human-made strike us without warning. However, our ability to survive and recover is no accident. It’s the result of decades of cumulative STEM innovation. Each discipline of STEM work in unison to reduce loss of life and protect communities. Let’s break it down.

S – Science: Predicting Threats

Science forms the foundation for disaster preparedness. Meteorology, seismology, volcanology, hydrology and epidemiology: they each play critical roles in predicting events before they escalate. For example, in May 2023, the India Meteorological Department issued early cyclone warnings for Cyclone Mocha in the Bay of Bengal, allowing Bangladesh and Myanmar to evacuate over 750,000 people—a move credited with saving thousands of lives¹. These systems are called Early Warning Systems (EWS) that contains sensors and signal detectors of information-bearing patterns². Scientific monitoring of earthquakes via global seismographic networks enables agencies to detect tremors within seconds, triggering automated alerts in countries like Japan and Mexico. These seconds can mean the difference between life and death, giving people just enough time to move to safety³.

T – Technology: Speeding Response

Technology transforms raw scientific data into actionable responses. Disaster technology now extends beyond alerts into active intervention. AI-powered geospatial analytics, like those used by NASA’s Earth Observing System Data and Information System (EOSDIS), process satellite imagery in near-real time to detect flooded zones, wildfire spread, or landslide-prone slopes. The principle is computer vision, training AI to recognise disaster-affected areas in optical and radar images⁴.

During Hurricane Harvey (2017), NASA’s AI models identified submerged neighbourhoods even through cloud cover, helping relief agencies route boats to trapped residents⁵. In the 2023 Turkey–Syria earthquake, autonomous drones equipped with LiDAR and thermal imaging located survivors in under 12 minutes from deployment. This work would have taken hours for human teams⁶.

E – Engineering: Building for Resilience

Engineering doesn’t just respond to disasters – it prevents them from becoming catastrophes. It is now evolving from passive defences to adaptive, smart systems. Japan’s latest base isolation systems for skyscrapers use active dampers, which are sensors that detect seismic vibrations and hydraulic actuators counteract them in real time. The principle is negative feedback control—detect a disturbance, apply an equal and opposite force⁷. This was crucial during the 2011 Great East Japan Earthquake, when Tokyo’s skyscrapers swayed but did not collapse, preventing mass casualties⁸. In Europe, self-healing concrete is already deployed in the Maas River flood barriers in the Netherlands, where embedded bacteria activate in moisture to seal cracks, extending the defences’ lifespan and preventing catastrophic breaches during floods⁹.

M – Mathematics: Planning and Simulating

Mathematics is the silent force that turns scattered observations into clear strategies.

Mathematics drives the invisible decision-making engines in disaster management. Agent-based models (ABM) simulate how individuals and vehicles move during evacuations. The principle is bottom-up simulation where complex crowd behaviour emerges from many simple rules. During the 2018 Camp Fire in California, ABM simulations run by emergency planners were used to redesign evacuation routes in real time, preventing gridlock as residents fled¹⁰. In infectious disease crises, Bayesian statistical models have guided field deployments: during the 2023 Marburg virus outbreak in Equatorial Guinea, these models forecasted transmission zones with 92% accuracy, enabling medical teams to reach high-risk areas before outbreaks peaked.¹¹ Without mathematics, the rest of STEM operates blindly!

Disasters will always be part of human life, but their impacts don’t have to be catastrophic. Science warns us, technology alerts us, engineering shields us and mathematics guides us. These four pillars are not abstract academic fields – they are survival systems, constantly refined by past tragedies to prepare us for the next.

The next time a storm changes course, a building withstands a quake, or aid arrives in time, remember: STEM isn’t just about innovation; it’s about preservation. It is, quite literally, the difference between vulnerability and resilience in a world where every second counts.

References

1. Extremely Severe Cyclonic Storm Mocha, May 2023, Myanmar: Global Rapid Post-Disaster Damage Estimation (GRADE) Report. (2023). Available at: https://thedocs.worldbank.org/en/doc/d547c7dcb949a8b07aea2cc2e66a7bbc-0070062023/original/GRADE-CycloneMochaMay23Myanmar.pdf.
2. Mohapatra, M. and Sharma, M. (2025). Cyclone Warning System in India: A Journey of Success over 25 Years. Weather and Forecasting, [online] 40(6), pp.829–855. doi:https://doi.org/10.1175/waf-d-23-0188.1.
3. ‌ Cremen, G., Galasso, C. and Zuccolo, E. (2022). Investigating the potential effectiveness of earthquake early warning across Europe. Nature Communications, [online] 13(1). doi:https://doi.org/10.1038/s41467-021-27807-2.
4. ‌Giannakidou, S., Panagiotis Radoglou-Grammatikis, Lagkas, T., Vasileios Argyriou, Sotirios Goudos, Markakis, E.K. and Panagiotis Sarigiannidis (2024). Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey. Internet of Things, [online] 26, pp.101171–101171. doi:https://doi.org/10.1016/j.iot.2024.101171.
5. ‌ Muench, R., Jones, M., Herndon, K., Shultz, L., Bell, J., Anderson, E., Markert, K., Molthan, A., Adams, E., Cherrington, E., Flores, A., Layne, G., Lucey, R., Munroe, T., Pulla, S., Tondapu, G., Weigel, A (2017) 331652: Collaborative, Rapid Mapping of Water Extents During Hurricane Harvey Using Optical and Radar Satellite Sensors. (n.d.). Available at: https://ntrs.nasa.gov/api/citations/20170012235/downloads/20170012235.pdf [Accessed 12 Aug. 2025].
6. Selcen Ozturkcan (2023). Technology and Disaster Relief: The Türkiye-Syria Earthquake Case Study. IntechOpen eBooks. [online] doi:https://doi.org/10.5772/intechopen.111612.
7. Hechmi, M., Mahdi Abdeddaim, Elias, S. and Kahla, N.B. (2022). Review of Vibration Control Strategies of High-Rise Buildings. Sensors, [online] 22(21), pp.8581–8581. doi:https://doi.org/10.3390/s22218581.
8. Nohara, M. (2011). Impact of the Great East Japan Earthquake and tsunami on health, medical care and public health systems in Iwate Prefecture, Japan, 2011. Western Pacific surveillance response journal, [online] 2(4), pp.e1–e1. doi:https://doi.org/10.5365/wpsar.2011.2.4.002.
9. Patel, P. (2015). Helping Concrete Heal Itself. ACS Central Science, [online] 1(9), pp.470–472. doi:https://doi.org/10.1021/acscentsci.5b00376.
10. ‌Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, [online] 99(suppl_3), pp.7280–7287. doi:https://doi.org/10.1073/pnas.082080899.
11. Cuomo-Dannenburg, G., McCain, K., McCabe, R., Unwin, J.T., Doohan, P., Nash, R.K., Hicks, J.T., Charniga, K., Geismar, C., Lambert, B., Nikitin, D., Skarp, J., Wardle, J., Kont, M., Bhatia, S., Imai, N., Elsland, S. van, Cori, A., Morgenstern, C. and Morris, A. (2023). Marburg virus disease outbreaks, mathematical models, and disease parameters: a systematic review. The Lancet Infectious Diseases, [online] 24(5), pp.e307–e317. doi:https://doi.org/10.1016/s1473-3099(23)00515-7.

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