Rapid age and gender disaggregated exposure assessment for earthquake emergencies

In the first few hours of responding to natural hazards it is crucial to understand the size of the hazard event and the scale of the potential humanitarian emergency. This is important for the timely activation of appropriate aid and support mechanisms. For earthquakes the most reliable source of immediate scientific information is from the United States Geological Survey (USGS). Through their PAGER system the USGS provide rough estimates of potential fatalities and economic impact of major earthquake events (Jaiswal et al., 2010). However, these estimates lack spatial granularity in addition to age and gender disaggregation. We know that children, elderly and women are more prone to negative impacts in a disaster (e.g. Neumayer & Plümper 2007). Therefore, it is important to have a sense of these numbers as soon as possible to understand the potential scale of the emergency.

Additionally, a map of the potentially affected areas is important to understand the spatial distribution of the potential humanitarian need (e.g. isolated communities, road connectivity etc.). For example, following the 2015 Nepal earthquake the immediate acute needs of remote communities of western Nepal were initially overlooked. These communities faced severe isolation due to destroyed infrastructure, making aid delivery and access to basic supplies like food, water, and shelter challenging (The Asia Foundation, 2015). Mapping the potential exposed populations and their spatial distribution rapidly can help target appropriate emergency interventions sooner.

Here we present a tool developed by the British Geological Survey that automatically and in real-time extracts earthquake shaking information from the USGS and extracts statistics of the populations, disaggregated by age and gender, who will have been exposed to certain levels of shaking. We test how rapid exposures estimates, within 3 hours of an event, can capture the final losses in major earthquakes by using the 2023 Türkiye earthquakes as a case study.

We also demonstrate how we can estimate exposures and potentially compounding impacts of multiple hazards on populations following major earthquakes.