A data-driven approach to rapidly estimate recovery potential to go beyond building damage after disasters

Following a disaster, crucial decisions about recovery resources often prioritize immediate damage, partly due to a lack of detailed information on who will struggle to recover in the long term. Here, we develop a data-driven approach to provide rapid estimates of non-recovery, or areas with the potential to fall behind during recovery, by relating surveyed data on recovery progress with data that would be readily available in most countries. We demonstrate this approach for one dimension of recovery—housing reconstruction—analyzing data collected five years after the 2015 Nepal earthquake to identify a range of ongoing social and environmental vulnerabilities related to non-recovery in Nepal. If such information were available in 2015, it would have exposed regional differences in recovery potential due to these vulnerabilities. More generally, moving beyond damage data by estimating non-recovery focuses attention on those most vulnerable sooner after a disaster to better support holistic and nuanced decisions.

Sabine Loos, David Lallemant, Feroz Khan, Jamie W. McCaughey, Robert Banick, Nama Budhathoki, Jack W. Baker