ArticleNature communications2023
Exploring interactions between socioeconomic context and natural hazards on human population displacement.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Review
- Compounding hazards increase flood economic losses across Europe.Nature communications · 2026Article
- Impact of climate-induced human migration on dengue exposure risk in Africa.Research square · 2026Article
- Mortality risk during the COVID-19 pandemic is shaped by human development.BMC global and public health · 2026Article
- Interpretable machine learning for shoreline forecasting.Scientific reports · 2026Article
- Combined models of violent conflict and natural hazards improve predictions of household mobility in Bangladesh.Communications earth & environment · 2026Article
- Political development predicts reduced human cost of flooding.Nature communications · 2025Article
- Socioeconomic predictors of vulnerability to flood-induced displacement.Nature communications · 2025Article
- Global climate migration is a story of who and not just how many.Nature communications · 2025Article
- The Rise of Hazard Gentrification.International journal of disaster risk reduction : IJDRR · 2025Article
- Early warning of complex climate risk with integrated artificial intelligence.Nature communications · 2025Review
- Artificial intelligence for modeling and understanding extreme weather and climate events.Nature communications · 2025Review
- A framework for ageing and health vulnerabilities in a changing climate.Nature climate change · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Climate change is leading to more extreme weather hazards, forcing human populations to be displaced. We employ explainable machine learning techniques to model and understand internal displacement flows and patterns from observational data alone. For this purpose, a large, harmonized, global database of disaster-induced movements in the presence of floods, storms, and landslides during 2016-2021 is presented. We account for environmental, societal, and economic factors to predict the number of displaced persons per event in the affected regions. Here we show that displacements can be primarily attributed to the combination of poor household conditions and intense precipitation, as revealed through the interpretation of the trained models using both Shapley values and causality-based methods. We hence provide empirical evidence that differential or uneven vulnerability exists and provide a means for its quantification, which could help advance evidence-based mitigation and adaptation planning efforts.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.