Dataset Information

Understanding post-disaster population recovery patterns.

ABSTRACT: Despite the rising importance of enhancing community resilience to disasters, our understandings on when, how and why communities are able to recover from such extreme events are limited. Here, we study the macroscopic population recovery patterns in disaster affected regions, by observing human mobility trajectories of over 1.9 million mobile phone users across three countries before, during and after five major disasters. We find that, despite the diversity in socio-economic characteristics among the affected regions and the types of hazards, population recovery trends after significant displacement resemble similar patterns after all five disasters. Moreover, the heterogeneity in initial and long-term displacement rates across communities in the three countries were explained by a set of key common factors, including the community's median income level, population, housing damage rates and the connectedness to other cities. Such insights discovered from large-scale empirical data could assist policymaking in various disciplines for developing community resilience to disasters.

SUBMITTER: Yabe T

PROVIDER: S-EPMC7061695 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Understanding post-disaster population recovery patterns.

Yabe Takahiro T Tsubouchi Kota K Fujiwara Naoya N Sekimoto Yoshihide Y Ukkusuri Satish V SV

Journal of the Royal Society, Interface 20200219 163

Despite the rising importance of enhancing community resilience to disasters, our understandings on when, how and why communities are able to recover from such extreme events are limited. Here, we study the macroscopic population recovery patterns in disaster affected regions, by observing human mobility trajectories of over 1.9 million mobile phone users across three countries before, during and after five major disasters. We find that, despite the diversity in socio-economic characteristics am ...[more]

PMID: 32070218

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Project description:IntroductionDisasters have many forms, including those related to natural hazards and armed conflict. Human-induced global change, such as climate change, may alter hazard parameters of these disasters. These alterations can have serious consequences for vulnerable populations, which often experience post-disaster infectious disease outbreaks, leading to morbidity and mortality. The risks and drivers for these outbreaks and their ability to form cascades are somewhat contested. Despite evidence for post-disaster outbreaks, reviews quantifying them have been on short time scales, specific geographic areas or specific hazards. This review aims to fill this gap and gain a greater understanding of the risk factors involved in these contextual outbreaks on a global level.Methods and analysisUsing the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist and Khan's methodological framework, a systematic search strategy will be created and carried out in August 2020. The strategy will search MEDLINE, Embase and GlobalHealth electronic databases and reference lists of selected literature will also be screened. Eligible studies will include any retrospective cross-sectional, case-control or cohort studies investigating an infectious disease outbreak in a local disaster affected population. Studies will not be excluded based on geographic area or publication date. Excluded papers will include non-English studies, reviews, single case studies and research discussing general risk factors, international refugee camps, public health, mental health and other non-communicable diseases, pathogen genetics or economics. Following selection, data will be extracted into a data charting form, that will be reviewed by other members of the team. The data will then be analysed both numerically and narratively.Ethics and disseminationOnly secondary data will be used and there will be no public or patient involvement; therefore, no ethical approval is needed. Our findings will aim to be disseminated through a peer-reviewed journal. The authors intend to use the results to inform future mathematical modelling studies.

Dataset Information

Understanding post-disaster population recovery patterns.

Publications

Understanding post-disaster population recovery patterns.

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