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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Disaster Medicine".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 16343

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Prof. Dr. Lili Yang

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Department of Statistics and Data Science, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, China
Interests: big data analysis; deep learning and LLM; smart cities and transportation
Special Issues, Collections and Topics in MDPI journals

Dr. Thomas Jack Huggins

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Guest Editor

Director of Psychology, School of Psychology, University of Monterrey, Monterrey 66238, NL, Mexico
Interests: disaster management; emergency management; mental health; technology adoption
Special Issues, Collections and Topics in MDPI journals

Dr. Zhejun Huang

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Guest Editor

Department of Mathematics, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen 518055, China
Interests: modelling; disaster risk analysis

Special Issue Information

Dear Colleagues,

This Special Issue of the International Journal of Environmental Research and Public Health (IJERPH) focuses on Disaster Prevention and Resilience. A range of disaster events have occurred frequently in recent years, and disaster impacts tend to be growing in line with social development, urban growth, population concentration, and wealth accumulation. Disasters triggered by natural hazards and human-instigated disasters continue to pose a major problem for societies around the world. Given the growing interest disaster prevention and resilience, we invite authors to submit original research works, methods papers, case investigations, and review papers that develop knowledge and capacity in strategic and practical aspects of reducing the impact of human-instigated disasters and disasters triggered by natural hazards.

Topics of interest include, but are not limited to:

Disasters triggered by natural hazards;
Human-instigated disasters;
Disaster risk analysis;
Disaster-risk-informed decision making;
Disaster risk reduction and management;
Disaster resilience of built environments;
Disaster monitoring and early warning;
Social analysis and urban planning;
Intelligent applications and education for disaster prevention and resilience.

Prof. Dr. Lili Yang
Dr. Thomas Jack Huggins
Dr. Zhejun Huang
Guest Editors

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Published Papers (7 papers)

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Research

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13 pages, 1418 KiB

Open AccessArticle

Predicting Unmet Healthcare Needs in Post-Disaster: A Machine Learning Approach

by Hyun Jin Han and Hae Sun Suh

Int. J. Environ. Res. Public Health 2023, 20(19), 6817; https://doi.org/10.3390/ijerph20196817 - 24 Sep 2023

Viewed by 1544

Abstract

Unmet healthcare needs in the aftermath of disasters can significantly impede recovery efforts and exacerbate health disparities among the affected communities. This study aims to assess and predict such needs, develop an accurate predictive model, and identify the key influencing factors. Data from the 2017 Long-term Survey on the Change of Life of Disaster Victims in South Korea were analyzed using machine learning techniques, including logistic regression, C5.0 tree-based model, and random forest. The features were selected based on Andersen’s health behavior model and disaster-related factors. Among 1659 participants, 31.5% experienced unmet healthcare needs after a disaster. The random forest algorithm exhibited the best performance in terms of precision, accuracy, Under the Receiver Operating Characteristic (AUC-ROC), and F-1 scores. Subjective health status, disaster-related diseases or injuries, and residential area have emerged as crucial factors predicting unmet healthcare needs. These findings emphasize the vulnerability of disaster-affected populations and highlight the value of machine learning in post-disaster management policies for decision-making. Full article

(This article belongs to the Special Issue Disaster Prevention and Resilience)

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22 pages, 14091 KiB

Open AccessArticle

A Digital Template for the Generic Multi-Risk (GenMR) Framework: A Virtual Natural Environment

by Arnaud Mignan

Int. J. Environ. Res. Public Health 2022, 19(23), 16097; https://doi.org/10.3390/ijerph192316097 - 1 Dec 2022

Cited by 1 | Viewed by 1693

Abstract

Extreme disasters, defined as low-probability–high-consequences events, are often due to cascading effects combined to amplifying environmental factors. While such a risk complexity is commonly addressed by the modeling of site-specific multi-risk scenarios, there exists no harmonized approach that considers the full space of possibilities, based on the general relationships between the environment and the perils that populate it. In this article, I define the concept of a digital template for multi-risk R&D and prototyping in the Generic Multi-Risk (GenMR) framework. This digital template consists of a virtual natural environment where different perils may occur. They are geological (earthquakes, landslides, volcanic eruptions), hydrological (river floods, storm surges), meteorological (windstorms, heavy rains), and extraterrestrial (asteroid impacts). Both geological and hydrological perils depend on the characteristics of the natural environment, here defined by two environmental layers: topography and soil. Environmental objects, which alter the layers, are also defined. They are here geomorphic structures linked to some peril source characteristics. Hazard intensity footprints are then generated for primary, secondary, and tertiary perils. The role of the natural environment on intensity footprints and event cascading is emphasized, one example being the generation of a “quake lake”. Future developments, à la SimCity, are finally discussed. Full article

(This article belongs to the Special Issue Disaster Prevention and Resilience)

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20 pages, 12070 KiB

Open AccessArticle

Gray Measure and Spatial Distribution Exploration of Local Emergency Resilience on Compound Disasters

by Feng Wu, Wanqiang Xu, Yue Tang, Yanwei Zhang and Chaoran Lin

Int. J. Environ. Res. Public Health 2022, 19(17), 11071; https://doi.org/10.3390/ijerph191711071 - 4 Sep 2022

Cited by 3 | Viewed by 1526

Abstract

The complexity and uncertainty of compound disasters highlight the significance of local emergency resilience. This paper puts forward a framework, including the Projection Pursuit Model based on Real-coded Accelerating Genetic Algorithm and the Moran’s Index (Moran’s I), to measure the local emergency resilience and analyze its spatial distribution. An empirical test is conducted with the case of Hubei Province, China. The results show that: (1) the measurement indices related to infrastructure, material reserves, and resource allocation have a larger weight, while those related to personnel and their practice have a smaller weight. (2) The measurement value of local emergency resilience of sub-provincial regions in Hubei Province is vital in the eastern and weak in the western, and there are apparent east-west segmentation and north-south aggregation characteristics. (3) Although the sub-provincial regions do not show significant spatial correlation, the eastern regions centered on Wuhan are negatively correlated, and the western regions are positively correlated. Furthermore, this study provides theories and methods for local emergency resilience evaluation and spatial correlation exploration, and it has specific guidance recommendations for optimizing local emergency management resource allocation and improving local emergency resilience. Full article

(This article belongs to the Special Issue Disaster Prevention and Resilience)

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22 pages, 7007 KiB

Open AccessArticle

Assessing the Dynamic Outcomes of Containment Strategies against COVID-19 under Different Public Health Governance Structures: A Comparison between Pakistan and Bangladesh

by Weiwei Zhang, Thomas Huggins, Wenwen Zheng, Shiyong Liu, Zhanwei Du, Hongli Zhu, Ahmad Raza and Ahmad Hussen Tareq

Int. J. Environ. Res. Public Health 2022, 19(15), 9239; https://doi.org/10.3390/ijerph19159239 - 28 Jul 2022

Cited by 1 | Viewed by 2157

Abstract

COVID-19 scenarios were run using an epidemiological mathematical model (system dynamics model) and counterfactual analysis to simulate the impacts of different control and containment measures on cumulative infections and deaths in Bangladesh and Pakistan. The simulations were based on national-level data concerning vaccination level, hospital capacity, and other factors, from the World Health Organization, the World Bank, and the Our World in Data web portal. These data were added to cumulative infections and death data from government agencies covering the period from 18 March 2020 to 28 February 2022. Baseline curves for Pakistan and Bangladesh were obtained using piecewise fitting with a consideration of different events against the reported data and allowing for less than 5% random errors in cumulative infections and deaths. The results indicate that Bangladesh could have achieved more reductions in each key outcome measure by shifting its initial lockdown at least five days backward, while Pakistan would have needed to extend its lockdown to achieve comparable improvements. Bangladesh’s second lockdown appears to have been better timed than Pakistan’s. There were potential benefits from starting the third lockdown two weeks earlier for Bangladesh and from combining this with the fourth lockdown or canceling the fourth lockdown altogether. Adding a two-week lockdown at the beginning of the upward slope of the second wave could have led to a more than 40 percent reduction in cumulative infections and a 35 percent reduction in cumulative deaths for both countries. However, Bangladesh’s reductions were more sensitive to the duration of the lockdown. Pakistan’s response was more constrained by medical resources, while Bangladesh’s outcomes were more sensitive to both vaccination timing and capacities. More benefits were lost when combining multiple scenarios for Bangladesh compared to the same combinations in Pakistan. Clearly, cumulative infections and deaths could have been highly impacted by adjusting the control and containment measures in both national settings. However, COVID-19 outcomes were more sensitive to adjustment interventions for the Bangladesh context. Disaggregated analyses, using a wider range of factors, may reveal several sub-national dynamics. Nonetheless, the current research demonstrates the relevance of lockdown timing adjustments and discrete adjustments to several other control and containment measures. Full article

(This article belongs to the Special Issue Disaster Prevention and Resilience)

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Review

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13 pages, 726 KiB

Open AccessReview

The Integration of Social Science for Community Engagement in the Humanitarian Fields of Conflicts and Disasters: A Scoping Review

by Luisa Toro-Alzate, Paola Maffi, Anu Puri, Rania Elessawi, Maria Falero Cusano, Jozefien Groenendijk and Daniel H. de Vries

Int. J. Environ. Res. Public Health 2023, 20(19), 6856; https://doi.org/10.3390/ijerph20196856 - 28 Sep 2023

Viewed by 1725

Abstract

Community engagement (CE) is essential to humanitarian assistance, and the social sciences have been credited in recent epidemics and disease outbreaks as having played a crucial, supportive role. Broadening this attention to other humanitarian fields, this scoping review asks what lessons learned can be found in grey and peer-reviewed literature on the integration of the social sciences in CE for conflicts and disasters. Using an analytical framework developed through a UNICEF-led project called Social Science for Community Engagement (SS4CE) in Humanitarian Action, we identified 1093 peer reviewed publications and 315 grey literature reports of possible relevance. The results show that only a small minority—18 publications and 4 reports—tangibly comment on the relevance of social sciences, mostly only in passing and implicitly. While social science techniques are used and the importance of understanding a community’s cultural, linguistic, and religious context is emphasized, further discussion on the integration of transdisciplinary and multidisciplinary social sciences is absent. Furthermore, CE is mostly seen as an instrumental (‘means to an end’) involvement, for example to collect data in emergency situations and receive feedback on interventions, but not as a critical and transformative intervention. We conclude that unlike the attention given to social sciences in disease outbreaks, there is a knowledge gap and an accordingly proper planning and implementation gap regarding the potentiality of social science to improve CE across all humanitarian contexts of disasters and conflicts. Full article

(This article belongs to the Special Issue Disaster Prevention and Resilience)

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28 pages, 2515 KiB

Open AccessReview

Remote Sensing Approaches for Meteorological Disaster Monitoring: Recent Achievements and New Challenges

by Peng Ye

Int. J. Environ. Res. Public Health 2022, 19(6), 3701; https://doi.org/10.3390/ijerph19063701 - 20 Mar 2022

Cited by 12 | Viewed by 4117

Abstract

Meteorological disaster monitoring is an important research direction in remote sensing technology in the field of meteorology, which can serve many meteorological disaster management tasks. The key issues in the remote sensing monitoring of meteorological disasters are monitoring task arrangement and organization, meteorological disaster information extraction, and multi-temporal disaster information change detection. To accurately represent the monitoring tasks, it is necessary to determine the timescale, perform sensor planning, and construct a representation model to monitor information. On this basis, the meteorological disaster information is extracted by remote sensing data-processing approaches. Furthermore, the multi-temporal meteorological disaster information is compared to detect the evolution of meteorological disasters. Due to the highly dynamic nature of meteorological disasters, the process characteristics of meteorological disasters monitoring have attracted more attention. Although many remote sensing approaches were successfully used for meteorological disaster monitoring, there are still gaps in process monitoring. In future, research on sensor planning, information representation models, multi-source data fusion, etc., will provide an important basis and direction to promote meteorological disaster process monitoring. The process monitoring strategy will further promote the discovery of correlations and impact mechanisms in the evolution of meteorological disasters. Full article

(This article belongs to the Special Issue Disaster Prevention and Resilience)

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Other

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6 pages, 580 KiB

Open AccessBrief Report

Building Resilience from the Grassroots: The Cyclone Preparedness Programme at 50

by Ahmadul Haque, Dilruba Haider, Muhammad Saidur Rahman, Laila Kabir and Raul Perez Lejano

Int. J. Environ. Res. Public Health 2022, 19(21), 14503; https://doi.org/10.3390/ijerph192114503 - 4 Nov 2022

Cited by 2 | Viewed by 1751

Abstract

As Bangladesh’s Cyclone Preparedness Programme (CPP) celebrates its 50th anniversary, we reflect on its legacy, the gains made, and progress that still lies ahead. The CPP is unique among disaster risk-management agencies, as more than 90% of its staff consists of community volunteers. This unique institutional design influences its functioning. An important part of its growth has included the expansion of the involvement and leadership of women in the CPP, so that today, women constitute about 50% of the volunteer corps. We reflect on the improvements made, since Cyclone Bhola of 1970 (the deadliest natural tragedy on record) and analyze institutional features of the CPP that other countries can learn from. Lastly, we reflect on remaining challenges for the years ahead. Full article

(This article belongs to the Special Issue Disaster Prevention and Resilience)

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