Disaster Events Research Articles

Association Between Social Determinants of Health, COVID-19 Stressors, and Mental Health Among New York Residents Early in the Pandemic.

The COVID-19 pandemic is a disaster event. Exposure to stressors during and after disaster events is associated with negative mental health symptoms. To inform targeted COVID-19 recovery efforts, data are needed to understand which stressors play a key role in this relationship. Cross-sectional survey data (demographics, impacts of COVID-19, social determinants of health, depression, and anxiety) were collected online from adults living in New York state between May and June 2020. Differences in the proportion of stressors (COVID-19 and social determinants) experienced by race/ethnicity were assessed using chi-square analyses. Logistic regression was used to assess which factors were associated with increased odds of depression and anxiety. A majority (n = 258, 62.2%) of the 415 respondents reported being directly impacted by the pandemic. Non-white respondents reported a significantly larger proportion of stressors compared to white respondents. Under half of respondents reported depression (n = 171, 41.2%) and anxiety (n = 164, 39.5%). Healthcare and food concerns were associated with increased odds of depression and anxiety, and economic concerns were associated with increased odds of anxiety. Findings underscore the need to respond to the COVID-19 mental health crisis by addressing social determinants of health.

New Methodology for Assessing Seismic Ground Vulnerability Based on Microtremor and Taiwan Seismic Intensity Scale

ABSTRACT The Taiwan Ground Deformation Index (TGDI) is proposed to quantify site vulnerability using a non-linear soil deformation model, PGA parameters from the Taiwan seismic intensity scale, and microtremor analysis. Site vulnerability is graded into four levels: low (TGDI < 2.4), moderate (2.4 ≤ TGDI < 4.2), high (4.2 ≤ TGDI < 7.4), and very high (TGDI ≥ 7.4), based on susceptibility to seismic damage. Validating TGDI with 13 disaster events in Tainan during the 2016 Meinong earthquake showed 12 cases of moderate-to-high vulnerability. Since weak ground and strong amplification properties favor TGDI increases, this index serves as an early warning parameter for disasters.

A Comprehensive Review of Remote Sensing Technologies for Improved Geological Disaster Management

ABSTRACTOver the past two decades, the frequency of natural hazard incidents has steadily risen, leading to substantial human casualties, infrastructure destruction, societal and economic disruption. The occurrence of disasters, both of natural and human origin, has exhibited an upward trend in frequency over the past few decades on a global scale, posing a significant threat to diverse populations. Over time, remote sensing technologies have proven to be effective in analysing and monitoring diverse natural disasters, including but not limited to droughts, earthquakes, tsunamis, landslides and cyclones. The significant extent of its coverage capacity and the ability to repeat observations make its application economically efficient. This paper aims to elucidate the fundamental contributions and role of remote sensing in disaster management applications. In a comprehensive analysis, this study explores recent practical applications in the context of disaster events. The utilisation of diverse methodologies and functions of remote sensing in disaster monitoring and control is further expanded to encompass the domain of disaster risk management, employing cutting‐edge sensors and satellites from emerging technological advancements. This paper also addresses challenges related to disaster monitoring, detection and management. Emergencies, particularly during typical catastrophe scenarios, often witness partial disruptions in communication networks. Consequently, the role of alternative networks becomes paramount in enabling effective disaster detection and management strategies. Emerging issues are highlighted, and potential directions for future research are outlined and also support sustainable development goal 13 (climate action).

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

Spatial evaluation rainfall estimation on weather radar using Marshall–Palmer reflectivity–rainfall rate in Banten

Based on data from the National Disaster Management Agency (Ind.: Badan Nasional Penanggulangan Bencana – BNPB), throughout 2022, more than 91% of disaster events were hydrometeorological disasters, with floods at 43% and landslides at 17%. One of the factors for floods and landslides is high rainfall intensity. Automatic rain gauge (ARG) is a rainfall observation instrument that can accurately measure rainfall at observation points. However, it has problems such as communication systems that cause delays in data transmission, low instrument density, and inability to cover a wide spatial area, which can affect the accuracy of rainfall information. Weather radar is a remote sensing instrument that can estimate rainfall spatially so that weather radar observations can reach areas of the region that do not have ARG. However, before being used as rainfall information, estimation rainfall needs to be evaluated or calibrated. Evaluation of rainfall estimation on weather radar to ARG in Banten at a 30– 120 km distance range, shows a coefficient of determination above 0.8. Based on the studies that have been conducted, increase of root mean square error (RMSE) is due to influence of radar observation range and observation distance on ARG. Adjustment of rainfall estimation improves the accuracy of rainfall estimation. Adjusting rainfall estimation can reduce RMSE by 50%.

Research on Emotion about Media Memory of Wenxi Fire in Central Daily News

The author conducted a comprehensive study on the emotions reflected in The Central Daily News coverage of the Wenxi Fire, utilizing the digital analysis tool ROST CM6 in combination with historical research. The media memory of the Wenxi Fire in The Central Daily News reflects the emotional response and guidance of the National Government towards this disastrous event. The newspaper's spirited stance on the War of Resistance against Japan invigorated the public’s morale. Through selective reporting and, at times, the outright omission of facts, the newspaper initially mitigated the public’s criticism towards the central and Hunan provincial governments during the first phase, when the cause of the fire was still under investigation. In the second phase, after the cause was determined, the newspaper successfully shifted the blame for this governmental failure to the Japanese invaders, the Hunan provincial government, and the “righteously indignant” public. Through a series of carefully orchestrated actions, the newspaper managed to quell public anger, boost morale, and foster a stronger sense of national unity under the National Government, ultimately enhancing its image.

Analyzing Public Interest in Geohazards Using Google Trends Data

This study investigates public interest in geological disasters by analyzing Google Trends data from 2023. This research focuses on earthquakes, hurricanes, floods, tornadoes, and tsunamis to understand how search behaviors reflect public awareness and concern. This study identifies temporal and geographical patterns in search trends. Key findings reveal that public interest spikes during significant disaster events, such as the February 2023 earthquake in Turkey and Syria and the August 2023 hurricanes in the United States. This study highlights the importance of timely and accurate information dissemination for disaster preparedness and response. Google Trends proves to be a valuable tool for monitoring public interest, offering real-time insights that can enhance disaster management strategies and improve community resilience. This study’s insights are essential for policymakers, disaster management agencies, and educational efforts aimed at mitigating the impacts of natural disasters.

Impact of natural disasters on HIV risk behaviors, seroprevalence, and virological supression in a hyperendemic fishing village in Uganda.

Understanding the impact of natural disasters on the HIV epidemic in populations with high HIV burden is critical for the effective delivery of HIV control efforts. We assessed HIV risk behaviors, seroprevalence, and viral suppression in a high HIV prevalence Lake Victoria fishing community before and after COVID-19 emergence and lockdown and a severe lake flooding event, both of which occurred in 2020. We used data from the largest Lake Victoria fishing community in the Rakai Community Cohort Study, an open population-based HIV surveillance cohort in south-central Uganda. The data were collected both prior to (September-December 2018) and after (October-December 2021) COVID-19 emergence and a severe flooding event. Households impacted by flooding were identified via drone data and through consulting village community health workers. The entire study population was subject to extensive COVID-19-related lockdowns in the first half of 2020. Differences in HIV-related outcomes before and after COVID, and between residents of flooded and non-flooded households, were assessed using a difference-in-differences statistical modeling approach. A total of 1,226 people participated in the pre- and post-COVID surveys, of whom 506 (41%) were affected by flooding. HIV seroprevalence in the initial period was 37% in flooded and 36.8% in non-flooded households. After the COVID-19 pandemic and lockdown, we observed a decline in HIV-associated risk behaviors: transactional sex declined from 29.4% to 24.8% (p = 0.011), and inconsistent condom use with non-marital partners declined from 41.6% to 37% (p = 0.021). ART coverage increased from 91.6% to 97.2% (p<0.001). There was 17% decline in transactional sex (aPR = 0.83, 95% CI: 0.75-0.92) and 28% decline in the overall HIV risk score (aPR = 0.83, 95% CI: 0.75-0.92) among HIV-seronegative participants. We observed no statistically significant differences in changes of HIV risk behavior, seroprevalence, or viral suppression outcomes when comparing those affected by floods to those not affected by floods, in the periods before and after COVID-19, based on difference-in-differences analyses. Despite a high background burden of HIV, the COVID-19 pandemic, and severe flooding, we observed no adverse impact on HIV risk behaviors, seroprevalence, or virologic outcomes. This may be attributed to innovative HIV programming during the period and/or population resilience. Understanding exactly what HIV programs and personal or community-level strategies worked to maintain good public health outcomes despite extreme environmental and pandemic conditions may help improve HIV epidemic control during future natural disaster events.

Correlation of Stature and Hand Dimension Among Medical Students of South Tamil Nadu, India

Background &amp; Objectives Estimation of stature from the incomplete skeletal remains have importance in personal identification in the events of murders, accidents or natural disasters and considered as one of the biggest challenges of forensic science. Objectives: To study the relationship of stature in relation with hand dimensions and to derive a mathematical model to predict the stature from hand dimensions. Methods This cross-sectional study was done among 50 medical students studying at a medical college in South Tamilnadu. Age, gender, hand dimensions and height of all the subjects were noted. Pearson correlation was done to find the correlation between hand dimensions and height. Linear regression was used to derive the equations to predict the stature of an individual using various dimensions of hands. Results The mean age of the study population was 20.06 ± 1.28 years. Regression equations were derived to predict the stature of an individual with length and breadth of right and left hand. There was statistically significant positive correlation between stature and dimensions of both right and left hand. Linear regression equations were derived to predict the stature of an individual with any of the hand dimensions. Interpretation &amp; Conclusion Hand dimensions have statistically significant positive correlation with the stature. The length or breadth of the right or left hand can be used to predict the stature of an individual. Keywords: Breadth of hand, Height, Identification, Length of hand, Linear regression, Prediction, Stature.

Bracing for the Storm: A Case Study on Hurricane Preparedness in Higher Education Institutions

This case is for use in graduate courses in higher education and student affairs administration. It presents a multitude of challenges faced by higher education professionals in the event of a natural disaster. While intensive planning prior to the disaster can help mitigate some of the aftereffects, there will still be a number of decisions that must be made to ensure the safety and security of students, faculty, and staff. This case highlights the complex issues that administrators must often face as well as provides an opportunity to evaluate the complex issues from a variety of perspectives.

Improving community resilience to disrupted food access: Empirical spatio-temporal analysis of volunteer-based crowdsourced food delivery

Unplanned disaster events can greatly disrupt access to essential resources, with calamitous outcomes for already vulnerable households. This is particularly challenging when concurrent extreme events affect both the ability of households to travel and the functioning of traditional transportation networks that supply resources. This paper examines the use of volunteer-based crowdsourced food delivery as a community resilience tactic to improve food accessibility during overlapping disruptions with lasting effects, such as the COVID-19 pandemic and climate disasters. The study uses large-scale spatio-temporal data (n = 28,512) on crowdsourced food deliveries in Houston, TX, spanning from 2020 through 2022, merged with data on community demographics and significant disruptive events occurring in the two-year timespan. Three research lenses are applied to understand the effectiveness of crowdsourced food delivery programs for food access recovery: 1) geographic analysis illustrates hot spots of demand and impacts of disasters on requests for food assistance within the study area; 2) linear spatio-temporal modeling identifies a distinction between shelter-in-place emergencies and evacuation emergencies regarding demand for food assistance; 3) structural equation modeling identifies socially vulnerable identity clusters that impact requests for food assistance. The findings from the study suggest that volunteer-based crowdsourced food delivery adds to the resilience of food insecure communities, supporting its effectiveness in serving its intended populations. The paper contributes to the literature by illustrating how resilience is a function of time and space, and that similarly, there is value in a dynamic representation of community vulnerability. The results point to a new approach to resource recovery following disaster events by shifting the burden of transportation from resource-seekers and traditional transportation systems to home delivery by a crowdsourced volunteer network.

On delivery policies for a truck-and-drone tandem in disaster relief

This paper introduces the traveling salesman problem with a truck and a drone under incomplete information (TSP-DI). TSP-DI is motivated by the deliveries of emergency supplies under unknown road conditions in the immediate aftermath of a disastrous event. The urgency may force the immediate dispatch of relief vehicles, such that road damages blocking the truck’s planned route are detected ’on-the-fly’. The relief transport must schedule deliveries anticipating possible unplanned truck detours, enforce (planned) drone detours for early checking of key road segments, and consider the dynamic nature of road condition information. In this paper, we perform a competitive analysis of a widely used delivery policy for TSP-DI in practice – the online re-optimization policy (Reopt) – and compare it to several alternative delivery strategies. Competitive analysis examines the worst-case performance of the strategies and is particularly important in the context of disaster relief, where worst-case outcomes must be avoided. Our analysis shows that Reopt is dominated by alternative delivery policies in terms of the competitive ratio even at a medium level of damage on the road. It also underscores the importance of surveillance detours performed by the drone, even if the surveillance delays the start of the deliveries.

Machine learning-Based method for gas leakage source term estimation in highway tunnels

Incidents involving the leakage of flammable and explosive chemicals in highway tunnels can lead to disastrous events such as fires and explosions. The distinctive features of highway tunnels, characterized by their confined and elongated structure, intensify the complexity of managing such accidents, potentially resulting in significant casualties and property damage. Rapid detection and acquisition of leakage source information during a flammable gas leak in a highway tunnel are of utmost importance for effective accident management. This study introduces a predictive model developed using a machine learning approach. The model uses time series data from gas concentration sensors and anemometers installed within the tunnel to predict parameters such as gas leakage velocity, source location, and initiation time. A computational fluid dynamics simulation of gas leakage within the tunnel was constructed, and a dataset was generated using sample generation and preprocessing methods. The results demonstrate that this method exhibits robust predictive performance. Furthermore, a prediction strategy is proposed to enhance the model’s predictive accuracy and resilience to data noise. This model offers valuable insights for improving accident management strategies for gas-related incidents in highway tunnels.

Assessment of internal disaster and emergency preparedness in hospitals using the Hospital Safety Index tool

BackgroundWhile being vital infrastructure in circumstances of crisis, health and treatment facilities' equipment is at risk of destruction, and harm to healthcare structures may prevent medical services from being delivered. Enhancements could promote resilience, and assessing current facilities for internal disaster preparedness is crucial. An evaluation of three hospitals' internal emergency and disaster preparedness is presented in this paper. MethodsHospital safety and preparedness were assessed using the World Health Organization's (WHO) Second Edition of Hospital Safety Index (HSI) tool. The safety indices were determined by three modules: emergency and disaster management, non-structural, and structural. A safety rating, represented as a probability with a maximum value of one and a minimum value of zero, is determined by adding up the weighted results. Over three days, a multi-disciplinary team performed three hospital assessments through staff interviews and visual inspections. ResultsThe study hospitals' safety indices were 0.90, 0.60, and 0.73; two were classified as Classification A, meaning they will probably be able to function in emergencies and disasters, and one was classified as Classification B, meaning that short-term intervention measures would be required to ensure the hospital's proper operation in the event of an internal disaster. This study has identified areas that must be prepared for internal disasters to increase resilience. These areas include (a) infrastructure, which has weaker protection, access, and physical security due to relatively old buildings; (b) critical systems, which have emergency response committees established by hospitals and can be improved by hospital staff receiving emergency response training; and (c) non-structural issues, which include the need to improve fuel storage, cupboard safety, shelf contents, and fitting protection. ConclusionClassification A applies to hospitals 1 and 3, whereas Classification B applies to Hospital 2. The HSI tool is a user-friendly, all-inclusive instrument offered by a reputable organisation that should be frequently used to evaluate the internal preparation of different hospitals for disasters.

Expert perspectives on disaster risk reduction strategies in the tourist area of Borobudur-Yogyakarta-Prambanan in Indonesia

Tourism plays a crucial role in the economic growth of many countries. However, tourist destinations located in disaster-prone areas face increased vulnerability in the event of natural disasters. It is essential for these areas to have specific interventions, plans, and policies that focus on reducing disaster risk, while ensuring the safety of residents and tourists. This study proposes strategies for minimizing disaster risk using Indonesia's Borobudur-Yogyakarta-Prambanan (BYP) region as a case study. The study used an Analytical Hierarchial Process in a Strengths Weaknesses Opportunities and Threats (SWOT-AHP) analysis to prioritize factors through pairwise comparisons and develop strategic alternatives based on expert perspectives. Primary data were collected through aSWOT-AHP questionnaire that was provided to 22 experts in tourism and disaster management in Central Java and Yogyakarta Special Provinces. Secondary data were obtained from government documents, specifically the Disaster Risk Reduction Plan for the BYP regions (2023–2027). The results indicate that “implementing land-use policies to promote tourism development while considering disasters” is the most important strategy, based on expert judgment. The BYP tourist destinations provided a case study to identify strategies to reduce disaster risk in tourism areas.

A guide to the use of the Inter-University Bio-Backup Project (IBBP) for the sustainability of individual research, even in the event of natural disasters or other unexpected accidents

A guide to the use of the Inter-University Bio-Backup Project (IBBP) for the sustainability of individual research, even in the event of natural disasters or other unexpected accidents

Prosthesis applications and challenges in children with earthquake-related amputations

BackgroundAmputations are among the most important traumatic injuries caused by earthquakes. However, data on amputee children and prosthesis application is quite limited in the literature. The aim of the study is to evaluate the injury-related data, stump problems, prosthesis application, difficulties and complications experienced with prosthesis during follow-up of children with 2023 Kahramanmaraş earthquake-related limb loss. Patients and methodsSociodemographic and injury-related data, pre-amputation and post-amputation interventions, prosthesis application, current prosthetic problems, and revision surgeries of the amputee children were recorded. ResultsMedian age of patients (n = 102) admitted to our center was 13.0 years. 67.6 % of patients had one or more concomitant injuries. Median time and number of amputations were 4 (0–57) days and 1 (1–4), respectively. Of the total 120 amputations, 67.5 % (n = 81) were lower extremity amputations. Most common amputation levels were transtibial (29.1 %, n = 35), transfemoral (22.5 %, n = 27), and transhumeral (15.8 %, n = 19). Most amputees (56.8 %) underwent revision surgery after initial amputation. Median duration of time from amputation to prosthesis application was 184 (28–314) days. For 25 prostheses, a socket revision was required. Six patients had surgical revision of the stumps to allow prosthetic fit and mobility (due to bone overgrowth, soft tissue failure, heterotopic ossification). ConclusionLimited healthcare facilities, surgeries performed under emergency conditions, accompanying multiple traumas, inadequate follow-up conditions, and additional difficulties arising from the pediatric patient group lead to difficulties in the care of pediatric amputee patients. Our results will guide the care of this vulnerable patient population in the event of a similar unfortunate disaster.

Dynamic Characteristics of the Long Runout Rock‐ice Avalanche at High Altitude—A Case from the Zelongnong Basin, Eastern Himalayan Syntaxis, China

AbstractRock‐ice avalanches have frequently occurred in the Eastern Himalayan Syntaxis region due to climate change and active tectonic movements. These events commonly trigger catastrophic geohazard chains, including debris flows, river blockages, and floods. This study focuses on the Zelongnong Basin, analyzing the geomorphic and dynamic characteristics of high‐altitude disasters. The basin exhibits typical vertical zonation, with disaster sources initiating at elevations exceeding 4000 m and runout distances reaching up to 10 km. The disaster chain movement involves complex dynamic effects, including impact disintegration, soil‐rock mixture arching, dynamic erosion, and debris deposition, enhancing understanding of the flow behavior and dynamic characteristics of rock‐ice avalanches. The presence of ice significantly increases mobility due to lubrication and frictional melting. In the disaster event of September 10, 2020, the maximum flow velocity and thickness reached 40 m/s and 43 m, respectively. Furthermore, continuous deformation of the Zelongnong glacier moraine was observed, with maximum cumulative deformations of 44.68 m in the distance direction and 25.96 m in the azimuth direction from March 25, 2022, to August 25, 2022. In the future, the risk of rock‐ice avalanches in the Eastern Himalayan Syntaxis region will remain extremely high, necessitating a focus on early warning and risk mitigation strategies for such basin disasters.

Mental health of nursing students regarding the Moroccan earthquake: A cross-sectional study

ObjectiveIn addition to the economic and social impact, mental health issues are one of health needs during natural disaster such as earthquake. Thus, surveillance for mental health variables is crucial for public health planning. Nursing students as professional students may be particularly vulnerable to adverse mental health effects after an earthquake because of their potential role in emergency response and the challenges associated with providing medical care in crisis conditions. The aim of this research was to assess the mental health of students after six months of the Moroccan earthquake and associated factors. MethodA cross-sectional survey of student nurses was conducted on March 2024; six months after the Moroccan earthquake in front to 261 students using class sampling method. Three validated psychometric measures, Post-Traumatic Stress Disorder (PCL-C), Depression, Anxiety and Stress Scale (DASS-21), and The Connor-Davidson Resilience Scale (CD-RISC) were used to assess the nursing student mental health after Moroccan earthquake. ResultsAnxiety, Depression, Stress and abnormal post-traumatic stress rank was present respectively in 44.8%, 37.1%, 7.7%, 61.3% of participants. However, a minority of students obtained the high resilience score (13.8%). The results showed that the DAS scale correlated negatively with age (p = 0.034). Furthermore, the female gender, 3rd grade students (as final year for the nursing diploma) and those who had lost a family member in the natural disaster obtained higher medians with a significant difference in the DAS scale score (p < 0.05). The post-traumatic stress scale was only affected by material damage (p = 0.044). However, the high level of scores resilience obtained significantly for students living with their families. In contrast, no association between the mental health of nursing students and attending the phenomenon or living near earthquake foci. ConclusionThis study has demonstrated the crucial need to train healthcare providers in emotion management in the event of a natural disaster, and to launch a priori initiatives, particularly with regard to the mental health of students in health universities, in order to prepare them psychologically for any intervention in the case of an alert.

Exploring variations in flood events and casualties over Yamuna River basin, India

ABSTRACT The current study seeks to investigate the temporal and spatial distribution of flood events and casualties in the Yamuna River basin from 1978 to 2014. The data were compiled from the published reports on disastrous weather events and analyzed with simple quantitative methods (sums, averages, and percentages). The analysis revealed the occurrence of a total of 1886 flood events, which resulted in 5481 casualties (148 per year). The years 1995, 2003, 1993, and 1996 were observed as the most devastating years, contributing about 30 percent of the total flood events, while 1978, 1980, 1981, 1995, 2006, and 2013 were the deadliest years in terms of casualties (47 percent). The largest number of flood events and casualties were recorded during June–September months. Considering the event/casualty density and rates, the states of Himachal Pradesh and Uttarakhand experienced the highest event density (0.036 and 0.091) and casualty rates (2.1 and 1.9 persons). These findings will help in identifying and demarcating the worst flood-affected areas over the Yamuna River basin, which can subsequently help in reducing the flood risk and human casualties in these regions after initiating suitable flood mitigation measures.