High-risk Areas Research Articles

Expensive Tastes and Living in High-Risk or Hazardous Areas: Claims to Compensation

In this paper, I defend a position contrary to a popular view of distributive justice. Residents of flood-prone or otherwise hazardous areas, like the Gulf South of the United States, receive substantial amounts of aid, paid through taxes on people living elsewhere in the US, after natural disasters that frequent the region. In popular discourse, some argue that we have reason not to (re)build in high-risk or hazardous areas, like the Gulf South. Instead, these residents, and others in similarly situated regions, should “relocate.” If residents choose to stay, then the government and taxpayers do not owe them financial compensation to rebuild. Against that view, I argue that egalitarians commit to compensating many such people. However, I propose broadening our understanding of ‘compensation’ and going forward, focus on agency-enhancing efforts as compensation instead of solely relying on standard post-disaster financial compensation, as current policies generally do. Later in the paper, I offer a metric and tiered system that proposes alternatives to the standard compensation and claim that agency-enhancing compensation aligns with the egalitarian’s commitment to compensate individuals following a natural disaster. I conclude that a tiered system and a broader compensation approach provides egalitarians with more options than the current standard compensation allows.

Monitoring livestock pregnancy loss.

Systematically tracking and analysing reproductive loss in livestock helps with efforts to safeguard the health and productivity of food animals by identifying causes and high-risk areas.

Borrelia burgdorferi

Since its recent discovery in the late 1970s, Lyme Disease (LD) has been a growing public health concern, especially in the United States where it accounts for the majority of vector-borne infections each year. The causative agent, Borrelia burgdorferi, is transmitted to humans through the bite of an infected Ixodes tick. This pathogen uses many unique mechanisms to both shield itself from the host immune response and cause disease. Clinically, LD presents in successive phases, with each increasing in severity as the bacterial cells migrate to new tissues and organ systems. On the epidemiological and ecological fronts, limitations in reporting, ecological changes, and a lack of public support hinder accurate surveillance and enhance the spread of the disease. The goal of this literature review is to increase public knowledge of B. burgdorferi, its vector, and the disease it causes, along with suggesting preventative measures to protect individuals who reside in high-risk areas. A collective and coordinated public health effort represents our greatest chance of restraining the LD-causing pathogen.

Risk Assessment of Spodoptera exempta against Food Security: Estimating the Potential Global Overlapping Areas of Wheat, Maize, and Rice under Climate Change

Spodoptera exempta, known as the black armyworm, has been extensively documented as an invasive agricultural pest prevalent across various crop planting regions globally. However, the potential geographical distribution and the threat it poses to host crops of remains unknown at present. Therefore, we used an optimized MaxEnt model based on 841 occurrence records and 19 bioclimatic variables to predict the potential suitable areas of S. exempta under current and future climatic conditions, and the overlap with wheat, rice, and maize planting areas was assessed. The optimized model was highly reliable in predicting potential suitable areas for this pest. The results showed that high-risk distribution areas for S. exempta were mainly in developing countries, including Latin America, central South America, central Africa, and southern Asia. Moreover, for the three major global food crops, S. exempta posed the greatest risk to maize planting areas (510.78 × 104 km2), followed by rice and wheat planting areas. Under future climate scenarios, global warming will limit the distribution of S. exempta. Overall, S. exempta had the strongest effect on global maize production areas and the least on global wheat planting areas. Our study offers a scientific basis for global prevention of S. exempta and protection of agricultural crops.

Constraints on Mechanical Fuel Reduction Treatments in United States Forest Service Wildfire Crisis Strategy Priority Landscapes

Abstract The USDA Forest Service recently launched a Wildfire Crisis Strategy outlining objectives to safeguard communities and other values at risk by substantially increasing the pace and scale of fuel reduction treatment. This analysis quantified layered operational constraints to mechanical fuel reduction treatments, including existing vegetation, protected areas, steep slopes, and administrative boundaries in twenty-one prioritized landscapes. Results suggest that achieving the objective to treat 20%–40% of high-risk area is unlikely in most landscapes under a business-as-usual approach to mechanical fuel reduction treatments. Increased investment in steep-slope systems and expanded road access opens sufficient acreage to meet treatment objectives in eighteen of twenty-one priority landscapes. Achieving treatment objectives in the remaining three landscapes will require both increased investment to overcome physical constraints and navigating administrative complexities within reserved land allocations to implement fuels treatments at the pace and scale needed to moderate fire risk to communities. Study Implications: Legal, operational, and administrative factors have hindered the implementation of proposed wildland fire risk reduction management actions. Investing in steep-slope systems, expanding use of temporary roads, and revising administrative rules to allow for appropriately tailored mechanical thinning in special conservation areas are possible ways to meet fuel reduction treatment objectives of the USDA Forest Service Wildfire Crisis Strategy in twenty-one landscapes across the western United States. Broadening the land base available for mechanical treatment allows for flexibility to develop treatment plans that optimize across the multiple dimensions of effective landscape-scale fuel treatment design and restore fire as a key ecosystem process.

Far-UVC (222 nm) irradiation effectively inactivates ssRNA, dsRNA, ssDNA, and dsDNA viruses as compared togermicidal UVC (254 nm).

Ultraviolet-C (UVC) irradiation is being used as an effective approach for the disinfection of pathogenic viruses present in air, surfaces, and water. Recently, far-UVC radiation (222 nm) emitted by KrCl* (krypton-chloride) excimer lamps have been recommended for disinfecting high-risk public spaces to reduce the presence and transmission of infectious viruses owing to limited human health exposure risks as compared to germicidal UVC (254 nm). In this study, the UVC inactivation performances of individual filtered KrCl* excimer lamp (222 nm) and germicidal UVC lamp (254 nm) were determined against four viruses, bacteriophages MS2, Phi6, M13, and T4, having different genome compositions (ssRNA, dsRNA, ssDNA and dsDNA, respectively) and shapes (i.e., spherical (Phi6), linear (M13), and icosahedral (MS2 and T4)). Here, the disinfection efficacies of filtered KrCl* excimer lamp (222 nm) and germicidal UVC lamp (254 nm) were evaluated for highly concentrated virus droplets that mimic the virus-laden droplets released from the infected person and deposited on surfaces as fomites. Filtered KrCl* excimer (222 nm) showed significantly better inactivation against all viruses having different genome compositions and structures compared to germicidal UVC (254 nm). The obtained sensitivity against the filtered KrCl* excimer (222 nm) was found to be in the order, T4 > M13 > Phi6 > MS2 whereas for the germicidal UVC (254 nm) it was T4 > M13 > MS2 > Phi6. These results provide a strong basis to promote the use of filtered KrCl* excimer lamps (222 nm) in disinfecting contagious viruses and to limit the associated disease spread in public places and other high-risk areas.

Mapping high probability area for the Bacillus anthracis occurrence in wildlife protected area, South Omo, Ethiopia

Anthrax is a zoonotic disease caused by a spore-forming gram-positive bacterium, Bacillus anthracis. Increased anthropogenic factors inside wildlife-protected areas may worsen the spillover of the disease at the interface. Consequently, environmental suitability prediction for B. anthracis spore survival to locate a high-risk area is urgent. Here, we identified a potentially suitable habitat and a high-risk area for appropriate control measures. Our result revealed that a relatively largest segment of Omo National Park, about 23.7% (1,218 square kilometers) of the total area; 36.6% (711 square kilometers) of Mago National Park, and 29.4% (489 square kilometers) of Tama wildlife Reserve predicted as a high-risk area for the anthrax occurrence in the current situation. Therefore, the findings of this study provide the priority area to focus on and allocate resources for effective surveillance, prevention, and control of anthrax before it causes devastating effects on wildlife.

Analysis Of Hybrid Solutions For Coastal Protection Combining Physical And Numerical Cfd Modeling

Historically, flooding and erosion challenges in coastal areas have been addressed through conventional gray/rigid structures such as breakwaters, dikes, and walls (Morris et al., 2017). As a consequence of the context of climate change, which is accompanied by a global biodiversity crisis and an increasing risk to coastal systems and communities, Nature-based Solutions (NbS) have emerged in recent years as an alternative to conventional engineering options. NbS can completely or partially mitigate coastal flooding and erosion problems while offering several additional co-benefits, such as carbon sequestration, improved water quality, habitat creation, and more (Sutton-Grier et al., 2015). Nevertheless, NbS may not be effective on their own in areas where there is insufficient available space for their development or in high-risk areas. In such cases, combining conventional engineering with nature-based solutions to obtain a so-called hybrid solution can represent an optimal approach, capable of providing the necessary risk reduction and realizing the benefits associated with natural solutions (Vuik et al., 2016). This makes hybrid solutions a highly attractive option that is currently gaining increasing interest. However, due to the limited number of real cases implemented, their relatively novel nature, and existing gaps in our knowledge about their hydrodynamic behavior, there is a pressing need to study hybrid solutions in greater detail. To this end, an experimental campaign is being conducted and is complemented by numerical CFD modeling, coupling IH2VOF and OpenFOAM models, to better understand the coastal protection services provided by the combined solution and to assess the suitability of the method of process superposition, commonly used to analyze the interaction between the flow and the hybrid solution. The main variables analyzed for this purpose are the evolution of wave height and wave run-up.

Spatial and temporal analysis of thyroid cancer incidence in Guilan Province, Northern Iran, 2009–2018

BackgroundThyroid cancer is one of the most common malignancies of the endocrine system, the fifth most common malignancy in women worldwide, and the second most common cancer in women over 50 in 2019. It is the sixth most common cancer in both sexes and the third most common cancer in women in Guilan province. This study was conducted to describe the geographic variation and investigate any changes in the trend of the thyroid cancer incidence rate. MethodsThis study was conducted on the data of the Guilan University of Medical Sciences cancer registration system. The crude and age-standardized incidence rate was calculated per 100,000 person-years. Joinpoint regression analysis evaluated the time trends and annual percent changes (APC). The incidence rate was estimated separately for each city and high-risk areas were shown on the province map using GIS software. Results1742 cases of thyroid cancer (83.7 % in women and 16.3 % in men) were registered in Guilan province from 2009 to 18. The incidence of thyroid cancer was 5.1-fold higher in women than men. The results of the joinpoint regression analysis showed that the age-standardized incidence rate of thyroid cancer in both sexes has increased significantly over ten years (APC: 26.4; 95 %CI: 22.5–30.4), (P-value < 0.001). In our study, Astaneh-ye Ashrafiyeh, Lahijan, and Langarud cities were identified as high-risk areas of the province for both sexes. ConclusionThe trend of incidence of thyroid cancer in Guilan province is increasing. Also, a wide geographical variation was found in the incidence of thyroid cancer.

Optimizing offshore wind export cable routing using GIS-based environmental heat maps

Abstract. In the United States, there are plans to produce up to 30 GW of offshore wind power by the year 2030, resulting in numerous seabed lease areas which are currently going through the leasing or construction and operations phase. A key challenge associated with offshore wind is optimal routing and installation of the subsea power cables, which transmit power from the main offshore wind energy production area to a land-based station, where it connects to the electrical grid. By traversing a vast extent of the seafloor, the installation and operational phases of subsea power cables have the potential to result in a range of environmental impacts, which may negatively affect sensitive biological, physical, human and/or cultural resource receptors. Presented here is a case study from southeastern North Carolina to identify optimal seabed cable routes and coastal landfalls for a recently leased offshore wind farm by using a combination of publicly available data, coupled with standard environmental impact assessment methodologies and geographic information system (GIS)-based heat maps. The study identified a range of high-risk areas, in addition to a number of potential low-risk routes and landfall areas which minimize seabed user conflicts and impacts on environmentally sensitive locations. Although additional high-resolution and site-specific environmental, geological and biological surveys are required to develop a robust cable installation plan, the preliminary steps from this research optimize early-phase marine spatial planning for offshore wind projects and other similar subsea industries.

Spatial analysis of cholangiocarcinoma in relation to diabetes mellitus and Opisthorchis viverrini infection in Northeast Thailand

Cholangiocarcinoma (CCA) exhibits a heightened incidence in regions with a high prevalence of Opisthorchis viverrini infection, with previous studies suggesting an association with diabetes mellitus (DM). Our study aimed to investigate the spatial distribution of CCA in relation to O. viverrini infection and DM within high-risk populations in Northeast Thailand. Participants from 20 provinces underwent CCA screening through the Cholangiocarcinoma Screening and Care Program between 2013 and 2019. Health questionnaires collected data on O. viverrini infection and DM, while ultrasonography confirmed CCA diagnoses through histopathology. Multiple zero-inflated Poisson regression, accounting for covariates like age and gender, assessed associations of O. viverrini infection and DM with CCA. Bayesian spatial analysis methods explored spatial relationships. Among 263,588 participants, O. viverrini infection, DM, and CCA prevalence were 32.37%, 8.22%, and 0.36%, respectively. The raw standardized morbidity ratios for CCA was notably elevated in the Northeast’s lower and upper regions. Coexistence of O. viverrini infection and DM correlated with CCA, particularly in males and those aged over 60 years, with a distribution along the Chi, Mun, and Songkhram Rivers. Our findings emphasize the association of the spatial distribution of O. viverrini infection and DM with high-risk CCA areas in Northeast Thailand. Thus, prioritizing CCA screening in regions with elevated O. viverrini infection and DM prevalence is recommended.

Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods

ABSTRACT Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

High-Risk Areas for Congenital Zika Syndrome in Rio de Janeiro: Spatial Cluster Detection

Brazil reported 18,282 suspected congenital Zika syndrome (CZS) cases up to 2018 and accounts for 61.4% of the total reported Zika cases in the Americas in the period. To detect high-risk areas for children with CZS in the city of Rio de Janeiro, we used cluster detection and thematic maps. We analyzed data using a Poisson model in Satscan 10.1.3 software. We also analyzed the records of children with CZS from 2015 to 2016 to describe the clinical and epidemiological maternal and child profile, as well as live births in 2016 and the social development index (SDI) by neighborhood. In 2015 and 2016, the incidence rates of CZS were 8.84 and 46.96 per 100,000 live births in the city, respectively. Severe congenital findings such as microcephaly and brain damage, osteoarticular impairment, ocular abnormalities, and hearing loss were observed in 47 children. The spatial distribution of CZS was concentrated in the north and west zones in heterogeneous neighborhoods. The neighborhoods with the highest occurrence of CZS cases were found to have the worst SDIs. Stascan detected three spatial clusters in the north zone, where the SDI is lower. The clusters presented high relative risks for CZS (7.86, 1.46, and 2.08), although they were not statistically significant. Our findings highlight a higher occurrence of CZS in areas with less favorable socioeconomic conditions.

Machine learning insights into HIV outbreak predictions in Sub-Saharan Africa

Predicting and preventing HIV outbreaks in Sub-Saharan Africa, a region disproportionately affected by the epidemic remains a significant challenge. This review explores the effectiveness and challenges of using machine learning (ML) for forecasting HIV spread in high-risk areas. ML models have shown promise in identifying patterns and trends in HIV data, enabling more accurate predictions and targeted interventions. ML insights into HIV outbreak predictions leverage various data sources, including demographic, epidemiological, and behavioural data. By analysing these data, ML algorithms can identify high-risk populations and geographical areas susceptible to HIV transmission. This information is crucial for public health authorities to allocate resources efficiently and implement preventive measures effectively. Despite the potential benefits, several challenges exist in using ML for HIV outbreak predictions. These include data quality issues, such as incomplete or inaccurate data, which can affect the reliability of predictions. Additionally, the complexity of HIV transmission dynamics and the need for real-time data pose challenges for ML models. To address these challenges, researchers and practitioners are exploring innovative approaches, such as integrating multiple data sources and using advanced ML techniques. Collaborations between researchers, public health officials, and technology experts are also crucial for developing robust ML models for HIV outbreak predictions. In conclusion, while ML offers valuable insights into HIV outbreak predictions in Sub-Saharan Africa, addressing challenges such as data quality and model complexity is essential for its effective use. By overcoming these challenges, ML has the potential to significantly improve HIV prevention efforts and ultimately reduce the burden of the epidemic in the region. Keywords:   Machine Learning, AI, HIV Outbreaks: Predictions, Insights.

Development and Efficacy of the Antivenom Specific to Severe Envenomations in Morocco and North Africa: Advancements in Scorpion Envenomation Management

Development and Efficacy of the Antivenom Specific to Severe Envenomations in Morocco and North Africa: Advancements in Scorpion Envenomation Management

Extreme rainfall and landslides as a response to human-induced climate change: a case study at Baixada Santista, Brazil, 2020

AbstractIn March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two large ensembles of the UK Met Office Hadley Centre HadGEM3-GA6 model that represented the event with and without the effects of anthropogenic climate change. Antecedent rainfall conditions on two different timescales are considered, namely extreme 60-day rainfall (Rx60day) which relates to the soil moisture conditions and extreme 3-day rainfall (Rx3day) which represents landslide triggering heavy rainfall. In the scenario including both natural and human-induced factors the antecedent 60 day rainfall became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20–42% of the total losses and damages. The greatest economic losses occurred in Guarujá (42%), followed by São Vicente (30%) and Santos (28%). Landslides were responsible for 47% of the homes damaged, 85% of the homes destroyed, all reported injuries, and 51% of the deaths associated with heavy rainfall. Changes in land cover and urbanization showed a pronounced increase in urbanized area in Guarujá (107%), São Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. In recent years, the region has experienced an increase in population growth and a rise in the proportion of irregular and/or precarious housing in high-risk areas. Guarujá has the highest number of such dwellings, accounting for 34.8%. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change.

Polygenic risk-stratified screening for nasopharyngeal carcinoma in high-risk endemic areas of China: a cost-effectiveness study

BackgroundNasopharyngeal carcinoma (NPC) has an extremely high incidence rate in Southern China, resulting in a severe disease burden for the local population. Current EBV serologic screening is limited by false positives, and there is opportunity to integrate polygenic risk scores for personalized screening which may enhance cost-effectiveness and resource utilization.MethodsA Markov model was developed based on epidemiological and genetic data specific to endemic areas of China, and further compared polygenic risk-stratified screening [subjects with a 10-year absolute risk (AR) greater than a threshold risk underwent EBV serological screening] to age-based screening (EBV serological screening for all subjects). For each initial screening age (30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, and 65–69 years), a modeled cohort of 100,000 participants was screened until age 69, and then followed until age 79.ResultsAmong subjects aged 30 to 54 years, polygenic risk-stratified screening strategies were more cost-effective than age-based screening strategies, and almost comprised the cost-effectiveness efficiency frontier. For men, screening strategies with a 1-year frequency and a 10-year absolute risk (AR) threshold of 0.7% or higher were cost-effective, with an incremental cost-effectiveness ratio (ICER) below the willingness to pay (¥203,810, twice the local per capita GDP). Specifically, the strategies with a 10-year AR threshold of 0.7% or 0.8% are the most cost-effective strategies, with an ICER ranging from ¥159,752 to ¥201,738 compared to lower-cost non-dominated strategies on the cost-effectiveness frontiers. The optimal strategies have a higher probability (29.4–35.8%) of being cost-effective compared to other strategies on the frontier. Additionally, they reduce the need for nasopharyngoscopies by 5.1–27.7% compared to optimal age-based strategies. Likewise, for women aged 30–54 years, the optimal strategy with a 0.3% threshold showed similar results. Among subjects aged 55 to 69 years, age-based screening strategies were more cost-effective for men, while no screening may be preferred for women.ConclusionOur economic evaluation found that the polygenic risk-stratified screening could improve the cost-effectiveness among individuals aged 30–54, providing valuable guidance for NPC prevention and control policies in endemic areas of China.

Flood risk evaluation of the coastal city by the EWM-TOPSIS and machine learning hybrid method

The frequent occurrence of floods and waterlogging has significantly impacted coastal cities. Effective mapping of flood risk can enhance the precision of disaster risk reduction strategies. However, there is room for improvement in fields such as flood inundation data, evaluation objectivity, and classification refinement. This study, using Xiamen as a case study, advanced flood inundation data accuracy by generating a frequency-based flood inundation map from multi-year remote sensing imagery. This study integrated the EWM-TOPSIS model with a neural network model to evaluate the flood risk. The EWM-TOPSIS model was employed to assess the flood vulnerability and flood exposure, while the multi-class neural network model to simulate the flood hazard. This combined approach reduced subjectivity in the flood risk assessment of Xiamen and achieved a finer level of risk classification compared to traditional binary neural networks. The results indicated that the flood vulnerable areas of Xiamen are concentrated along waterways distant from roads, with high flood exposure in Tong'an and Xiang'an districts. The flood-prone areas in Xiamen are primarily located along the coastal areas characterized by extensive impermeable surfaces. High flood risk areas are mainly distributed in Tong'an and Xiang'an subdistricts, particularly in Xiangping and Xindian. The method developed offers support for accurate flood risk identification and decision-making in cities with limited data resources.

Spatial Analysis for Natural Gas Pipeline Routing, Monitoring, and High-Risk Areas Identification

Spatial Analysis for Natural Gas Pipeline Routing, Monitoring, and High-Risk Areas Identification

Sonic Weapon for Women Safety

This paper introduces an IoT-integrated teaser gun designed specifically for enhancing women's safety. The device, incorporating an ESP32 module, integrates GPS tracking and IoT functionalities to provide real-time location monitoring and emergency response capabilities. Upon activation via a panic button, precise GPS coordinates are transmitted to a centralized database accessible through web and mobile applications, enabling rapid assistance in critical situations. Furthermore, the device employs preset location monitoring to identify and alert users and designated receivers of potential safety threats in high-risk areas. Audible alert mechanisms are integrated to promptly notify users of impending dangers, empowering women to prioritize their safety effectively. This innovative solution addresses the urgent need for proactive measures to combat gender-based violence, offering a comprehensive approach to ensuring women's safety in vulnerable situations.