High-risk Areas Research Articles

Deep Learning Approach for Suspicious Activity Detection from Surveillance Video

The present generation seeks ways to live their lives free of fear. This often prompts the need for enhanced law enforcement and security systems. Suspicion activity recognition in surveillance footage has become an essential component in modern security systems, focused around the problem of recognizing inappropriate behaviors without too much human time. The advent of deep learning has made an impact in computer vision tasks within such a way the real-time detection by the evaluation of large amounts of video footage becomes realistic. In this context the paper presents a model in which deep learning, particularly Convolutional Neural Networks (CNNs), are utilized for the behavioral pattern recognition from security footage. In addition, it also improves the efficacy of detection and reduces the shortcomings associated with these systems where only the post-event analysis is performed manually or automatically. In this paper, we demonstrate a novel IP model that analyzes live video and performs normal-suspicious–threat triage in real-time. Results indicate the validity of the method in different environmental and illumination conditions. An important objective that this system is designed for is to change the way security is practiced and delivered in today’s intelligent systems providing actionable intelligence and the ability to prevent threats rather than just respond to them making this a high growth opportunity for marketing high-risk and sensitive areas.

Timeliness of lung cancer care and area-level determinants in Victoria: A Bayesian spatio-temporal analysis.

It has been reported that the timeliness of lung cancer care varies significantly across different regions. According to the Victorian Lung Cancer Registry (VLCR) report, the timeliness of lung cancer care in Victoria has changed over time. Therefore, we aimed to quantify the extent of these spatial inequalities over time and to identify area-level determinants contributing to these changes. The study analysed lung cancer cases reported to the VLCR between 2011 and 2022. Bayesian spatio-temporal Conditional Autoregressive (CAR) models were fitted, incorporating spatial random effects, temporal random effects, as well as spatio-temporal interactions. The best-performing model was selected using the Deviance Information Criterion (DIC). For the final best-fit model, the adjusted Relative Risks (aRR) and their 95% Credible Interval (CrI) were reported. Over half (51.24%) of lung cancer patients experienced treatment delays, while approximately one-third (30.98%) encountered diagnostic delays. Moderate spatio-temporal variations were observed in both delayed diagnosis and treatment. In the final best-fit model for treatment delay, an increase in the percentage of smokers was significantly associated with a higher risk of treatment delay (RR = 2.13, 95% CrI: 1.13, 4.20). Identifying high-risk areas provides useful information for policymakers, helping in the reduction of delays in lung cancer diagnosis and treatment. This study has revealed spatio-temporal inequalities in diagnostic and treatment delays, providing valuable insights for identifying areas that should be prioritized to ensure timely care for lung cancer.

Dengue Outbreak Control and Prevention: A Case Study of Gampaha District, Sri Lanka

Dengue fever remains a persistent public health challenge in Gampaha District, Sri Lanka. Despite ongoing efforts by the Ministry of Healthcare, Nutrition & Indigenous Medicine, the district reported 10,212 cases in 2022, with significant hospital admissions and deaths in high-risk areas like MC Negombo, Gampaha, and Minuwangoda. The study examines Dengue control and prevention strategies implemented from November 2022 to March 2023. Data was collected from existing records, interviews with key informants, and field observations. Major challenges were identified and prioritised using evidence-based methodologies, including the "Five Whys" root cause analysis. The analysis revealed significant challenges categorised into program-related and non-program-related issues. Key issues include inadequate use of existing data, insufficient evidence-based control measures, and operational inefficiencies. The root cause analysis identified core problems such as lack of funding, poor planning, and limited community engagement. The study highlights the critical need for data-driven decision-making and enhanced collaborative efforts. Establishing a technical body, develop context-specific programs, and improve community involvement. Resource mobilisation for research and forecasting epidemics was also emphasised. This case study provides valuable insights into the challenges and strategic interventions necessary for effective Dengue control in the Gampaha District. Emphasising evidence-based decision-making and a comprehensive action plan underscores the importance of collaborative efforts to combat Dengue in the region.

Flood Risk Assessment and Its Implications on Critical Infrastructure Resilience in Nigeria

Flooding is one of the recurrent natural disasters in Nigeria that poses huge threats to not just people but also critical infrastructures. In 2024, the Federal Ministry of Water Resources and Sanitation and the Nigeria Hydrological Services Agency (NIHSA) identified 31 states including the FCT as high-risk areas for significant flood impacts. The impact of floods generally has caused devastating damage to critical infrastructures resulting from high rain intensities for a long duration. It becomes pertinent to evaluate the impact of floods on critical infrastructures like roads, bridges, residential buildings, water supply systems, and electricity facilities. Thus, this research seeks to explore the vulnerabilities and impacts of floods on critical infrastructure which is fundamental to economic growth, development, and coping mechanisms. A structured questionnaire was distributed among a sample of 90 including government department, construction industry, and community members to seek their views on the vulnerability risk of infrastructure and flood sources. Results indicate that most of the damages reported 42% were due to the destruction of roads and bridges, followed by residential houses at 31%. The main cause of flooding identified by 54% of respondents was poor drainage systems, while 31% cited poor waste management as a contributing issue. Hence, restoration of the drainage systems, implementation of prohibitions on encroachment of flood-prone areas and improvement of early warning systems are critical and pressing. The uniqueness of the study lies in its effort to address the gaps and challenges that exist in infrastructure resilience by identifying the most critical elements that are prone to risks and proposing mitigation measures against floods in the urban centers of Nigeria which are experiencing rapid growth.

Disaster preparedness: The role of spatial disaster learning using geospatial technology

Preparedness for natural disasters is crucial for people living in high-risk areas along the Ring of Fire, such as people in Indonesia. Although Spatial Disaster Learning using Geospatial Technology (SDL-GeoTech) has been developed, its effectiveness in enhancing students’ preparedness remains unproven. This study examines the impact of SDL-GeoTech on junior high school students’ readiness using a quasi-experimental, time-series, single-group pre-post-test design. The research involved students from three schools: SMA 1 Grati Pasuruan (flood-prone), SMA 1 Puncu Kediri (volcanic eruption-prone), and SMA 3 Mataram Lombok (earthquake-prone). Data were collected through tests on knowledge, skills, and attitudes, followed by t-test analysis at a 0.05 significance level. The results of this study show SDL-GeoTech was significantly able to improve students’ preparedness, including their knowledge, skills, and attitudes. These findings highlight SDL-GeoTech as an innovative tool for disaster education, with the potential to enhance curricula and teaching strategies, especially in vulnerable areas.Contribution: This research teaches students to use techno-geospatial learning through SDL-GeoTech, equipping those in the Ring of Fire region to be better prepared for potential natural disasters such as earthquakes and floods. The proven analysis of SDL-GeoTech has shown that it enhances students’ knowledge, skills and attitude in preparedness in dealing with disasters.

Malaria treatment for prevention: a modelling study of the impact of routine case management on malaria prevalence and burden

BackgroundTesting and treating symptomatic malaria cases is crucial for case management, but it may also prevent future illness by reducing mean infection duration. Measuring the impact of effective treatment on burden and transmission via field studies or routine surveillance systems is difficult and potentially unethical. This project uses mathematical modeling to explore how increasing treatment of symptomatic cases impacts malaria prevalence and incidence.MethodsLeveraging the OpenMalaria stochastic agent-based transmission model, we first simulated an array of transmission intensities with baseline effective treatment coverages of 28%, 44%, and 54% incorporated to reflect the 2023 coverage distribution across Africa, as estimated by the Malaria Atlas Project. We assessed the impact of increasing coverage to as high as 60%, the highest 2023 estimate on the continent. Subsequently, we performed simulations resembling the specific subnational endemicities of Kenya, Mozambique, and Benin, using the Malaria Atlas Project estimates of intervention coverages to reproduce historical subnational prevalence. We estimated the impact of increasing effective treatment coverage in these example settings in terms of prevalence reduction and clinical cases averted in children under 5 years old and the total population.ResultsThe most significant prevalence reduction – up to 50% – was observed in young children from lower transmission settings (prevalence below 0.2), alongside a 35% reduction in incidence, when increasing effective treatment from 28% to 60%. A nonlinear relationship between baseline transmission intensity and the impact of treatment was observed. Increasing effective treatment coverage to 60% reduced the risk in high-risk areas (prevalence in children under 5 years old > 0.3), affecting 39% of young children in Benin and 20% in Mozambique previously living in those areas. In Kenya where most of the population lives in areas with prevalence below 0.15, and case management is fairly high (53.9%), 0.39% of children were estimated to transition to lower-risk areas.ConclusionsImproving case management directly reduces the burden of illness, but these results suggest it also reduces transmission, especially for young children. With vector control interventions, enhancing case management can be an important tool for reducing transmission intensity over time.

Analysis of Flood Risk in the Lower Hydrographic Basin of the Río Negro (Argentina)

The recurrent floods in the lower hydrographic basin of the Río Negro (RN), documented since 1899, have caused significant damage to the resident population settled in flood-prone areas, whose socioeconomic condition is unfavorable. The aim of this study was to analyze the risk associated with flood occurrences in this area by integrating the flood susceptibility level (hazard) of the study area and the vulnerability of the population. The risk analysis was derived from the algebraic overlay of hazard and vulnerability maps, developed according to criteria published by Renda et al. (2017). In the study area, floods are primarily caused by intense rainfall and winds, upstream water discharges from the Limay and Neuquén rivers, and interactions between increased upstream flow from the lower hydrographic basin of the RN and extreme meteorological events known as Sudestadas. Every certain period, such as every 2 years for winds exceeding 75 km/h and precipitation exceeding 37 mm in 24 hours, as well as every 100 years for flows exceeding 4,000 , the occurrence of floods in the lower basin of the RN is expected to be derived from these events. Based on terrain characteristics and topography, 41.7% of the lower RN basin area showed moderate to high susceptibility to flooding. INDEC data (2010) indicated that 51.0% of the population in the study area was vulnerable to floods due to unfavorable socioeconomic conditions. According to hazard and vulnerability analyses, 43.2% of residents in the lower RN basin lived in high-risk areas, with homes located near the riverbank and on the outskirts of the Viedma-Carmen de Patagones urban conglomerate, hence being the most vulnerable. The results obtained in this research showed the influence of the natural and anthropogenic factors on the occurrence of flood-related disasters in the lower hydrographic basin of the RN. In addition, they served as a preliminary study that will enable decision-makers to create better prevention and mitigation plans contributing to improving the quality of life of its inhabitants.

A spatial autocorrelation analysis of road traffic accidents by severity using Moran’s I spatial statistics: a study from Nepal 2019–2022

BackgroundRoad traffic accidents (RTAs) are a significant global public health issue, leading to injuries, fatalities, and substantial economic losses. In Nepal, RTAs are a major concern, with notable regional variations in incidence and severity. This study analyzed the spatial distribution and types of road traffic injuries in Nepal from Fiscal Year (FY) 2019–2020 to 2021–2022, focusing on the impact of road types and geographic factors.MethodsThe study covers all 77 districts of Nepal, incorporating diverse geographic and climatic regions. Data on RTAs, including fatal and non-fatal injuries, were obtained from the Kathmandu Valley Traffic Police Office, Nepal. Spatial analysis was conducted using Quantum GIS (QGIS) and GeoDa software. Univariate and bivariate risk factor analyses were performed using Moran’s I statistics to detect spatial autocorrelation in RTA severity.ResultsThe overall fatality rate increased from 7.70 to 9.89 per 100,000 population from FY 2019–2020 to 2021–2022. However, spatial clustering of crashes showed a decline over the years. In FY 2019–2020 (Moran’s I; 0.276, p-value; 0.001), moderate clustering was observed, which weakened in the subsequent years (Moran’s I; 0.127, p-value; 0.002), with a near-random distribution by FY 2021–2022 (Moran’s I; -0.015, p-value; 0.457). The analysis of crash severity revealed significant variations across districts, with high fatality rates in remote areas like Mustang and Mugu, and low rates in districts such as Manang.Road Network AnalysisThe study examined the impact of different road types on RTA severity. Bitumen (BT) roads showed a negative correlation with RTA rates, while Earthen Roads (ER) were positively associated with higher RTA rates. Hot-spot and cold-spot clusters were identified for each road type, highlighting higher or lower RTA severity areas.ConclusionThis study provides valuable insights into the spatial patterns of RTAs in Nepal and the influence of road types on accident severity. The findings emphasize the need for targeted road safety interventions and infrastructure improvements, particularly in high-risk areas. By understanding spatial distributions and road network impacts, policymakers can better address road safety challenges and reduce the incidence of RTAs in Nepal.

Supporting prescriber progression: a group concept mapping study

Abstract Introduction Education reforms allow pharmacists to become prescribers earlier in their career and from 2026, new registrants will be independent prescribers.1 In NHS Scotland, an operational framework supports the development of pharmacist independent prescribing.2 We wanted to supplement the operational framework by identifying our core areas of prescribing to articulate prescribing progression and inform education and training provision. Aim To develop a core prescribing framework to support safe prescribing and a spiral approach to expanding scope of practice. Methods We conducted a participatory mixed-method approach using group concept mapping methodology.3 Phase 1 (brainstorming): all hospital pharmacists working in a patient-facing clinical role were invited to enter statements on a cloud-based platform (Padlet®) using the prompt ‘In my day-to-day practice I prescribe and/or make prescribing recommendations about the following medicines / conditions …’. The statements were triangulated with electronic inpatient pharmacist prescribing data over the previous three months. Phase 2 (sorting and rating): a project group comprised of pharmacists with different levels of practice and areas of clinical expertise, individually sorted the statements into groups based on the statements’ conceptual similarity and rated each statement by level of practice (post-registration foundation, advancing, advanced) and importance for including in a core framework (5-point Likert scale). R software applied multidimensional scaling to sorting data to create a similarity matrix, cluster map and Go-Zones. ChatGPT generated cluster titles. The project group discussed the results and following a few revisions, consensus was achieved on the content of the prescribing framework. This study was not considered research and was approved by the pharmacy quality improvement team in line with local governance process. Results Forty-three pharmacists participated in the brainstorming phase and generated 158 statements. Fourteen pharmacists participated in the sorting and rating phase. Multidimensional scaling analysis identified three clusters of prescribing activities: initiating, adjusting and deprescribing. The data identified activities involving initiating medicines and autonomous decision making rated at advancing/advanced level practice while adjusting/deprescribing medicines (often with the caveat of within guidelines, protocols or team decisions) was rated at post-registration foundation level. The prescribing framework includes the activities considered important and is organised by cluster and level of practice. Discussion / Conclusion This study provides a structured conceptualisation of prescribing across the levels of practice. The prescribing framework reflects the needs of our patients and hospital inpatient service and provides a scaffolded approach to progressing from inexperienced to more advanced prescribing roles. Post-registration foundation prescribing activities focus on processes or types of prescribing, whilst advanced are more autonomous and complex. Limitations we didn’t collect clinical area of practice as part of the demographic information for the brainstorming phase and some specialities may not be represented. Further work includes a training needs analysis and developing entrustable professional activities for core and higher risk areas of prescribing (e.g. post-operative pain).

Drought disaster risk assessment and yield insurance pricing of maize: a case study of Liaoning Province, China

Starting from the ‘four-factors’ theory of natural disaster risk formation, the study first selects indices that represent hazard, vulnerability, exposure, and emergency response and recovery capability to construct the drought risk assessment index system. Then the natural disaster risk index method is employed to evaluate the risk of maize drought disasters in Liaoning. Finally, the pure premium rate for maize yield insurance is determined based on the risk assessment results and the Copula function. The results show that: (1) The hazard, vulnerability, and exposure index in northwestern Liaoning are high, while emergency response and recovery capability is relatively low. Therefore, northwestern Liaoning is identified as a high-risk area for drought risk. (2) There exists a negative correlation between the drought risk index and maize yield in most areas of Liaoning; that is, higher drought risk is associated with lower maize yield. (3) Under the same coverage level, pure rates are higher in the northwestern and eastern Liaoning and relatively lower in the southwestern and southeastern Liaoning. Under different coverage levels, higher coverage corresponds to increased pure rates. The results can offer valuable guidance and benchmarks for relevant departments in Liaoning Province to assess the impact of drought risk on agriculture.

RVSV-ZEBOV vaccination in people with pre-existing immunity to Ebolavirus: an open-label safety and immunogenicity study in Guinean communities affected by Ebola virus disease (l’essai proches)

BackgroundZaire Ebolavirus disease (EVD) outbreaks can be controlled using rVSV-ZEBOV vaccination and other public health measures. People in high-risk areas may have pre-existing antibodies from asymptomatic Ebolavirus exposure that might affect response to rVSV-ZEBOV. Therefore, we assessed the impact pre-existing immunity had on post-vaccination IgG titre, virus neutralisation, and reactogenicity following vaccination.MethodsIn this prospective cohort study, 2115 consenting close contacts (“proches”) of EVD survivors were recruited. Proches were vaccinated with rVSV-ZEBOV and followed up for 28 days for safety and immunogenicity. Anti-GP IgG titre at baseline and day 28 was assessed by ELISA. Samples from a representative subset were evaluated using live virus neutralisation.ResultsTen percent were seropositive at baseline. At day 28, IgG in baseline seronegative (GMT 0.106 IU/ml, 95% CI: 0.100 to 0.113) and seropositive (GMT 0.237 IU/ml, 0.210 to 0.267) participants significantly increased from baseline (both p < 0.0001). There was strong correlation between antibody titres and virus neutralisation in day 28 samples (Spearman’s rho 0.75). Vaccinees with baseline IgG antibodies against Zaire Ebolavirus had similar safety profiles to those without detectable antibodies (63.6% vs 66.1% adults experienced any adverse event; 49.1% vs 60.9% in children), with almost all adverse events graded as mild. No serious adverse events were attributed to vaccination. No EVD survivors tested positive for Ebolavirus by RT-PCR.ConclusionsThese data add further evidence of rVSV-ZEBOV safety and immunogenicity, including in people with pre-existing antibodies from suspected natural ZEBOV infection whose state does not blunt rVSV-ZEBOV immune response. Pre-vaccination serological screening is not required.

Assessing the impact of socio-economic and environmental factors on COVID-19 spread in urban neighborhoods: evidence from Urmia, Iran

ABSTRACT The majority of the world’s population has been living in cities for the past two decades, making these areas particularly vulnerable to various stressors, including natural and man-made disasters. A growing literature suggests that socio-economic and environmental indicators significantly affect the epidemic. As a result, this research aims to investigate the effect of these indicators on the rate of infection with COVID-19 in the neighborhoods of Urmia City in Iran. This study uses Moran’s analysis, cluster and outlier analysis to identify high-risk and low-risk areas, as well as weighted spatial regression analysis. The results indicate that the number of employees, the number of elderly people, construction density, and road density have a significant relationship in predicting the epidemic. Based on the study of the effect of variables on the number of COVID-19 patients in Urmia City in Iran, the predicted values for the number of coronavirus patients indicate that the risk of contracting COVID-19 in the southern and part of the western areas is higher than in the northern and northeastern areas.

Innovative Perspectives on Ecological Assessment in the Agro-Pastoral Ecotone of Inner Mongolia: An Integrated Evaluation and Forecast of Landscape and Ecological Risks and Drivers

The agro-pastoral ecotone of Inner Mongolia, one of China’s most ecologically vulnerable regions, requires careful evaluation and prediction of landscape ecological risks to improve its environment and support sustainable development. Our study built a model to assess the landscape ecological risks from 1990 to 2020 using land use data from Google Earth Engine. We examined the changes in landscape ecological risks and their driving factors through spatial autocorrelation analysis and geographic detectors. Future ecological risks from 2025 to 2040 were predicted using the multi-criteria evaluation-cellular automata-Markov model. Results revealed a declining trend in both disturbance and loss intensity across land use types, with the overall ecological risk index also decreasing. Higher risk areas were concentrated in the east and southwest, while lower risks were observed in the north and center. Temperature and precipitation are key natural factors, while the impact of Gross Domestic Product (GDP), a human factor, on ecological risk is increasing and surpassed natural influences in 2015 and 2020. In the future, the highest risk areas will remain in the southwest and northeast. This study provides detailed evidence and guidance for ecological safety and sustainable development in the agro-pastoral ecotone of Inner Mongolia.

Terraced landscapes: risk and liability

Terraced landscapes are preserved elements of the human-influenced environment, maintained by ongoing agricultural activities. Farmers are subject to the periodic collapse of terraces, which are repaired by means of traditional techniques. Unless it can be proven that the collapse was accidental, the owner is liable for the damage caused by the collapse of the terraces or landslides. This topic, applied to Minori (Amalfi Coast, Italy), is interesting for two reasons. First, with the advancement of scientific knowledge, the unpredictability of events diminishes and the liability increases. Second, the intensive construction at the base of the terraces and the economic value of the buildings, enhanced by the landscape context, expose the owners to claims for damages from third parties that are disproportionate to the agricultural income, including claims for amnestied illegal constructions. The result has been an increase in the renunciation of property rights by farmers in areas of high hydro-geological risk. However, the State, which becomes the owner, denies the validity of property relinquishments made by owners solely to transfer risk, cost, and liability to the state treasury. This paper discusses these issues, going beyond the Italian case study, and proposes a different risk allocation related to territorial governance. This study reveals a discrepancy in government decision making and suggests that the division of risk into multiple components could be a solution to this inconsistency.

Sand and Dust Storm Risk Assessment in Arid Central Asia: Implications for the Environment, Society, and Agriculture

AbstractThis study aimed to assess sand and dust storm (SDS) risks in arid Central Asia during 2001–2021 from a multisectoral (environment, society, and agriculture) and comprehensive perspective on the Google Earth Engine (GEE) platform. The results show that the areas with moderate or greater SDS risk accounted for 18.75% of the total area of arid Central Asia. The high SDS risk areas are mainly concentrated in the oases around the desert and are most widely distributed in spring and summer. The SDS risk in the oasis area of southern Xinjiang increased significantly, while the SDS risk in the northeastern Aral Sea region and the Kazakh hilly region decreased significantly over the 21 years. Khwarazm of Uzbekistan, located in the Amu Darya River Delta, is the administrative district with the highest comprehensive risk of sandstorms, and the Balkan State of Turkmenistan and Kashi City and Zepu County in China are the administrative districts with the highest multisectoral risk of sandstorms. The results of this study provide a complete picture of SDS risks in the arid Central Asia region and will provide some guidance to policymakers and local authorities in SDS risk mitigation.

Identification of Aquatic Habitats of Anopheles Mosquito Using Time-series Analysis of Sentinel-1 data through Google Earth Engine

Abstract. Malaria, a severe disease transmitted by Anopheles mosquitoes, presents a substantial public health concern. Since Anopheles mosquitoes thrive in water-rich environments, accurately mapping surface water is essential for assessing malaria risk and managing mosquito populations. This study seeks to identify areas prone to water accumulation, which create habitats conducive to mosquito breeding. Initially, high-risk months were extracted using precipitation and temperature data. Subsequently, the Sentinel-1 Water Index (SWI) was utilized to analyse seven years of monthly Sentinel-1 time-series images via Google Earth Engine (GEE). To enhance our findings, we integrated monthly surface-water maps using a weighted majority voting strategy. Validation efforts included collecting 520 samples, half of which were water bodies identified through field observations and Google Earth Pro, with masks generated using the Segment Anything Model (SAM) algorithm. Object-based evaluation was employed, treating each water body as a distinct entity. The results revealed an overall accuracy of 96.1% and a kappa coefficient of 92.2% in water body detection, underscoring the method's effectiveness. This method, which outperformed other approaches in the domain and machine learning classifiers, is straightforward to implement, rapid, and does not require training data. Furthermore, while field monitoring may be challenging, the findings of this study could aid health authorities in identifying high-risk areas for disease control and prevention efforts.

Assessing the shielding capability of NiO-infused BPSCCO ceramics against low-energy X-rays

This research examines the improvement of radiation shielding capabilities in Bismuth Strontium Calcium Copper Oxide (BSCCO) superconductors via NiO doping. With the increasing need for advanced shielding materials in high-risk areas, exploring effective and nonhazardous alternatives is becoming increasingly important. The purpose of the study was to investigate the radiation-shielding characteristics of BPSCCO superconducting ceramics and the effects of NiO additives on their shielding properties. Comparisons of linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), half-value layer (HVL), tenth-value layer (TVL), mean free path (MFP), as well as fully simulated values for transmission factor (TF), neutron removal cross section (ΣR), electrical conductivity (Ceff), alpha and proton stopping powers, and radiation protection efficiency (RPE) using NIST XCOM, Phy-X, NIST Pstar and NIST Astar for all ceramic samples. The results indicate that optimal NiO doping notably enhances the radiation shielding capabilities of BSCCO ceramics, highlighting their significant potential for use in the medical, aerospace, and nuclear industries. This research provides a fundamental understanding of the potential of doped superconductor ceramics for radiation protection, promoting further research into these materials and the creation of safer, more efficient shielding solutions.

Modeling the Impact of Short-Term Displacement of Domestic Animals on the Transmission Dynamics of Brucellosis

Brucellosis, a prevalent zoonotic disease caused by bacteria from the genus Brucella, presents a substantial public health and economic challenges worldwide. In this paper, we formulate a two-patch deterministic model to investigate the impact of short-term displacement of domestic animals on the transmission dynamics of brucellosis within and between patches with heterogeneous risk of infection. The model analysis is performed, and the basic reproduction number (R0) is computed and used in the stability analysis of equilibria. A global sensitivity analysis is performed using Latin Hypercube Sampling (LHS) approach to check the impact of model parameters over a period of time. Numerical simulation results indicate that the increase in time spent by domestic animals in high-risk areas, such as communal grazing areas or wildlife interfaces, significantly increases the spread of infections. This underscores the importance of considering short-term animal movements to communal grazing areas and water points in brucellosis management strategies. It equips policymakers and stakeholders with actionable insights to combat the burden of the disease.

Community prevention and standardized clinical treatment jointly improve cancer outcome: Real-world evidence from an esophageal cancer patient cohort study

Extensive efforts have been put into reducing the heavy burden of esophageal squamous cell carcinoma (ESCC) in China. However, the joint impact of prevention and treatment on the long-term overall survival (OS) of ESCC patients remains largely unknown. We consecutively recruited 13,255 ESCC patients from two Chinese centers: the Northern center, located in a high-risk area with abundant screening programs; and the Southern center, situated in a non-high-risk area with improved clinical practices. Inter-center comparison, longitudinal intra-center comparison, and a simulation analysis were conducted to investigate the influence of tumor downstaging and high-quality clinical treatment on OS. During a follow-up period of 12.52 years, the Northern center exhibited higher median survival than the Southern center (6.22 vs. 3.15 years; HRadjusted = 0.73, 95% CI: 0.69–0.77). Mediation analysis demonstrated that its OS advantage was largely (77.7%) attributed to earlier TNM stage (stage 0–II: 51.3% vs. 24.6%). In temporal analyses, patient survival in the Southern center gradually improved (median survival during 2015–2018 vs. 2009–2014: 3.58 vs. 2.93 years; HRadjusted = 0.86, 95% CI: 0.79–0.94), coinciding with the progress of treatment-related indices (completeness of TNM staging in discharge diagnosis [from 53.7% to 99.6%], adoption of minimally invasive esophagectomy [from 0.0% to 51.1%] and right thoracic esophagectomy [from 12.4% to 86.4%], etc.). Simulation analysis further demonstrated that integrating both downstaging and high-quality treatment would lead to the best survival. Tumor downstaging and high-quality clinical treatment have a joint impact on ESCC patient survival. Establishing a comprehensive strategy that integrates cancer prevention with optimal clinical treatment is crucial for alleviating the ESCC burden.

Non-Attendance in Randomized Remote Mental Health Clinical Trial During the COVID-19 Pandemic

During the COVID-19 pandemic, a significant increase in psychiatric symptoms was found among essential service professionals (professionals or interns from the health and educational sector) who were exposed to high-risk contamination areas. These symptoms impair functionality, impacting quality of life, work, autonomy, and relationships. Recent data show that brief interventions are effective; however, between 20 and 30% of those individuals often do not attend their scheduled appointments. This study aimed to identify variables related to non-attendance for remote telepsychotherapy among participants with COVID-19-related emotional distress compared to those who initiated treatment. We included 2097 essential service professionals with PROMIS (Patient-Reported Outcomes Measurement Information System) T-scores ≥70 for anxiety, depression, or irritability who scheduled a remote mental health appointment. Participants completed clinical and sociodemographic data, mental health risk and protective factors, and PROMIS subscales. We conducted a binary logistic regression, using attendance vs. non-attendance as the dependent variable and these demographic and clinical characteristics as independent variables. Of the 2097 participants, 230 (11%) did not attend the first session despite three rescheduling attempts. Non-attendance was associated with the presence of irritability and seeking treatment during the second COVID-19 wave. In contrast, anxious symptoms, older age, and coping mechanisms that typically include intellectual and reading activities were associated with attendance. Our findings shed light on non-attendance patterns in mental health services, revealing that personal characteristics and environmental factors significantly contribute to missed appointments. Understanding these dynamics can improve patient engagement and adherence to mental health treatments, particularly in the context of telepsychotherapy during the COVID-19 pandemic.