Optimal Intervention Strategies Research Articles

Relationship between autonomic and peripheral neuropathies and cardiovascular outcomes in diabetes

This review explores the complex relationship between diabetic neuropathy and cardiovascular disease (CVD). Neuropathy, a common complication of type 1 and type 2 diabetes, is divided into autonomic and peripheral types, each impacting cardiovascular health. Cardiovascular autonomic neuropathy, a form of autonomic neuropathy, is associated with various CVD complications, including arrhythmias, impaired nocturnal blood pressure regulation, and increased mortality. The prevalence of cardiovascular autonomic neuropathy varies depending on the type and duration of diabetes and is influenced by factors like glycemic control and metabolic stress. Peripheral polyneuropathy, which is often linked to diabetic foot disease, is also correlated with elevated CVD risk; research suggests shared pathophysiological mechanisms between peripheral neuropathy and cardiovascular conditions. Screening for neuropathies using tools like the Michigan Neuropathy Screening Instrument and heart rate variability analyses can facilitate early detection of CVD risk. Additionally, emerging technologies, like deep learning models, have demonstrated promise in detecting early cardiovascular patterns associated with autonomic neuropathy through electrocardiogram analysis. These findings underscore the value of integrating novel diagnostic approaches for early intervention. As CVD represents a leading cause of death among patients with diabetes, this article emphasizes the need for thorough assessment and proactive management of neuropathy to mitigate cardiovascular risk. The review recommends a multidisciplinary approach to diabetes care, including early screening, accurate risk stratification, and targeted therapeutic strategies to prevent or slow the progression of CVD in patients with autonomic and peripheral neuropathies. Further research is warranted to clarify the optimal intervention strategies for reducing CVD risk in these populations.

Determination of digital biomarkers of disease progression for digital phenotyping of patients with arterial hypertension.

Background: To compare the effectiveness of digital phenotyping of patients diagnosed with arterial hypertension with traditional monitoring methods over a three-year period. Patients and methods: The study was conducted from January 2021 to January 2024 among 800 patients diagnosed with arterial hypertension at 6 clinics in Moscow, Russia, evenly divided into experimental (identification of digital biomarkers of disease progression for digital phenotyping) and control (standard monitoring methods) groups. The intervention included lifestyle changes focused on increasing physical activity, improving sleep quality, reducing stress, and modifying diet. Significant improvements were observed in the experimental group compared to the control group. Systolic blood pressure decreased by 10 mmHg (p<0.001), pulse by 5 beats per minute (p<0.001), and stress level by 2 points (p<0.001) in the experimental group. Additionally, physical activity increased by 15 minutes per day (p<0.001), and sleep quality improved by 2 points on a scale from 1 to 10 (p<0.001). Results: Multiple regression analysis showed a decrease in the significance of digital biomarkers over the study period, indicating a positive response to the intervention. Conclusions: The obtained results emphasize the importance of comprehensive interventions in managing arterial hypertension and its related conditions. Implementing comprehensive lifestyle changes can lead to significant health improvements and serve as an effective preventive strategy. Further research is needed to explore optimal intervention strategies for promoting societal health.

Infant and young child feeding practices as mediators in the relationship between early temperament and children's eating behaviors: A longitudinal study during COVID-19 pandemic

Simultaneously investigating the influence of multiple early temperament dimensions on children's eating behaviors through infant and young child feeding practices may be essential for developing optimal intervention strategies. This longitudinal study gathered data at two assessment points: Time 1, evaluation of infant and young child feeding practices and children's temperament when they were between 6 and 12 months, and Time 2, assessment of children's eating behaviors at age 2. This study included sociodemographic characteristics, the Child Eating Behavior Questionnaire for Toddlers (CEBQ-T), the short form of Children Behavior Questionnaire (Revised IBQ-RSF), and the Infant Feeding Style Questionnaire (IFSQ) in eastern China. Structural equation modeling (SEM) was utilized to examine the pathways from three dimensions of early temperament to five types of children's eating behaviors mediated by three styles of infant and young child feeding practices. A total of 972 children, children's mean age was 14.58 ± 5.11 months, 464 (47.74%) being girls, while the mothers' mean age was 30.23 ± 3.56 years. Effortful control exhibited significant effects on food responsiveness and satiety responsiveness mediated by responsive feeding. Additionally, effortful control influenced enjoyment of food and satiety responsiveness mediated by restrictive feeding. Surgency had a significant effect on food responsiveness mediated by indulgent feeding. Negative affectivity impacted satiety responsiveness, enjoyment of food, and food responsiveness mediated by responsive feeding. This longitudinal study delineates the pathways from early temperament to eating behaviors mediated by infant and young child feeding practices among children aged 6–23 months. These findings highlight the need to prioritize intervention programs aimed at nurturing early temperament through appropriate infant and young child feeding practices to promote healthy eating behaviors for upper- and middle-income countries (UMICs) with similar contexts.

The role of dietary modification in the prevention and management of metabolic dysfunction-associated fatty liver disease: An international multidisciplinary expert consensus

Metabolic dysfunction-associated fatty liver disease (MAFLD) or metabolic dysfunction-associated steatotic liver disease (MASLD), has become the leading cause of chronic liver disease worldwide. Optimal dietary intervention strategies for MAFLD are not standardized. This study aimed to achieve consensus on prevention of MAFLD through dietary modification. A multidisciplinary panel of 55 international experts, including specialists in hepatology, gastroenterology, dietetics, endocrinology and other medical specialties from six continents collaborated in a Delphi-based consensus development process. The consensus statements covered aspects ranging from epidemiology to mechanisms, management, and dietary recommendations for MAFLD. The recommended dietary strategies emphasize adherence to a balanced diet with controlled energy intake and personalized nutritional interventions, such as calorie restriction, high-protein, or low-carbohydrate diets. Specific dietary advice encouraged increasing the consumption of whole grains, plant-based proteins, fish, seafood, low-fat or fat-free dairy products, liquid plant oils, and deeply colored fruits and vegetables. Concurrently, it advised reducing the intake of red and processed meats, saturated and trans fats, ultra-processed foods, added sugars, and alcohol. Additionally, maintaining the Mediterranean or DASH diet, minimizing sedentary behavior, and engaging in regular physical activity are recommended. These consensus statements lay the foundation for customized dietary guidelines and proposing avenues for further research on nutrition and MAFLD.

Deployment of Industry 4.0 into the Agricultural Food Industry: A Focus on Facet, Insight, Knowledge, and Resilience (FIKR) Personality Traits and AI-Powered Inventory Management

Integrating Artificial Intelligence (AI) in precision agriculture within the framework of Industry 4.0 (I4) is revolutionizing crop disease management and inventory management, offering innovative solutions that enhance both agricultural productivity and environmental sustainability. Combined with I4 technologies, AI-powered systems can predict, detect, and manage crop diseases accurately, reducing reliance on chemical pesticides and improving overall farm efficiency. AI algorithms identify disease patterns and suggest optimal intervention strategies by analyzing real-time data from drones, sensors, and satellite imagery. This approach minimizes crop loss, maximizes yield, and aligns with sustainable farming practices by reducing the environmental footprint. However, the success of these technologies is influenced by the personality traits of farmers. Traits such as openness to innovation, conscientiousness, and analytical thinking are crucial for the effective adoption and utilization of AI-driven solutions. Conscientious farmers follow precise instructions and maintain equipment, while those open to new experiences are more likely to experiment with innovative technologies. Analytical thinkers excel in interpreting complex data, and making informed decisions that improve crop health and yield. The research underscores the need for fostering these traits among farmers to maximize the benefits of AI technologies. Additionally, the study highlights the importance of interdisciplinary collaboration in developing and implementing AI-driven solutions that address both agricultural productivity and environmental sustainability. By integrating technological advancements with human factors, AI has the potential to transform crop disease management, contributing to a more sustainable and resilient agricultural system. The findings call for continued research, policy support, and a holistic approach to fully realize the benefits of AI in agriculture.

Effects of body weight support training on balance and walking function in stroke patients: a systematic review and meta-analysis.

To comprehensively and quantitatively evaluate the impact of body weight support training (BWST) on balance and gait function in stroke patients based on an evidence-based basis and to identify the most effective intervention strategies. PubMed, Web of Science, The Cochrane Library, CNKI, Wanfang, and Chinese SinoMed Database were searched until November 25, 2023. Quality assessment and meta-analysis were performed using RevMan 5.2 and Stata 14.0 software. A total of 31 randomized controlled trials involving 1,918 patients were included in the study. The meta-analysis demonstrated that body weight support training (BWST) significantly improved Berg Balance Scale (BBS) scores (MD = 3.60; 95% CI: 1.23 to 5.98; p = 0.003), gait speed (SMD = 0.77; 95% CI: 0.38 to 1.15; p < 0.0001), and step length (SMD = 0.46; 95% CI: 0.19 to 0.72; p = 0.0008) in stroke patients compared to conventional rehabilitation. For enhancing balance function, the most effective interventions were identified as a disease duration of 3-6 months (MD = 5.16; 95% CI: 0.76 to 9.57; p = 0.02), intervention time of 4-8 weeks (MD = 5.70; 95% CI: 2.90 to 8.50; p < 0.0001), a maximum body weight support level above 30% (MD = 3.80; 95% CI: 1.48 to 6.13; p = 0.001), and a maximum training walking speed of 0.2 m/s or more (MD = 4.66; 95% CI: 0.37 to 9.70; p = 0.03). For improving walking function, the optimal interventions were also a disease duration of 3-6 months (gait speed: SMD = 0.59; 95% CI: 0.15 to 1.03; p = 0.008; step length: SMD = 0.27; 95% CI: 0.06 to 0.56; p = 0.04), intervention time of 4-8 weeks (gait speed: SMD = 1.01; 95% CI: 0.44 to 1.59; p = 0.0006; step length: SMD = 0.83; 95% CI: 0.54 to 1.12; p < 0.00001), a maximum body weight support level above 30% (gait speed: SMD = 0.79; 95% CI: 0.36 to 1.22; p = 0.0003; step length: SMD = 0.79; 95% CI: 0.47 to 1.11; p < 0.00001), and a maximum training walking speed of 0.2 m/s or more (gait speed: SMD = 1.26; 95% CI: 0.62 to 1.90; p = 0.0001; step length: SMD = 0.85; 95% CI: 0.38 to 1.31; p = 0.0003). Compared with conventional rehabilitation training, BWST demonstrates superior efficacy in enhancing balance and walking function in stroke patients, with a consistent optimal intervention strategy. The most effective program includes a disease duration of 3-6 months, an intervention period of 4-8 weeks, a maximum body weight support of 30% or more, and a maximum training walking speed of 0.2 m/s or greater. http://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022358963.

When should lockdown be implemented? Devising cost-effective strategies for managing epidemics amid vaccine uncertainty.

During an infectious disease outbreak, public health policy makers are tasked with strategically implementing interventions whilst balancing competing objectives. To provide a quantitative framework that can be used to guide these decisions, it is helpful to devise a clear and specific objective function that can be evaluated to determine the optimal outbreak response. In this study, we have developed a mathematical modelling framework representing outbreaks of a novel emerging pathogen for which non-pharmaceutical interventions (NPIs) are imposed or removed based on thresholds for hospital occupancy. These thresholds are set at different levels to define four unique strategies for disease control. We illustrate that the optimal intervention strategy is contingent on the choice of objective function. Specifically, the optimal strategy depends on the extent to which policy makers prioritise reducing health costs due to infection over the costs associated with maintaining interventions. Motivated by the scenario early in the COVID-19 pandemic, we incorporate the development of a vaccine into our modelling framework and demonstrate that a policy maker's belief about when a vaccine will become available in future, and its eventual coverage (and/or effectiveness), affects the optimal strategy to adopt early in the outbreak. Furthermore, we show how uncertainty in these quantities can be accounted for when deciding which interventions to introduce. This research highlights the benefits of policy makers being explicit about the precise objectives of introducing interventions.

Aqueous Grown Quantum Dots with Robust Near-Infrared Fluorescence for Integrated Traumatic Brain Injury Diagnosis and Surgical Monitoring.

Surgical intervention is the most common first-line treatment for severe traumatic brain injuries (TBIs) associated with high intracranial pressure, while the complexity of these surgical procedures often results in complications. Surgeons often struggle to comprehensively evaluate the TBI status, making it difficult to select the optimal intervention strategy. Here, we introduce a fluorescence imaging-based technology that uses high-quality silver indium selenide-based quantum dots (QDs) for integrated TBI diagnosis and surgical guidance. These engineered, poly(ethylene glycol)-capped QDs emit in the near-infrared region, are resistant to phagocytosis, and importantly, are ultrastable after the epitaxial growth of an aluminum-doped zinc sulfide shell in the aqueous phase that renders the QDs resistant to long-term light irradiation and complex physiological environments. We found that intravenous injection of QDs enabled both the precise diagnosis of TBI in a mouse model and, more importantly, the comprehensive evaluation of the TBI status before, during, and after an operation to distinguish intracranial from superficial hemorrhages, provide real-time monitoring of the secondary hemorrhage, and guide the decision making on the evacuation of intracranial hematomas. This QD-based diagnostic and monitoring system could ultimately complement existing clinical tools for treating TBI, which may help surgeons improve patient outcomes and avoid unnecessary procedures.

Frontal pole-precuneus connectivity is associated with a discrepancy between self-rated and observer-rated depression severity in mood disorders: a resting-state functional magnetic resonance imaging study.

Discrepancies in self-rated and observer-rated depression severity may underlie the basis for biological heterogeneity in depressive disorders and be an important predictor of outcomes and indicators to optimize intervention strategies. However, the neural mechanisms underlying this discrepancy have been understudied. This study aimed to examine the brain networks that represent the neural basis of the discrepancy between self-rated and observer-rated depression severity using resting-state functional MRI. To examine the discrepancy between self-rated and observer-rated depression severity, self- and observer-ratings discrepancy (SOD) was defined, and the higher and lower SOD groups were selected from depressed patients as participants showing extreme deviation. Resting-state functional MRI analysis was performed to examine regions with significant differences in functional connectivity in the two groups. The results showed that, in the higher SOD group compared to the lower SOD group, there was increased functional connectivity between the frontal pole and precuneus, both of which are subregions of the default mode network that have been reported to be associated with ruminative and self-referential thinking. These results provide insight into the association of brain circuitry with discrepancies between self- and observer-rated depression severity and may lead to more treatment-oriented diagnostic reclassification in the future.

Safety and adherence to medications and self-care advice in oncology (SAMSON): pilot randomised controlled trial protocol

IntroductionWith the increasing use of oral anti-cancer medicines (OAMs), research demonstrating the magnitude of the medication non-adherence problem and its consequences on treatments’ efficacy and toxicity is drawing more attention....

The use of an imperfect vaccination and awareness campaign in the control of antibiotic resistant gonorrhoea infection: A mathematical modelling perspective

The multi drug-resistant Neisseria gonorrhoeae has been classified by the World Health Organisation (WHO) as a high-priority global public health problem. This underlines the need for better understanding of the transmission dynamics and proposing an optimal intervention strategy to control the disease. In this article, a deterministic mathematical model for the transmission dynamics of gonorrhoea as an antibiotic resistant disease in a population with an imperfect vaccination is proposed and analysed. The model incorporates the classes of vaccinated individuals and individuals equipped with self protection interventions to reduce antibiotic resistant cases. The threshold parameter R0, the basic reproduction number, for the analysis of the model is calculated. In the given setting, the model exhibits a backward bifurcation for R0<1. However, if the efficacy of the vaccine is 100% without a waning effect, the model is shown to be without a backward bifurcation and the disease-free equilibrium is globally asymptotically stable whenever R0<1. The global sensitivity analysis of the model to variations in parameter values is also performed to determine the most influential parameters on the disease transmission. Moreover, the optimal control analysis of the full model is presented and the optimal intervention strategies are proposed. The proposed intervention strategies are shown to be able to control the disease within a relatively shorter period of time. Finally, numerical experiments are presented to support the theoretical analysis of the model.

High intestinal carriage of Clostridium perfringens in healthy individuals and ICU patients in Hangzhou, China.

Clostridium perfringens is a bacterium of growing public health concern due to its ability to cause infections and its increasing resistance to antibiotics. Understanding its epidemiology in clinical settings is essential for intervention strategies. This study surveyed healthy individuals and ICU inpatients in a provincial hospital in China. It found a high prevalence of C. perfringens, indicating infection risk. The isolates also showed significant antibiotic resistance. Importantly, the study revealed diverse sequence types and phylogenetic variation, suggesting infection risk from intestinal colonization rather than clonal transmission in hospitals. This analysis emphasizes the need to optimize intervention strategies against this public health threat.

The dark triad, dating app use and online disinhibition positively predict technology-facilitated sexual violence perpetration

Technology-facilitated sexual violence (TFSV) describes the perpetration of a variety of sexual offences committed through digital communication technologies. The current study aims to investigate the similarity of risk factors between TFSV and in-person sexual offending. The sample consisted of 105 young adult, Australian males who responded to measures of sexual violence acceptance, dating app usage, online disinhibition, and the dark triad. Overall, 45.3% of the sample reported at least one instance of TFSV perpetration in the past 5 years. Standard and hierarchical linear regressions revealed dating app usage, the dark triad, and online disinhibition positively predicted TFSV perpetration. Neither measure of attitudes accepting of sexual violence predicted TFSV perpetration, but their positive correlation is indicative of cultural norms enabling sexual violence against women. As illuminated here, the congruence between TFSV and in-person violence extends beyond conceptual and prevalence trends to predictive risk factors. Recommendations for interventions and prevention include raising awareness of risk-generating app features that create vulnerability to sexual harm and identifying heterogenous groups of TFSV offenders based on key characteristics to develop optimal intervention strategies.

Effectiveness of Nonpharmacological Measures on Improving Headache Score, Strength, Pain, and Quality of Life in Cervicogenic Headaches: A Systematic Review.

Cervicogenic headache (CGH) is a common condition affecting a significant portion of the population and is effectively managed through various interventions, including nonpharmacological approaches. Physical therapy plays a crucial role in CGH management, with numerous studies supporting its effectiveness. This systematic review aimed to evaluate the effectiveness of specific nonpharmacological physical therapy interventions for CGH. A comprehensive search was conducted across various databases (PubMed, Medline, PEDro, and Cochrane Library) for randomized controlled trials (RCTs) published between January 2017 and January 2023 investigating the effectiveness of specific nonpharmacological physical therapy interventions for CGH. We employed manual searches to capture potentially missed studies. Independent reviewers screened all studies based on predefined eligibility criteria. Extracted data included methodology, specific interventions, outcome measures (headache score, strength, pain, and quality of life (QOL)), and study conclusions. Eight RCTs were identified as meeting all inclusion criteria and were thus included in the data synthesis. The findings from these trials revealed a diverse range of nonpharmacological physical therapy interventions, including but not limited to manual therapy, exercise therapy, and multimodal approaches. Specifically, the interventions demonstrated significant improvements in headache scores, strength, pain levels, and overall QOL among individuals with CGH. These results underscore the multifaceted benefits of physical therapy in managing CGH and highlight its potential as a comprehensive treatment option. This review identified eight relevant RCTs investigating nonpharmacological interventions for CGH. Despite the promising findings, this review acknowledges several limitations, including the limited sample size and the heterogeneity of interventions across studies. These limitations emphasize the necessity for further research to elucidate optimal intervention strategies and refine treatment protocols. Nevertheless, the comprehensive analysis presented herein reinforces the pivotal role of physical therapy in not only alleviating pain but also enhancing function and improving the QOL for individuals suffering from CGH.

Analytically Tractable Models for Decision Making under Present Bias

Time-inconsistency is a characteristic of human behavior in which people plan for long-term benefits but take actions that differ from the plan due to conflicts with short-term benefits. Such time-inconsistent behavior is believed to be caused by present bias, a tendency to overestimate immediate rewards and underestimate future rewards. It is essential in behavioral economics to investigate the relationship between present bias and time-inconsistency. In this paper, we propose a model for analyzing agent behavior with present bias in tasks to make progress toward a goal over a specific period. Unlike previous models, the state sequence of the agent can be described analytically in our model. Based on this property, we analyze three crucial problems related to agents under present bias: task abandonment, optimal goal setting, and optimal reward scheduling. Extensive analysis reveals how present bias affects the condition under which task abandonment occurs and optimal intervention strategies. Our findings are meaningful for preventing task abandonment and intervening through incentives in the real world.

Implementing Activity-Based Therapy for Spinal Cord Injury Rehabilitation in Canada: Challenges and Proposed Solutions.

Activity-based therapy (ABT) is a therapeutic approach with multiple benefits including promoting neurorecovery and reducing the likelihood of secondary complications in people living with spinal cord injury (SCI). Barriers and facilitators to ABT implementation for SCI rehabilitation have been studied from various perspectives through qualitative research. However, these viewpoints have not been synthesized to identify challenges of and strategies for implementing ABT across the Canadian healthcare system. Thus, the purpose of our study was to examine the current state of ABT in Canadian healthcare settings according to users' perspectives. Our main objectives were to compare barriers and facilitators to ABT implementation across Canadian healthcare settings according to users' perspectives and to identify optimal intervention strategies for ABT delivery across the Canadian healthcare system from acute to community care. We searched Scopus, CINAHL, OvidMedline, and other sources. Eligible articles were qualitative or mixed methods studies exploring ABT for adults with SCI in a Canadian healthcare setting. We analyzed qualitative findings through a thematic synthesis followed by a deductive content analysis. The Mixed Methods Appraisal Tool was used for critical appraisal. Nine articles were included. The thematic synthesis revealed two main themes: (1) factors influencing acceptance and adaptation of ABT across healthcare settings in Canada and (2) proposed solutions. The deductive analysis applied the Behaviour Change Wheel (BCW) to identify limited components of behaviour and appropriate interventions. To address ABT implementation challenges across the Canadian healthcare system, evidence-based interventions should target BCW subcategories of reflective motivation, social opportunity, and physical opportunity.

Unlocking the Potential of Repetitive Transcranial Magnetic Stimulation in Alzheimer's Disease: A Meta-Analysis of Randomized Clinical Trials to Optimize Intervention Strategies.

Repetitive transcranial magnetic stimulation (rTMS) is an advanced and noninvasive technology that uses pulse stimulation to treat cognitive impairment. However, its specific effects have always been mixed with those of cognitive training, and the optimal parameter for Alzheimer's disease (AD) intervention is still ambiguous. This study aimed to summarize the therapeutic effects of pure rTMS on AD, excluding the influence of cognitive training, and to develop a preliminary rTMS treatment plan. Between 1 January 2010 and 28 February 2023, we screened randomized controlled clinical trials from five databases (PubMed, Web of Science, Embase, Cochrane, and ClinicalTrials. gov). We conducted a meta-analysis and systematic review of treatment outcomes and rTMS treatment parameters. A total of 4,606 articles were retrieved. After applying the inclusion and exclusion criteria, 16 articles, comprising 655 participants (308 males and 337 females), were included in the final analysis. The findings revealed that rTMS significantly enhances both global cognitive ability (p = 0.0002, SMD = 0.43, 95% CI = 0.20-0.66) and memory (p = 0.009, SMD = 0.37, 95% CI = 0.09-0.65). Based on follow-up periods of at least 6 weeks, the following stimulation protocols have demonstrated efficacy for AD: stimulation sites (single or multiple targets), frequency (20 Hz), stimulation time (1-2 s), interval (20-30 s), single pulses (≤2500), total pulses (>20000), duration (≥3 weeks), and sessions (≥20). This study suggests that rTMS may be an effective treatment option for patients with AD, and its potential therapeutic capabilities should be further developed in the future.

Evaluation of the Environmental Impact of Oil Pollution from Maritime Transportation

Maritime transportation, which has a growing share in global transportation, and especially ships with large carrying capacities, increases the risk of marine environment pollution. The fact that maritime transport takes place not only in the open seas, but also in coastal waters, narrow waters with high navigational risk, and sea areas in the vicinity of ecologically sensitive zones necessitates prediction and calculation of this risk, as well as taking precautionary measures. This study seeks to identify optimal intervention strategies and determine where and when interventions should take place to minimize the adverse effects of potential oil pollution on the marine environment, utilizing a simulation model based on the example of Istanbul's Tuzla shipyard region. The Potential Incident Simulation and Evaluation System (PISCES) decision support system, which predicts the spread of pollution based on the behavior of oil in water, was used in the simulation. As oil weathering processes, which result in evaporation, emulsification, and dispersion, vary depending on the type of oil, weather, and oceanographic conditions, their seasonal average values were obtained from the relevant institutions and entered into the simulation. In the dispersion of oil, the surface current structure, which plays the most pivotal role, has been established by PISCES, based on the reference vector obtained from the relevant institute.Given that, once the oil disperses, starts to submerge and sedimentation occurs, intervention becomes impossible, leading to long-lasting negative impacts on the marine ecosystem, timely response to marine pollution caused by ships is of utmost importance. Intervention strategies should be selected based on dispersion time and quantity, as well as the duration of the interaction of oil with the coast. Another consequence is that oil pollution can affect not only the marine environment but other areas as well, such as adjacent environmentally sensitive areas, causing devastation to the marine habitat.

Harnessing Artificial Intelligence for Early Detection and Management of Infectious Disease Outbreaks

Infectious diseases pose ongoing threats to global public health, demanding advanced detection methods for effective outbreak management. This study explores integrating artificial intelligence (AI) for early detection and management. AI algorithms analyze diverse datasets, including electronic health records and social media, to identify potential outbreaks. Machine learning models predict disease spread and severity, aiding proactive resource allocation. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses AI's role in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study also evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. Ethical considerations are crucial, emphasizing collaboration between public health agencies, healthcare providers, and technology experts. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. This paper advocates for AI integration to enhance infectious disease surveillance, offering a proactive response to safeguard public health.

SIS Epidemic Spreading Under Multilayer Population Dispersal in Patchy Environments

We study SIS epidemic spreading models under population dispersal on multi-layer networks. We consider a patchy environment in which each patch comprises individuals belonging to different classes. Individuals disperse to other patches on a multi-layer network in which each layer corresponds to a class. The dispersal on each layer is modeled by a Continuous Time Markov Chain (CTMC). At each time, individuals disperse according to their CTMC and subsequently interact with the local individuals in the patch according to an SIS model. We establish the existence of various equilibria under different parameter regimes and establish their (almost) global asymptotic stability using Lyapunov techniques. We also derive simple conditions that highlight the influence of the multi-layer network on the stability of these equilibria. For this model, we study optimal intervention strategies using a convex optimization framework. Finally, we numerically illustrate the influence of the multi-layer network structure and the effectiveness of the optimal intervention strategies.