Early Warning Signs Research Articles

Early warning signs for SPDEs with continuous spectrum

Early warning signs for SPDEs with continuous spectrum

Warning signs for boundary noise and their application to an ocean Boussinesq model

In this paper, we construct and discuss early-warning signs of the approach of a parameter to a deterministic bifurcation on a stochastic partial differential equation (SPDE) model with Gaussian white-noise on the boundary. We specifically focus on the qualitative behaviour of the time-asymptotic autocovariance and autocorrelation of the solutions of the linearized system. We also discuss the reliability of the tools from an analytic perspective and through various examples. Among those, the application of the early-warning signs for an ocean Boussinesq model is explored through numerical simulations. The analytic results obtained expand on previous work and present valuable early-warning signs for various applications.

Proactive cyber fraud response: a comprehensive framework from detection to mitigation in banks

PurposeThis study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.Design/methodology/approachThe methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.FindingsFindings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.Practical implicationsThe practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.Originality/valueThe originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

Nurse by numbers: The impact of early warning systems on nurses' higher-order thinking, a quantitative study.

To evaluate registered nurses' perceptions of whether the mandated use of the early warning system vital signs tool impacts the development of nurses' higher-order thinking skills. A concurrent mixed methods study design. Using an online survey, registered nurses' perceptions were elucidated on whether early warning system algorithmic tools affected the development of their higher-order thinking. Likert-type matrix questions with additional qualitative fields were used to obtain information on nurse's perceptions of the tool's usefulness, clinical confidence in using the tool, compliance with escalation protocols, work environment and perceived compliance barriers. Most of the 305 (91%) participants included in the analysis had more than 5 years of nursing experience. Most nurses supported the early warning tool and were happy to comply with escalation protocols if the early warning score concurred with their assessment of the patient (63.6%). When the score and the nurse's higher-order thinking did not align, some had the confidence to override the escalation protocol (40.0%), while others omitted (69.4%) or inaccurately documented vital signs (63.3%) to achieve the desired score. Very few nurses (3.6%) believe using early warning tools did not impede the development of higher-order thinking. Although experienced nurses appreciate the support of early warning tools, most value patient safety above the tools and rely on their higher-order thinking. The sustained development and use of nurses' higher-order thinking should be encouraged, possibly by adding a critical thinking criterion to existing algorithmic tools. The study has implications for all nurses who utilize algorithmic tools, such as early warning systems, in their practice. Relying heavily on algorithmic tools risks impeding the development of higher-order thinking. Most experienced nurses prioritize their higher-order thinking in decision-making but believe early warning tools can impede higher-order thinking. Registered nurses participated as survey respondents.

Novel anticancer mechanisms of JinYingZi-derived oleanolic acid against renal cell carcinoma: an in silico analysis

Renal cell carcinoma (RCC) is the most common type of kidney cancer, accounting for 90% of all estimated cases. Advanced RCC often carries poor prognosis due to its high metastases rate, the lack of early-warning signs, as well as its complex clinical manifestations and its resistance to chemotherapy. Ethnopharmacologically, Fructus Rosae laevigata (JinYingZi) has been employed by Chinese medicine to treat various urinary tract and gastrointestinal dis¬orders. This study aimed at performing a series of mechanistic analyses in order to unlock the anticancer potential of JinYingZi-derived bioactive components against RCC. Several network pharmacology tools were employed so as to analyse the drug-disease interactions. Our data revealed that more than 2,214 genes were dysregulated in RCC, whereas the JinYingZi-derived bioactive compounds modulated 347 genes. The intersecting between RCC and the bioactive compounds revealed 132 cross targets. Our results were further validated by conducting molecular docking, which revealed a stable association between oleanolic acid with each of the following targets: androgen receptor (AR), dipeptidyl peptidase (DPP), estradiol (ESR1), nitric oxide synthase 2 (NOS2), and cyclooxygenase-2 (PTGS2). Our approach is being used successfully in order to evaluate a panel of novel medicinal plant-derived bioactive com¬pounds, and may lead to the identification of safe and effective chemical scaffolds that could act as templates for drug discovery or yield potential drug candidates.

Application of early warning signs to physiological contexts: a comparison of multivariate indices in patients on long-term hemodialysis.

Early warnings signs (EWSs) can anticipate abrupt changes in system state, known as "critical transitions," by detecting dynamic variations, including increases in variance, autocorrelation (AC), and cross-correlation. Numerous EWSs have been proposed; yet no consensus on which perform best exists. Here, we compared 15 multivariate EWSs in time series of 763 hemodialyzed patients, previously shown to present relevant critical transition dynamics. We calculated five EWSs based on AC, six on variance, one on cross-correlation, and three on AC and variance. We assessed their pairwise correlations, trends before death, and mortality predictive power, alone and in combination. Variance-based EWSs showed stronger correlations (r = 0.663 ± 0.222 vs. 0.170 ± 0.205 for AC-based indices) and a steeper increase before death. Two variance-based EWSs yielded HR95 > 9 (HR95 standing for a scale-invariant metric of hazard ratio), but combining them did not improve the area under the receiver-operating curve (AUC) much compared to using them alone (AUC = 0.798 vs. 0.796 and 0.791). Nevertheless, the AUC reached 0.825 when combining 13 indices. While some indicators did not perform overly well alone, their addition to the best performing EWSs increased the predictive power, suggesting that indices combination captures a broader range of dynamic changes occurring within the system. It is unclear whether this added benefit reflects measurement error of a unified phenomenon or heterogeneity in the nature of signals preceding critical transitions. Finally, the modest predictive performance and weak correlations among some indices call into question their validity, at least in this context.

The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses

BackgroundWhen job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data.MethodsA total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly.DiscussionThe BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices.Trial RegistrationNCT05481138.

The Assessment of Awareness of Early Warning Signs of Bipolar Disorder Recurrence Among Patients and Their Main Caregivers in Jordan.

One of the biggest challenges to public health is increasing knowledge of the early warning signs (EWS) of bipolar disorder recurrence. The recurrence incidence is still high despite multiple attempts to improve the awareness level. The awareness level is still ambiguous and insufficient. The purpose of this study was to assess the degree of awareness of EWS of bipolar disorder recurrence among patients and their main caregivers in Jordan. A descriptive cross-sectional design was used to recruit 275 patients and their main caregivers to fill out the adapted version of the Awareness of the EWS of Relapse Questionnaire. The analysis showed that patients and their main caregivers' awareness of the EWS of bipolar disorder recurrence was low and inadequate (patients, mean = 2.61 (SD = 2.6); main caregivers, mean = 3.84 (SD = 2.2); scores range from 0 to 10). According to the study, the awareness level of these signs is still vague and inadequate. This means that ensuring long-term positive results for patients and their main caregivers requires a high degree of awareness regarding the warning signs and symptoms of bipolar disorder recurrence. To engage in standard practice, EWS of bipolar disorder recurrence awareness necessitate adequate resources, support, and an empowered and well-communicated multidisciplinary team.

Improved Monitoring of Early-Warning Signs of an AMOC Collapse

This paper reviews a recent study predicting that the Atlantic Meridional Overturning Current circulation may collapse in the middle of this century and concludes that there are too many uncertainties to allow a reliable prediction. Even if the model used in that study is fairly adequate, its predictions depend heavily on the quality of the involved approximations, the choice of the circulation proxy, the method of trend estimation, the size of the rolling window used for the estimation of the second moments, the determination of the start time of ramping, etc. Forced to set lower targets, the focus is consequently shifted to the improved detection of early-warning signs like an increase in variance and autocorrelation. Two new estimation procedures are introduced. The first concerns the estimation of the trend and includes a new criterion for comparing the quality of different estimates. The second is a procedure for detecting changes in the first-order autocorrelation without any delay caused by an estimation window. The results obtained with these methods suggest that the autocorrelation increases erratically rather than steadily which would make forecasting based on extrapolation impossible.

Effectiveness of information technology assisted relapse prevention program on relapse among people who live with severe mental disorders.

Severe Mental Disorders (SMDs) cause mental health worldwide challenges because of several relapses and extensive recovery periods of hospitalization, which put a lot of economic and social burden on families and governments. Therefore, interventions are necessary to decrease the relapse of these disorders. This study was conducted to investigate the effect of Information Technology Assisted Relapse Prevention Program (ITAREP) on relapse among people who live with SMDs. This study was a randomized clinical trial with intervention and control groups. ITAREP is a remote intervention based on monitoring the Early Warning Signs (EWS) to decrease the number of potential relapses. Using convenience sampling, people with SMDs admitted to Sina Juneqan Psychiatric Hospital and their caregivers were recruited in this study and randomly allocated to the control and intervention groups. Two checklists of the EWS for the patient and the patient's caregiver were used for monitoring the relapse signs. Data were collected at baseline and 90 days after discharge and were analyzed using t-test and Chi-square statistical tests and linear regression in SPSS software. Fifty-two patients who lived with SMDs participated in this study (26 in the intervention group and 26 in the control group). The two groups were homogeneous regarding age, gender, type of mental disorder, and duration of the disorder. Forty-two males and ten females participated in this research. Most of the participants were diagnosed with schizophrenia. The results showed that the number of relapses in the intervention group was significantly lower than in the control group in the post-test. Social workers, as the case managers and a member of the interdisciplinary psychiatric team, can actively perform follow-up measures after discharge using ITAREP, and it can be expected that these interventions will reduce the number of relapses among patients who live with SMDs.

EARLY WARNING SIGNS AND CRISES IN ADRD/AD: VIEWS FROM LATINO/AFRICAN AMERICAN CAREGIVERS AND PROVIDERS

Abstract Incidence of Alzheimer’s disease and related dementias (ADRD) in Latino and African American populations is significantly greater than for non-Hispanic Whites. Latino and African American caregiver families rely on family for care, including children and youth, and access formal care systems less frequently and at later stages compared to non-Latino White families. Yet we know little about what precipitates the eventual move to formal care. This study sought to understand whether African American and Latino families living with ADRD were able to identify Early Warning Signs (EWS) and potential crises in persons living with ADRD and explore differences in how families and health care professionals perceive EWS and crises. Semi-structured interviews were conducted with health care professionals (n = 26), Latino and African American adult (n = 36), and youth caregivers aged 10 – 19 (n = 31). Participants listed eight EWS (cooking, getting lost, forgetfulness, emotional/anger, repeating, bringing up the past) and defined Crisis as an event or series of events that led to seeking formal care, included getting lost, bad falls, and death of a loved one. Adults felt frustration, even at the beginning of EWS while youth found humor in the care. There were differences in stories shared within the same caregiver family; youth recognized EWS but reported that adults would not talk about it. Health care professionals identified EWS but believed families would be unable to identify EWS. This potential misperception needs further attention to reduce communication barriers around the care planning for this population.

Impact of climate change, climate variability and adaptation in the coastal area of Cambodia

Cambodia's coastal zones are highly dependent on climate and weather, making them susceptible to climate change and causing farmers’ livelihoods to be more vulnerable compared to urban residents. Since climate issues already impact the agricultural sector in Cambodia, this paper aims to assess the impact of disasters in the context of climate change, which impacts household agricultural production, and to identify the existing community's capacity to respond and adapt to climate change and climate variability in the coastal zones of Cambodia. A survey was conducted in the agricultural coastal zone provinces of Cambodia. Data was analysed using SPSS software. The survey data demonstrates that disasters and climate change, particularly drought and floods, significantly impact the economic status of the interviewed households. The proportion of households experiencing disasters and climate change impacts has increased gradually. Regarding household capacity to respond and adapt to disasters and climate change, households in the target area mainly focused on resilient farming techniques for rice crop production rather than other farming activities, and most of the practices were targeted to adaptation in the occurrence of droughts. The issue of receiving Early Warning Signs (EWS) information was addressed and 37% of the respondents reported “no source of climate information”. Therefore, it is recommended to strengthen the capacity of local people in the coastal areas of Cambodia in responding to climate change.

In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment.

Driving is a complex daily activity indicating age and disease-related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults' driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with MCI to those without reveals that those with MCI exhibit smoother and safer driving patterns. This suggests that drivers with MCI are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.

Very low risk of short bowel after Roux-en-Y gastric bypass – a large nationwide Swedish cohort study

BackgroundRoux-en-Y gastric bypass (RYGB) is one of the most common bariatric procedures. Internal herniation may lead to small bowel ischemia requiring small bowel resection, resulting in short bowel syndrome. ObjectiveTo determine the incidence of extensive small bowel resection in patients operated with RYGB. We also aimed to look for early clinical warning signs among patients requiring extensive small bowel resection. SettingCohort from national quality registers. MethodsAll patients having undergone RYGB between January 2007 to June 2019 were analyzed in the Scandinavian Obesity Surgery Registry (SOReg). We identified patients with small bowel obstruction (SBO) for whom small bowel resection was necessary. Additionally, we assessed clinical signs in these patients. ResultsThe study included 57,255 patients having undergone RYGB. Closure of the mesenteric openings was performed in 78%. Surgery for SBO was required in 3659 (6%) of patients, and small bowel resection in 188 (.3%). Extensive small bowel resection, resulting in less than 1.5 meters of remaining small bowel, was required in 7 patients (.01%). All patients with extensive small bowel resection presented with abdominal pain and had confirmed internal herniation as the cause of the small bowel resection, and 2 of 7 patients died. Closure of mesenteric defects was not associated with a reduction in overall small bowel resection rates (P = .89) ConclusionSurgery for SBO after RYGB was common (6%). The risk of extensive small bowel resection leading to short bowel was low (.01%). Patients with abdominal pain after RYGB should be assessed for internal hernia, as it can be devastating.

Watershed-Mediated Ecomorphological Variation: A Case Study with the Twin-Striped Clubtail Dragonfly (Hylogomphus geminatus).

Anthropogenic land-cover change is modifying ecosystems at an accelerating rate. Changes to ecomorphologically variable taxa within those ecosystems serve as early-warning signs that resources on which humans and other animals depend are being altered. One known ecomorphologically variable taxon is Hylogomphus geminatus, a species of dragonfly in the southeastern United States that shows pronounced variation in total body length across its limited geographic range. We measured total length of live as well as preserved museum specimens of H. geminatus and the sympatric species Progomphus obscurus (as a means for comparison). Both species showed significant size differences linked to HUC-8 watersheds in which they occur. H. geminatus showed additional significant differences on either side of the Apalachicola River, Florida, for all comparisons by sex. In overlapping watersheds, the species tended to show the same trends in length relative to their respective averages. Smaller body length was associated with more urban and agricultural land cover. These findings indicate that ecomorphological variation is tied to the watershed scale and point to significant variations on either side of the Apalachicola River. More thorough future analyses would be needed to verify trends in body length and identify the drivers behind them.

EVALUATING THE INFLUENCE OF SPATIAL RESOLUTION ON LANDSLIDE DETECTION: A CASE STUDY IN THE CARLYON BEACH PENINSULA, WASHINGTON

Abstract. Landslides are geological events in which masses of rock and soil slide down the slope of a mountain or hillside. They are influenced by topography, geology, weather, and human activity, and can cause extensive damage to the environment and infrastructure, as well as delay transportation networks. Therefore, it is imperative to detect early-warning signs of landslide hazards as a means of prevention. Traditional landslide surveillance consists of field mapping, but the process is costly and time consuming. Modern landslide mapping uses Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and sophisticated algorithms to analyze surface roughness and extract spatial features and patterns of landslide and landslide-prone areas. This study follows a previous study performed that demonstrated that it is possible to detect unstable terrain using algorithmic mapping techniques. The focus of this study is to show how spatial resolution can influence the accuracy of the classification results. The DEM data was resampled from 6 to 12, 24, 48 and 96 ft spatial resolution. The surface feature extractors employed (local topographic range, local topographic variability, slope, and roughness) are fused and analyzed simultaneously by applying k-means and Gaussian Mixture Model (GMM) clustering methods. When compared with the detailed, independently compiled landslide reference map, our data shows a decrease in performance as spatial resolution decreases. These results suggest that spatial resolution does impact the performance of landslide classification.

Bifurcations and Early-Warning Signs for SPDEs with Spatial Heterogeneity

AbstractBistability is a key property of many systems arising in the nonlinear sciences. For example, it appears in many partial differential equations (PDEs). For scalar bistable reaction-diffusions PDEs, the bistable case even has taken on different names within communities such as Allee, Allen-Cahn, Chafee-Infante, Nagumo, Ginzburg-Landau,$$\Phi ^4$$Φ4, Schlögl, Stommel, just to name a few structurally similar bistable model names. One key mechanism, how bistability arises under parameter variation is a pitchfork bifurcation. In particular, taking the pitchfork bifurcation normal form for reaction-diffusion PDEs is yet another variant within the family of PDEs mentioned above. More generally, the study of this PDE class considering steady states and stability, related to bifurcations due to a parameter is well-understood for the deterministic case. For the stochastic PDE (SPDE) case, the situation is less well-understood and has been studied recently. In this paper we generalize and unify several recent results for SPDE bifurcations. Our generalisation is motivated directly by applications as we introduce in the equation a spatially heterogeneous term and relax the assumptions on the covariance operator that defines the noise. For this spatially heterogeneous SPDE, we prove a finite-time Lyapunov exponent bifurcation result. Furthermore, we extend the theory of early warning signs in our context and we explain, the role of universal exponents between covariance operator warning signs and the lack of finite-time Lyapunov uniformity. Our results are accompanied and cross-validated by numerical simulations.

Early warning signs of thyroid autoantibodies seroconversion: A retrospective cohort study

BackgroundSerum anti-thyroid peroxidase antibody (anti-TPO) and anti-thyroglobulin antibody (anti-Tg) levels are key indicators for the diagnosis of autoimmune diseases, especially autoimmune thyroiditis. Before the thyroid autoantibodies turn from negative to positive, it is unknown whether any clinical indicators in the body play a warning role. PurposeTo establish an early prediction model of seroconversion to positive thyroid autoantibodies. MethodsThis retrospective cohort study collected information based on clinical laboratory data. A logistic regression model was used to analyse the risk factors associated with a change in thyroid autoantibodies to an abnormal status. A machine-learning approach was employed to establish an early warning model, and a nomogram was used for model performance assessment and visualisation. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses were used for internal and external validation. ResultsLogistic regression analysis revealed that albumin to globulin ratio, triglyceride levels, and Glutamic acid levels among liver function and some metabolism-related indicators, high density lipoprotein C among metabolism-related indicators, and cystatin C among renal function indicators were all risk factors for thyroid antibody conversion (P < 0.05). In addition, several indicators in the blood count correlated with thyroid conversion (P < 0.05). Changes in the ratio of free thyroxine to free triiodothyronine were a risk factor for positive thyroid antibody conversion (ORfT4/fT3 = 1.763; 95% confidence interval 1.554–2.000). The area under the curve (AUC) of the early warning model based on the positive impact of clinical laboratory indicators, age, and sex was 0.85, which was validated by both internal (AUC 0.8515) and external (AUC 0.8378) validation. ConclusionsThe early warning model of anti-TPO and anti-Tg conversion combined with some clinical laboratory indicators in routine physical examination has a stable warning efficiency.

Recognizing Early Warning Signs (EWS) in Patients Is Critically Important

Introduction: Monitoring vital signs is a basic indicator of a patient’s health status and allows prompt detection of delayed recovery or adverse effects and early intervention. Patients with adverse events during hospitalization often display clinical decline for several hours before the event is observed. Non-critical care Nurses’ inconsistent recognition and response to patient deterioration lead to an increase in the length of hospital stay, unexpected admissions to the ICU, and increased morbidity and mortality. Aim: The study aimed to assess the factors that facilitate or impede the detection of early warning signs among adult patients hospitalized in tertiary care settings. Training should be provided to improve nurses’ knowledge, practice and attitude toward early warning signs of deteriorating patients leading to enhanced clinical judgment, skills and decision-making in addressing alerts. Methodology: A literature search was carried out in various databases; these were Cumulative Index to Nursing and Allied Health Literature (CINHAL), Google Scholar, PubMed, Science Direct, and Sage. The search area was narrowed from 2017 to 2022. The keywords used were “prevalence” AND “unplanned ICU admission”, “the importance of early warning signs” “outcome failure in rescue” “patient deterioration, communication” “improvement in early detection” AND “patient outcome admission” AND “early warning signs” AND “Pakistan”. After the analysis process, around 33 articles that met the inclusion criteria and were most relevant to the scope and context of the current study were considered. Conclusion: Most of the studies had reviewed literature in a qualitative retrospective observational study, content analysis, mixed method, and quasi-experimental study. The literature review identified that long hours of shift, nurse staffing levels, missed vital signs, lack of nursing training and education, and communication impact nurses’ ability to recognize and respond to early warning signs.

Early Warning System of Conflict Prevention Strategy in Nigeria

The entire globe is heated and faces immense annihilations that no single country can vouch to be truly free from. Though, conflict is an intrinsic and inevitable part of human existence, the nature and volatility of conflict experienced in Nigeria brought with it a quantum of warning signs which predominantly is not noticed or ignored. This study examines the Nigerian security sector as regards the disregard for available early warning signs of conflict in the polity which ordinarily form a whole ‘Early Warning System’ (EWS) of Conflict. The study employed historical method of analyzing data generated from both primary and secondary sources. A hundred key informants were systematically stratified and interviewed in each of six geopolitical zone of the country making total of 600 respondents while simple percentage frequency count and other statistical tools were used to analyze the derivatives in order to consolidate the mixed methodology. Findings show that significant early warning signs (EWS) precede most conflicts unawares and when the conflict signs are visible, we are not enlightened or informed to respond correspondingly let alone taking preventive actions. Also, government is complacent in the direction of conflict prevention policies and advocacy etc. The author therefore recommended an apt conflict prevention and management model which, if mainstreamed, would network all the security architectures hence, enhance security in the nation by fully engaging all the security operatives. It will ensure citizens’ rights and privileges while dignifying the nation in the international scene.