Future Incidence Research Articles

Evaluation of a charge nurse leadership development program.

Evaluation of a charge nurse leadership development program.

Hypotensive Transfusion Reaction due to Leukoreduced Platelets

Hypotensive transfusion reaction is a distinct category of adverse sequelae of transfusion characterized by an early and abrupt onset of hypotension, typically resolving promptly upon discontinuation of the transfusion. The primary mechanism behind this reaction seems to be the accumulation of bradykinin in the transfused blood product during processing. The authors wish to highlight a case of isolated hypotensive reaction following administration of leukoreduced platelets in an Indian male patient, which required vasopressors. Awareness of the risk factors and clinical presentation for this type of reaction are vital for proper patient care and the prevention of future incidents.

Predicting the dynamics of norovirus infection using time series models

Introduction. Norovirus infection (NI) is the most prevalent cause of acute gastroenteritis and outbreaks in semi-closed settings. Forecasting of NI may improve situational awareness and control measures.The aim of the study is to evaluate accuracy of time-series models for forecasting of norovirus incidence (on Sverdlovsk region dataset).Materials and methods. Simple ARIMA time-series models was chosen to forecast NI incidence via regression on its own lagged values. Dataset including passive surveillance monthly reports for Sverdlovsk region was used. All models were trained on data for 2015−2018 and tested on data for 2019. Models were benchmarked using Akaike information criterion (AIC) and mean absolute percentage error (MAPE).Results and discussion. NI incidence in Sverdlovsk raised in 2015-2018 with strong winter-spring seasonality. The time-series incidence data was stationary. Nine significant models were found and the most accurate model was SARIMA (1,0,0)(0,0,1). Despite its accuracy on 2019 test sample, forecast on COVID-19 pandemic period was failed. It was supposed that including additional regressors (climate and herd immunity data) and choosing of more robust time-series models may improve forecasting accuracy.Conclusion. ARIMA time-series models (especially SARIMA) suitable to forecast future incidence of NI in Sverdlovsk region. Additional investigations in terms of possible regressors and improved model robustness are needed.

A Comprehensive Case Study on the Historical Earthquakes in Major Fault Zones of Bangladesh

An earthquake is a fatal natural disaster that occurs suddenly without any prior signal. Every year the world faces many earthquakes with magnitudes varying from low to high. The uncertain nature of earthquakes makes it almost impossible to predict the exact time of occurrence. So, it is necessary to take precautionary steps before an earthquake hits. To take preventive measures, a proper study of tectonic plates and faults must be done to gain insight into the study area's susceptibility. Due to the geological characteristics and tec-tonic plate activity, Bangladesh risks facing an Earthquake. Every year, Bangladesh also faces some number of earthquakes of different magnitude. This study analyzes the earth-quake events in different fault zone of Bangladesh in the last 49 years. The study showed that the Tripura Fault and Shillong Plateau are the most active among the five major faults. The frequency of these faults is higher than the other ones. Though the past earthquake magnitudes were small, it is still concerning that the Tripura fault and Shillong plateau show much more frequent seismic activities that may indicate a bigger earthquake incident in the near future. The epicenter depth of most earthquakes is within 40 km from the sur-face, which is a concerning issue because most earthquakes are shallow earthquakes that tend to have more damaging properties than deep ones.

A machine learning approach to formation of earthquake categories using hierarchies of magnitude and consequence to guide emergency management

This study deployed k-means clustering to formulate earthquake categories based on magnitude and consequence, using global earthquake data spanning from 1900 to 2021. Based on patterns within the historical data, numeric boundaries were extracted to categorize the magnitude, deaths, injuries, and damage caused by earthquakes into low, medium, and high classes. Following a future earthquake incident, the classification scheme can be utilized to assign earthquakes into appropriate categories by inputting the magnitude, number of fatalities and injuries, and monetary estimates of total damage. The resulting taxonomy provides a means of classifying future earthquake incidents, thereby guiding the allocation and deployment of disaster management resources in proportion to the specific characteristics of each incident. Furthermore, the scheme can serve as a reference tool for auditing the utilization of earthquake management resources.

Estimating the Cost-Effectiveness of Switching to Higher-Valency Pediatric Pneumococcal Conjugate Vaccines in the United Kingdom.

Currently, the 13-valent pneumococcal conjugate vaccine (PCV13) is administered under a 1+1 (1 primary dose) pediatric schedule in the United Kingdom (UK). Higher-valency PCVs, 15-valent PCV (PCV15), or 20-valent PCV (PCV20) might be considered to expand serotype coverage. We evaluated the cost-effectiveness of PCV20 or PCV15 using either a 2+1 (2 primary doses) or 1+1 schedule for pediatric immunization in the UK. Using a dynamic transmission model, we simulated future disease incidence and costs under PCV13 1+1, PCV20 2+1, PCV20 1+1, PCV15 2+1, and PCV15 1+1 schedules from the UK National Health Service perspective. We prospectively estimated disease cases, direct costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio. Scenario analyses were performed to estimate the impact of model assumptions and parameter uncertainty. Over a five-year period, PCV20 2+1 averted the most disease cases and gained the most additional QALYs. PCV20 2+1 and 1+1 were dominant (cost-saving and more QALYs gained) compared with PCV15 (2+1 or 1+1) and PCV13 1+1. PCV20 2+1 was cost-effective (GBP 8110/QALY) compared with PCV20 1+1. PCV20 was found cost-saving compared with PCV13 1+1, and PCV20 2+1 was cost-effective compared with PCV20 1+1. Policymakers should consider the reduction in disease cases with PCV20, which may offset vaccination costs.

1213-P: Artificial Intelligence–Based Body Composition Analysis Using Computed Tomography Images Predicts Both Prevalence and Incidence of Diabetes—A Cross-Sectional and Longitudinal Analysis of 10-Year Retrospective Cohort in Korea

Background: We aimed to assess how the ageing-related changes in body composition contribute to the prevalence and incidence of diabetes using artificial intelligence (AI)-based analysis of abdominal computed tomography (CT) images. Methods: In this retrospective cohort study, we identified 15330 subjects age ≥18 years with abdominal CT scans at baseline, who underwent medical checkup at Seoul National University Hospital Healthcare System Gangnam Center from January 1, 2011 to September 30, 2012. Of these, 11693 subjects with available follow-up data were included in the longitudinal analysis. The volume of each body segment involved in abdominal CT images was measured by using an AI-based image analysis software. Findings: The ratio of visceral fat to subcutaneous fat (VF/SF ratio) increased with ageing. The optimal cutoffs of VF/SF ratio to predict the prevalence of diabetes were 1.2 and 0.5 in men and women, respectively. A VF/SF ratio over the cutoff was associated with a higher prevalence of diabetes (age and BMI adjusted OR 2.1, [95% CI 1.8-2.4] in men; 3.1, [2.4-3.9] in women). The same cutoffs of VF/SF ratio were used to predict incident diabetes in ten years of follow-up. Subjects with normal glucose tolerance at baseline who had higher VF/SF ratio had increased risk of progression to prediabetes or diabetes (age and BMI adjusted HR 1.2, [95% CI 1.1-1.4] in men; 1.4, [1.2-1.6] in women). Subjects with prediabetes at baseline who had higher VF/SF ratio also more frequently progressed to diabetes (age and BMI adjusted HR 1.4, [95% CI 1.2-1.6] in men; 1.8, [1.5-2.3] in women). Interpretation: VF/SF ratio in the abdomen change with ageing and are associated with the prevalence and future incidence of diabetes. AI-based analysis of abdominal CT images may help easily obtain body composition data to clinically assess the risk of incident diabetes. Disclosure Y.Kim: None. H.Son: None. J.Yoon: None. H.Choe: None. T.Oh: None. Y.Cho: Consultant; LG Chem. Funding Ministry of Health & Welfare, Republic of Korea

Operational planning of vehicles for rescue and relief operations considering the unavailability of the relocated vehicles

Rapid urbanization has led to increasing fire incidents and false alarms, increasing the response time of fire departments. When a call arrives, the current technology deploys and relocates the vehicles based on their immediate impact on the system's preparedness. However, the unavailability of the relocated vehicles is often ignored during the relocation, thus the system's preparedness is overestimated. This paper presents a novel mixed-integer programming (MIP) model developed for the relocation and deployment of emergency/fire vehicles. The proposed model incorporates the unavailability element, and estimates system preparedness for future incidents more accurately than current models. To confirm the efficiency of the proposed approach, the required simulations were conducted in Mashhad, Iran. The results demonstrated the ability of the proposed model to improve the performance of the fire department in several performance metrics. We also provide sensitivity analysis over the critical parameters to demonstrate the robustness of the model.

Evolution of Floods: From Ancient Times to the Present Times (ca 7600 BC to the Present) and the Future

Floods are one of the most dangerous natural disasters, causing great destruction, damage, and even fatalities worldwide. Flooding is the phenomenon of a sudden increase or even slow increase in the volume of water in a river or stream bed as the result of several possible factors: heavy or very long precipitation, melting snowpack, strong winds over the water, unusually high tides, tsunamis, or the failure of dams, gages, detention basins, or other structures that hold back water. To gain a better understanding of flooding, it is necessary to examine evidence, search for ancient wisdom, and compare flood-management practices in different regions in a chronological perspective. This study reviews flood events caused by rising sea levels and erratic weather from ancient times to the present. In addition, this review contemplates concerns about future flood challenges and possible countermeasures. Thus, it presents a catalogue of past examples in order to present a point of departure for the study of ancient floods and to learn lessons for preparation for future flood incidents including heavy rainfalls, particularly in urbanized areas. The study results show that ancient societies developed multifaceted technologies to cope with floods and many of them are still usable now and may even represent solutions and measures to counter the changing and increasingly more erratic weather of the present.

Global, Regional, and National Prevalence of Gout From 1990 to 2019: Age-Period-Cohort Analysis With Future Burden Prediction.

Gout is a common and debilitating condition that is associated with significant morbidity and mortality. Despite advances in medical treatment, the global burden of gout continues to increase, particularly in high-sociodemographic index (SDI) regions. To address the aforementioned issue, we used age-period-cohort (APC) modeling to analyze global trends in gout incidence and prevalence from 1990 to 2019. Data were extracted from the Global Burden of Disease Study 2019 to assess all-age prevalence and age-standardized prevalence rates, as well as years lived with disability rates, for 204 countries and territories. APC effects were also examined in relation to gout prevalence. Future burden prediction was carried out using the Nordpred APC prediction of future incidence cases and the Bayesian APC model. The global gout incidence has increased by 63.44% over the past 2 decades, with a corresponding increase of 51.12% in global years lived with disability. The sex ratio remained consistent at 3:1 (male to female), but the global gout incidence increased in both sexes over time. Notably, the prevalence and incidence of gout were the highest in high-SDI regions (95% uncertainty interval 14.19-20.62), with a growth rate of 94.3%. Gout prevalence increases steadily with age, and the prevalence increases rapidly in high-SDI quantiles for the period effect. Finally, the cohort effect showed that gout prevalence increases steadily, with the risk of morbidity increasing in younger birth cohorts. The prediction model suggests that the gout incidence rate will continue to increase globally. Our study provides important insights into the global burden of gout and highlights the need for effective management and prophylaxis of this condition. The APC model used in our analysis provides a novel approach to understanding the complex trends in gout prevalence and incidence, and our findings can inform the development of targeted interventions to address this growing health issue.

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020

As of January 2021, Australia had effectively controlled local transmission of COVID-19 despite a steady influx of imported cases and several local, but contained, outbreaks in 2020. Throughout 2020, state and territory public health responses were informed by weekly situational reports that included an ensemble forecast of daily COVID-19 cases for each jurisdiction. We present here an analysis of one forecasting model included in this ensemble across the variety of scenarios experienced by each jurisdiction from May to October 2020. We examine how successfully the forecasts characterised future case incidence, subject to variations in data timeliness and completeness, showcase how we adapted these forecasts to support decisions of public health priority in rapidly-evolving situations, evaluate the impact of key model features on forecast skill, and demonstrate how to assess forecast skill in real-time before the ground truth is known. Conditioning the model on the most recent, but incomplete, data improved the forecast skill, emphasising the importance of developing strong quantitative models of surveillance system characteristics, such as ascertainment delay distributions. Forecast skill was highest when there were at least 10 reported cases per day, the circumstances in which authorities were most in need of forecasts to aid in planning and response.

PICTUREE-Aedes: A Web Application for Dengue Data Visualization and Case Prediction.

Dengue fever remains a significant public health concern in many tropical and subtropical countries, and there is still a need for a system that can effectively combine global risk assessment with timely incidence forecasting. This research describes an integrated application called PICTUREE-Aedes, which can collect and analyze dengue-related data, display simulation results, and forecast outbreak incidence. PICTUREE-Aedes automatically updates global temperature and precipitation data and contains historical records of dengue incidence (1960-2012) and Aedes mosquito occurrences (1960-2014) in its database. The application utilizes a mosquito population model to estimate mosquito abundance, dengue reproduction number, and dengue risk. To predict future dengue outbreak incidence, PICTUREE-Aedes applies various forecasting techniques, including the ensemble Kalman filter, recurrent neural network, particle filter, and super ensemble forecast, which are all based on user-entered case data. The PICTUREE-Aedes' risk estimation identifies favorable conditions for potential dengue outbreaks, and its forecasting accuracy is validated by available outbreak data from Cambodia.

Innovative Approaches in Cybersecurity: Addressing Future Trends, Challenges, and Incident Response

Innovative Approaches in Cybersecurity: Addressing Future Trends, Challenges, and Incident Response

Learning from cyber security incidents: A systematic review and future research agenda

Cyber security incidents are now prevalent in many organisations. Arguably, those who can learn from security incidents and address the underlying causes will reduce the prevalence of similar ones in the future. This research provides a new examination of how organisations learn from incidents by systematically reviewing academic research on organisational learning from cyber security incidents and identifying further research needed in this area. To do this, it considers three research questions: what research has been conducted on learning from cyber security incidents, what learning practices in organisations have been found by research and what improvements have been recommended, and what further research is needed as organisations learn from such incidents. Using the PRISMA method, a total of 3,986 articles were extracted and, from these, a relevant set of 30 were selected for analysis to map the body of research, and to identify future research avenues. Despite learning lessons being recommended by both researchers and industry standards, our findings suggest that this advice is not being fully adopted by organisations. Importantly, these studies have found inadequate participation in learning activities, with superficial causal investigations, scarce effort on ensuring lessons are implemented and no evaluation of whether the actions taken actually reduce future security incidents. More research is needed to understand the right level and which learning practices to invest in for the greatest impact. For practitioners, this review discusses the essential elements of an effective process to learn from incidents. This review provides academics with a novel synthesis of the research undertaken on this topic, enabling them to incorporate the significant findings into their work and potentially explore the research agenda suggested.

Association Between the Incidence of Hospitalizations for Acute Cardiovascular Events, Weather, and Air Pollution

BackgroundThe incidence of hospitalizations for cardiovascular events has been associated with specific weather conditions and air pollution. A comprehensive model including the interactions between various environmental factors remains to be developed. ObjectivesThe purpose of this study was to develop a comprehensive model of the association between weather patterns and the incidence of cardiovascular events and use this model to forecast near-term spatiotemporal risk. MethodsWe present a spatiotemporal analysis of the association between atmospheric data and the incidence rate of hospital admissions related to heart failure (922,132 episodes), myocardial infarction (521,988 episodes), and ischemic stroke (263,529 episodes) in ∼24 million people in Canada between 2007 and 2017. Our hierarchical Bayesian model captured the spatiotemporal distribution of hospitalizations and identified weather and air pollution–related factors that could partially explain fluctuations in incidence. ResultsModels that included weather and air pollution variables outperformed models without those covariates for most event types. Our results suggest that environmental factors may interact in complex ways on human physiology. The impact of environmental factors was magnified with increasing age. The weather and air pollution variables included in our models were predictive of the future incidence of heart failure, myocardial infarction, and ischemic strokes. ConclusionsThe increasing importance of environmental factors on cardiovascular events with increasing age raises the need for the development of educational materials for older patients to recognize environmental conditions where exacerbations are more likely. This model could be the basis of a forecasting system used for local, short-term clinical resource planning based on the anticipated incidence of events.

A multivariate regressor of patterned dopamine release predicts relapse to cocaine

Understanding mesolimbic dopamine adaptations underlying vulnerability to drug relapse is essential to inform prognostic tools for effective treatment strategies. However, technical limitations have hindered the direct measurement of sub-second dopamine release invivo for prolonged periods of time, making it difficult to gauge the weight that these dopamine abnormalities have in determining future relapse incidence. Here, we use the fluorescent sensor GrabDA to record, with millisecond resolution, every single cocaine-evoked dopamine transient in the nucleus accumbens (NAc) of freely moving mice during self-administration. We reveal low-dimensional features of patterned dopamine release that are strong predictors of cue-induced reinstatement of cocaine seeking. Additionally, we report sex-specific differences in cocaine-related dopamine responses related to a greater resistance to extinction in males compared with females. These findings provide important insights into the sufficiency of NAc dopamine signaling dynamics-in interaction with sex-for recapitulating persistent cocaine seeking and future relapse vulnerability.

Investigation of the First African Swine Fever Outbreak in a Domestic Pig Farm in Hong Kong

In this study, we describe the epidemiological investigation of the first African swine fever (ASF) outbreak in a local domestic pig farm in the New Territories of Hong Kong in 2021. In the outbreak farm, several affected pigs presented clinical and pathological signs consistent with ASF, while the remaining pigs showed nonspecific clinical signs or did not exhibit any clinical signs. The relative low morbidity and mortality of ASF on this farm resulted in delayed detection and implementation of the control response. Despite this delay, no further spread of the disease from this farm to other farms or wild boars was observed. The clinical presentation of ASF in terms of morbidity and mortality on this farm indicated that it is essential for effective surveillance aimed at early detection for farmers, veterinarians, and pathologists to be educated about the different ways ASF can express itself in domestic pig populations. Epidemiological investigations consisted of field inspection, interviews with farm personnel to assess the management and biosecurity practices within the farm, and laboratory testing of animal and environmental samples. In addition, the complete genome of ASFV was obtained directly from the tissues of an infected pig to facilitate the epidemiological investigation. The genetic relationship at the whole genome level indicated that the isolate shared the highest level of similarity with genotype II ASFVs, including a 2019 isolate from Guangdong province, China (GD2019). Overall, the information presented here from the on-farm investigation with that from diagnostic testing and molecular analyses provides a basis for informed actions to prevent future incidents in farms with similar characteristics. Furthermore, this study highlighted the need to increase current knowledge about the molecular diversity amongst circulating viruses and potentially trace the source of infection.

All the King’s Horses and All the King’s Men: Falling Into Moral Injury, During the COVID-19 Pandemic

Health care providers who experienced giving care or abbreviating those services, during the COVID-19 pandemic, risked experiencing a sense of moral injury. Moral injury is a term to describe incidents where a health care provider knows the right thing to do, but constraints make it exceedingly difficult or impossible to execute the right course of action. These incidents likely also were present for sonographers and vascular technologists, and it is vital that these compromises for patients are recognized, and healing strategies offered to those suffering from moral injury. Some suggestions for treatment of moral injury are to recognize these experiences, seek solutions, provide empathy for patients and resources for health care providers. It may also be beneficial to allow employees and employers to forgive the mistakes made during the pandemic and focus on solutions to prevent future incidents of moral injury.

Modelling Responsible Digital Security Behaviour for Countering Social Media Manipulation

While the digital environment, and in particular social media, surrounds not only human’s identity and its societal functions projection, e.g., institutional and financial aspects, it also captures both individual and collective thoughts regarding former, ongoing, and future concepts, trends, and incidents placed in the physical world, in the digital environment, or in both which could impact both individual and collective consciousness, behaviour, and attitude towards different dimensions of reality. Accordingly, an initial attempt to define and model responsible digital security behaviour was made and ongoing discourses and AI-based solutions for tackling and containing social manipulation mechanisms exist in this domain. Noteworthily is that dedicated attention to understanding and modelling responsible digital security behaviour in social media for tackling and/or countering social media manipulation, e.g., disinformation and misinformation, still lacks. To this end, this research aims (i) to capture the factors influencing user behaviour towards tackling and/or countering social media manipulation, (ii) to build a Machine Learning model that assesses user’s responsibility in relation to tackling and/or countering social media manipulation mechanisms, and (iii) propose a set of socio-technical recommendations for building resilience to such mechanisms. To accomplish these research objectives, a Design Science Research methodological approach is taken by designing, developing, and evaluating the model proposed through exemplification. Finally, this research aims to enhance digital security awareness and resilience to social media manipulation of users and policy decision-makers to manage and further extend in a responsible and safe way the digital environment.

How to deal with an AI near-miss: Look to the skies

ABSTRACT AI systems are harming people. Harms such as discrimination and manipulation are reported in the media, which is the primary source of information on AI incidents. Reporting AI near-misses and learning from how a serious incident was prevented would help avoid future incidents. The problem is that ongoing efforts to catalog AI incidents rely on media reports—which does not prevent incidents. Developers, designers, and deployers of AI systems should be incentivized to report and share information on near misses. Such an AI near-miss reporting system does not have to be designed from scratch; the aviation industry’s voluntary, confidential, and non-punitive approach to such reporting can be used as a guide. AI incidents are accumulating, and the sooner such a near-miss reporting system is established, the better.