Prediction Of Disease Outbreaks Research Articles

The Impact of Big Data and Predictive Analytics on U.S. Healthcare Delivery: Opportunities, Challenges, and Future Directions

Healthcare, like many other industries, has been significantly influenced by big data and predictive analytics. The vast volume, velocity, and variety of information within big data sets have transformed the way we approach patient care and medical innovation. From predicting disease outbreaks to delivering personalized treatment plans, big data analytics offers immense potential for revolutionizing healthcare. This review explores the significant impact of big data and predictive analytics on the U.S. healthcare system. It delves into hospitals' ability to effectively leverage complex information and assess the potential benefits they may reap from successful implementation. The introduction defines big data and predictive analytics in the context of healthcare and outlines the paper's objectives. It further discusses the evolution of big data and analytics in healthcare, its key applications, and the challenges it presents. Additionally, we explore potential solutions and ethical considerations surrounding big data analytics in healthcare. Overall, this paper underscores the transformative power of big data and predictive analytics in revolutionizing U.S. healthcare delivery and improving patient outcomes.

Measles and Pertussis outbreaks in England and Wales: a time-series analysis

Background Vaccine coverage for common infectious diseases such as Measles and Pertussis (also known as whooping cough) have been declining in England and Wales since 2014. Consequently, significant increases in Measles and Pertussis cases are observed in the community. Aim To explore whether Google Trends offers a predictive utility as a health surveillance tool for Meases and Pertussis in England and Wales. Design and Setting Google search data related to Measles and Pertussis, including common associated symptoms, were downloaded for 52 weeks from 07/01/2023 – 07/01/2024. Measles and Pertussis case data were retrieved from the weekly Notification of Infectious Disease (NOID) reports. Methods The associations between searching and case data were explored using a time-series analyses, including cross-correlations, Prais-Winsten regression and joinpoint analysis. Results Significant cross-correlations were found for Measles cases and “measles” searching (r=.41) at a lag of -1 week. For Pertussis cases, searching for “whooping cough” (r=.31), “cough” (r=.39), “100 day cough” (r.41) and “vomiting” (r=.42) were significantly correlated at a lag of -3 to -2 weeks. In multivariable regression, “measles” remained significantly associated with Measles cases (β=.24, SE=.33, p=.02) as did “whooping cough” (β=.71, SE=.27, p=.01) and “cough” (β=1.99, SE=.54, p=.001) for pertussis. Conclusion Increases in Measles and Pertussis cases follow increases in online searches for both diseases and selected respective symptoms. Further work is required to explore how GT can be used in conjunction with other health surveillance systems to monitor or even predict disease outbreaks, to better target public health interventions.

Temporal changes in streamflow can predict parasitism levels in freshwater prawns better than host traits

Abstract Understanding how changes in the hydrological regime drive parasite loads and dynamics remains a challenging issue in ecological parasitology. Temporal changes in streamflow and rainfall are key hydrological factors that could alter interactions between the parasite and host and affect parasitism levels. However, to investigate the effect of streamflow, rainfall and its mechanisms, it is important to control host traits that can also influence parasitism levels in freshwater systems. Here, we used a cymothoid‐palaemonid prawn model to test the combined effects of streamflow and rainfall accounting for host traits to predict variations in parasite loads in stream ecosystems. We collected the palaemonid prawns monthly for 2 years and measured the variation of rainfall and streamflow (i.e. stream discharge) at the time of prawn sampling. Our best model showed that streamflow can predict parasitism levels in prawns better than host traits. We found a higher prevalence and abundance of parasites in reduced streamflow compared to increased ones. We also found higher ectoparasite loads in females rather than males and occurring in autumn and winter rather than in spring and summer. Our results also showed that ectoparasite loads also suffer an effect of host body size, sex and moult stage, but not host age. Our findings show that reduced streamflow can facilitate host finding that favours parasite transmission in dry seasons and increase parasitism levels in stream systems. Temporal variability in streamflow can have a strong influence on parasite loads and dynamics compared to host traits in stream ecosystems. Identifying how interactions between aquatic invertebrates and parasites respond to variability in the hydrological regime can help to better understand and predict disease outbreaks as habitat reduction and disturbance continue in stream ecosystems.

The Use of AI in Predicting Disease Outbreaks

The rapid and unpredictable emergence of infectious diseases continues to pose a significant threat to global health, necessitating more advanced prediction and prevention strategies. The use of Artificial Intelligence (AI) in predicting disease outbreaks has emerged as a powerful tool to navigate the complex biological, environmental, and sociological factors contributing to these outbreaks. AI models, particularly those leveraging machine learning, can analyze vast datasets, detect patterns, and predict the spread of diseases with improved accuracy compared to traditional methods. This paper examines the role of AI in early disease outbreak prediction, current prediction methodologies, and the applications of AI in outbreak forecasting, while also discussing the challenges and limitations associated with AI-driven models. Keywords: Artificial Intelligence, disease outbreaks, machine learning, epidemic prediction, big data analytics.

High-performance simulation of disease outbreaks in growing-finishing pig herds raised by the precision feeding method

Perturbations always affect livestock during the breeding process, including harmful diseases. Researching the impact of disease outbreaks on pig herds is extremely important so that disease control measures can be applied early. However, conducting practical experiments on disease outbreaks is extremely expensive. Precision feeding systems (PFS) for pigs use data on the animal’s own feed intake to calculate the appropriate amount of feed for each individual. This helps increase productivity and product quality while contributing to reducing waste generation in the environment. Daily feed intake (DFI) and cumulative feed intake (CFI) data can be automatically collected and estimated from the PFS, which can help detect or predict disease outbreaks. In this article, we introduce an advanced simulation model of the PFS for pigs and the integration of disease outbreak models into this system. A disease outbreak simulation application within the pig herd raised by the precision feeding method is also developed for running high-performance experimental simulations. The results of the simulation scenarios are analyzed and compared with data from a real-world experiment to assess the accuracy of the application. The correlation coefficient values of DFI in all scenarios fall within the range of 0.25 to 0.5, suggesting almost no correlation between simulated DFI and actual DFI. The overall average correlation coefficient of CFI for all scenarios is 0.764, falling within the strong correlation range. It can be concluded that the simulation accurately represents CFI values compared to reality.

Integrating the exposome and one health approach to national health surveillance: an opportunity for Latin American countries in health preventive management.

The exposome approach, emphasizing lifelong environmental exposures, is a holistic framework exploring the intricate interplay between genetics and the environment in shaping health outcomes. Complementing this, the one health approach recognizes the interconnectedness of human and ecological health within a shared ecosystem, extending to planetary health, which encompasses the entire planet. Integrating Disease Surveillance Systems with exposome, one health, and planetary health signifies a paradigm shift in health management, fostering a comprehensive public health framework. This publication advocates for combining traditional health surveillance with exposome and one health/planetary health approach, proposing a three-step approach: ecological analysis, territorial intervention in identified issues, and an analytical phase for assessing interventions. Particularly relevant for Latin American countries facing a double burden of diseases, integrating the exposome into traditional health surveillance proves cost-effective by leveraging existing data and environmental measurements. In conclusion, the integration of exposome and one health approaches into traditional health surveillance presents a robust framework for monitoring population health, especially in regions like Latin America with complex health challenges. This innovative approach enables tailored interventions, disease outbreak predictions, and a holistic understanding of the intricate links between human health and the environment, offering substantial benefits for public health and disease prevention despite existing challenges.

Disease outbreak prediction using natural language processing: a review

Disease outbreak prediction using natural language processing: a review

ARTIFICIAL INTELLIGENCE IN CLINICAL APPLICATIONS FOR INFECTIOUS DISEASES: DIAGNOSIS, TREATMENT AND IMMUNIZATION

Despite scientific and technological advances in recent years, infectious diseases continue to pose a significant threat to public health. These diseases can cause serious health problems as they have the potential to spread rapidly. In addition, they occur in the form of epidemics and affect populations. The difficulty of rapid and accurate diagnosis and increasing antimicrobial resistance create difficulties in the treatment of infectious diseases. Artificial intelligence technology has developed useful applications in many areas such as the development of diagnosis and treatment methods, anti-infective drug and vaccine discovery, and prevention of increasing anti-infective drug resistance. In particular, AI-assisted clinical decision support systems can help predict disease outbreaks, support diagnosis of diseases, optimise treatment options and monitor epidemiological trends by analysing large datasets. It can also provide more accurate and faster results in analysing diagnostic images and identifying diseases. Advances in this field need to be supported by multidisciplinary studies and a strong ethical framework. In this review, we outline approaches to the application and use of artificial intelligence in infectious diseases, highlight the progress supported by artificial intelligence, and discuss how it can be used. We outline the applications and benefits of AI in infectious diseases. In this way, more effective intervention strategies can be developed to control infectious diseases and protect public health.

Data analytics for predicting disease outbreaks: A review of models and tools

The burgeoning field of data analytics has emerged as a pivotal force in the realm of public health, particularly in the context of predicting and mitigating disease outbreaks. This comprehensive review delves into the diverse landscape of models and tools employed in data analytics for disease outbreak prediction. With a focus on synthesizing existing knowledge, the paper aims to provide a nuanced understanding of the strengths, limitations, and future directions within this dynamic field. The review begins with an exploration of various models utilized for disease outbreak prediction, ranging from statistical approaches to machine learning models and epidemiological frameworks. Each model category is scrutinized for its efficacy in capturing the complexities inherent in infectious disease dynamics. Simultaneously, the paper investigates the array of tools and technologies leveraged in disease outbreak prediction, encompassing Geographic Information Systems (GIS), data visualization tools, and big data analytics platforms. A critical aspect of the review lies in the examination of diverse data sources contributing to predictive analytics. Epidemiological data, environmental factors, and the burgeoning influence of social media and web data are dissected for their roles in enhancing the accuracy and timeliness of outbreak predictions. Amidst the promises of data analytics, the paper navigates the challenges inherent in predicting disease outbreaks. Issues of data quality and availability, model complexity, interpretability, and ethical considerations are dissected, providing a holistic view of the hurdles that practitioners encounter. Drawing upon case studies and real-world applications, the review showcases instances where data analytics has proven successful in predicting disease outbreaks, shedding light on both triumphs and setbacks. The implications for public health, lessons learned from challenges, and the evolving role of data analytics in shaping global health preparedness are thoroughly discussed. As the paper unfolds, it illuminates future trends and innovations in the field, foreseeing the integration of advanced technologies, global collaboration for information sharing, and the adaptation of predictive analytics for emerging diseases. The review culminates in a comprehensive conclusion, summarizing key findings and emphasizing the potential transformative impact of data analytics on the landscape of disease outbreak prediction.

THE IMPACT OF BIG DATA ON HEALTHCARE PRODUCT DEVELOPMENT: A THEORETICAL AND ANALYTICAL REVIEW

The intersection of big data and healthcare product development has catalyzed transformative shifts in the industry, revolutionizing how medical solutions are conceptualized, designed, and deployed. This theoretical and analytical review explores the profound impact of big data on healthcare product development, elucidating its implications across various facets of the healthcare landscape. Utilizing big data analytics, healthcare stakeholders can harness vast volumes of structured and unstructured data to derive actionable insights. These insights inform evidence-based decision-making processes, driving innovation in product development pipelines. By analyzing real-time patient data, trends, and treatment outcomes, developers gain invaluable insights into disease progression, treatment efficacy, and patient preferences, thus facilitating the creation of tailored, patient-centric solutions. Moreover, big data analytics play a pivotal role in improving patient outcomes and quality of care. Through predictive analytics and machine learning algorithms, healthcare providers can identify at-risk populations, predict disease outbreaks, and personalize treatment plans. This proactive approach enhances preventive care strategies and minimizes healthcare costs by averting complications and hospital readmissions. However, the integration of big data into healthcare product development is not without challenges. Data privacy and security concerns necessitate robust frameworks to safeguard sensitive patient information. Moreover, regulatory compliance frameworks must evolve to accommodate the complexities of big data analytics while ensuring patient safety and data integrity. Despite these challenges, the potential of big data in healthcare product development is vast. By leveraging big data analytics, stakeholders can bridge gaps in healthcare access and equity, tailor interventions to underserved populations, and optimize resource allocation. In conclusion, this review underscores the transformative impact of big data on healthcare product development. By embracing data-driven approaches, stakeholders can drive innovation, enhance patient outcomes, and navigate the evolving healthcare landscape with agility and efficacy.
 Keywords: Big Data, Healthcare Product Development, Innovation, Patient Outcomes, Data Analytics, Regulatory Compliance.

ICE-VDOP: an integrated clustering and ensemble machine learning methods for an enhanced vector-borne disease outbreak prediction using climatic variables

ICE-VDOP: an integrated clustering and ensemble machine learning methods for an enhanced vector-borne disease outbreak prediction using climatic variables

Developing public health surveillance dashboards: a scoping review on the design principles

BackgroundPublic Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards.MethodologyThis scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted.ResultsFindings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure.ConclusionEffective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.

Refining the Occurrence of Viral Encephalopathy and Retinopathy, Photobacteriosis, and Vibriosis in Connection with Seawater Physicochemical Parameters: A Five-Year Case Study

The marine environment and its physicochemical parameters play a pivotal role in the context of fish farming. Fundamental physicochemical factors of water, including temperature (T°C), salinity (S psu), dissolved oxygen (D.O. mg/l), and pH, exert a profound influence on the physiology of farmed aquatic animals and the composition of microbial communities. This study aimed to establish correlations between documented disease occurrences in sea bass intensive rearing and the prevailing physicochemical parameters of water. The objective was to analyse the significance of these parameters in disease outbreaks and develop a predictive model for the outbreak risk of key diseases. To this end, real-time physicochemical data were collected daily over five years (2011-2015) from two fish farms (A and B) located in the Gulf of Argostoli, island of Cephalonia, Western Greece. The focus was on incidences of Viral Encephalopathy and Retinopathy (VER), Photobacteriosis caused by Photobacterium damselae subsp. piscicida (PHDP) and Vibriosis caused by Vibrio anguillarum serotype O1 (VAO1). Statistically significant correlations were found between increasing water temperature and salinity and the occurrence of VER, PHDP, and VAO1 diseases. The reduction of dissolved oxygen, when combined with other parameters, synergistically triggered the manifestation of these three diseases. The study’s findings were discussed within the context of their potential to predict disease outbreaks based on fluctuations in the physicochemical parameters of water and anticipated climate change scenarios.

A New Statistical Method to Detect Disease Outbreaks from Hospital Emergency Department Data.

Early detection and prediction of disease outbreaks are crucial for public health service delivery, containment response, saving patient lives, and reducing costs. We propose a new data-driven statistical methodology for outbreak detection and prediction based on routinely collected hospital Emergency Department data. The time between consecutive ED presentations matching a diagnosis of interest forms the basis of a novel index measure to signal that an outbreak has occurred. We validate the method using historical presentations of influenza-like illness made to a large sample of public hospital EDs in 2020 and compare outbreaks identified by the method with the start of the first wave of COVID-19. The method shows promise within the field of disease outbreak detection.

Hand contamination and hand hygiene knowledge and practices among commercial transport users after the SARS-CoV-2 virus (COVID-19) scare, Enugu State, Nigeria.

Contaminated hands are one of the most common modes of microorganism transmission that are responsible for many associated infections in healthcare, food industries, and public places such as transportation parks. Public health approaches during COVID-19 pandemic have shown that hand hygiene practices and associated knowledge are critical measure to control the spread of infectious agent. Hence, assessment of commercial transport users' knowledge, belief and practices on hand hygiene, and potential contamination with infectious agents which is the aim of the study, aligns with general health concern of quantifying contamination risk levels to predict disease outbreaks. This study utilized a randomized sampling approach to select 10 frequently used commercial parks within two districts in the State: Enugu and Nsukka. The parameters analysed include a cross-sectional questionnaire survey, hand swab and hand washed samples collected from dominant hand of participants. A total of 600 participants responded to the questionnaire survey, while 100 participants' hand swabs were examined for microbial contamination. This study recorded a high prevalence of fungal (90.0%) and bacterial (87.0%) species; 20 species of fungus were identified with prevalence range of 1% to 14%; 21 bacterial species were isolated with prevalence range of 1% to 16%. These species were identified as either opportunistic, non-invasive, or pathogenic, which may constitute a health concern amongst immunocompromised individuals within the population. Aspergillus spp. (14%), was the most common fungal species that was exclusively found amongst Nsukka commercial users, while E. coli was the most prevalent isolated bacterial species amongst Nsukka (12%) and Enugu (20%) commercial park users. Prevalence of fungal contamination in Nsukka (94.0%; 47/50) and Enugu (86.0%; 43/50) were both high. Prevalence of bacterial contamination was higher in Enugu than Nsukka but not significantly (47[94.0%] vs. 40[80.0%], p = 0.583). A greater number of participants (99.3%) were aware of the importance of hand hygiene, however with low compliance rate aside "after using the toilet" (80%) and "before eating" (90%), other relevant hand washing and sanitizing practices were considered less important. With these observations, we can emphatically say that despite the COVID-19 scare, commercial park users within the sampled population do not efficiently practice quality hand wash and hygiene measures, hence, risking the widespread of infectious agents in situation of disease outbreak or among immunocompromised individuals.

Making waves: The benefits and challenges of responsibly implementing wastewater-based surveillance for rural communities

The sampling and analysis of sewage for pathogens and other biomarkers offers a powerful tool for monitoring and understanding community health trends and potentially predicting disease outbreaks. Since the early months of the COVID-19 pandemic, the use of wastewater-based testing for public health surveillance has increased markedly. However, these efforts have focused on urban and peri‑urban areas. In most rural regions of the world, healthcare service access is more limited than in urban areas, and rural public health agencies typically have less disease outcome surveillance data than their urban counterparts. The potential public health benefits of wastewater-based surveillance for rural communities are therefore substantial – though so too are the methodological and ethical challenges. For many rural communities, population dynamics and insufficient, aging, and inadequately maintained wastewater collection and treatment infrastructure present obstacles to the reliable and responsible implementation of wastewater-based surveillance. Practitioner observations and research findings indicate that for many rural systems, typical implementation approaches for wastewater-based surveillance will not yield sufficiently reliable or actionable results. We discuss key challenges and potential strategies to address them. However, to support and expand the implementation of responsible, reliable, and ethical wastewater-based surveillance for rural communities, best practice guidelines and standards are needed.

Mathematical simulations and sensitivity visualization of fractional order disease model describing human immunodeficiency

In this study, we explore a mathematical model of human immunodeficiency virus type 1 (HIV-1) infection in CD4+ T-cells, considering fractional-order dynamics in the Caputo sense. Fractional models are of great significance due to their capacity to predict disease outbreaks while incorporating memory and non-local characteristics. To establish the existence and uniqueness of solutions for the fractional model, a fixed-point theorem and an iterative technique are employed. Furthermore, it is confirmed that the solutions of the model are both positive and bounded. The basic reproduction number, denoted as R0, is calculated using the next-generation matrix approach, and the equilibrium points of the model, including disease-free and endemic equilibrium points, are presented. A sensitivity analysis of R0 is performed by varying different parameters. To analyze the local stability of equilibrium points, the Routh-Hurwitz technique is employed. The global stability of equilibrium points is demonstrated using the Lyapunov function and LaSalle's principle, as well as through UlamHyers (UH) stability and the generalized UH stability conditions. The proposed numerical scheme Adams-Bashforth predictor-corrector is validated by comparing it with the fourth-order Runge-Kutta (RK4) technique. We examine the influence of the fractional order (ξ) by conducting numerical simulations for different values of ξ using the Adams-Bashforth predictor-corrector approach. Furthermore, graphical presentations illustrate how different crucial model parameters impact disease dynamics. The results obtained from this study demonstrate that the adopted strategies significantly improve prediction accuracy.

Unraveling Microblog Sentiment Dynamics: A Twitter Public Attitudes Analysis towards COVID-19 Cases and Deaths

The identification and analysis of sentiment polarity in microblog data has drawn increased attention. Researchers and practitioners attempt to extract knowledge by evaluating public sentiment in response to global events. This study aimed to evaluate public attitudes towards the spread of COVID-19 by performing sentiment analysis on over 2.1 million tweets in English. The implications included the generation of insights for timely disease outbreak prediction and assertions regarding worldwide events, which can help policymakers take suitable actions. We investigated whether there was a correlation between public sentiment and the number of cases and deaths attributed to COVID-19. The research design integrated text preprocessing (regular expression operations, (de)tokenization, stopwords), sentiment polarization analysis via TextBlob, hypothesis formulation (null hypothesis testing), and statistical analysis (Pearson coefficient and p-value) to produce the results. The key findings highlight a correlation between sentiment polarity and deaths, starting at 41 days before and expanding up to 3 days after counting. Twitter users reacted to increased numbers of COVID-19-related deaths after four days by posting tweets with fading sentiment polarization. We also detected a strong correlation between COVID-19 Twitter conversation polarity and reported cases and a weak correlation between polarity and reported deaths.

Predicting Infectious Disease Outbreaks with Machine Learning and Epidemiological Data

Over the past several years, there has been a notable shift in the international public health arena, mostly driven by the use of machine learning methodologies and epidemiological data for the purpose of forecasting and controlling outbreaks of infectious diseases. This study explores the changing paradigm of disease outbreak prediction by examining current advancements and emerging patterns in the field of machine learning and epidemiology. In this paper, we explore the complex procedure of forecasting infectious disease outbreaks, a task of significant significance for global public health authorities. This paper examines the crucial role of machine learning algorithms in this undertaking, elucidating their capacity to analyze extensive and heterogeneous datasets in order to produce significant insights and predictions Our inquiry spans multiple facets of this complex topic. This study examines the transformative impact of machine learning models, namely deep learning and ensemble approaches, on the field. The individuals in question have exhibited remarkable proficiency in recognizing patterns, establishing correlations, and formulating predictions by utilizing past data. Consequently, this has greatly contributed to the prompt identification and readiness for potential outbreaks. Moreover, our study involves the incorporation of epidemiological data, including case reports, genetic sequencing, and population dynamics, into the machine learning architecture. This study investigates the enhanced predictive accuracy and improved comprehension of disease dynamics resulting from the integration of data-driven models and expert knowledge from the field of epidemiology. The integration of different approaches is of utmost importance when it comes to effectively tackling the distinct characteristics and problems presented by diverse infectious illnesses. Additionally, the research emphasizes the significance of incorporating a wide range of data sources, including not only data related to human health, but also environmental factors, socio-economic metrics, and patterns of human mobility. Non-conventional data sources provide essential contextual information for comprehending the dynamics of disease transmission, hence enhancing the robustness and comprehensiveness of forecasts.

Transformative Approaches in Integrating Data Science for Disease Outbreak Prediction: A Comprehensive Survey in Epidemiology

Transformative Approaches in Integrating Data Science for Disease Outbreak Prediction: A Comprehensive Survey in Epidemiology