Prediction Of Disease Outbreaks Research Articles

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

Machine learning in drug supply chain management during disease outbreaks: a systematic review

<span lang="EN-US">The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks.</span>

육계농장의 ICT장비가 생산한 빅데이터 분석을 활용한 질병발생예측모델(DOPM) 개발연구

Since 2014, the government has promoted the ICT equipment support project for livestock farmers as an alternative system to address the rapid aging of livestock farmers and sharp decline in labor force due to population decline. This project aims to realize more precise and scientific smart livestock farming through the analysis of big data produced by ICT equipment in real time. Prior to the introduction of ICT equipment, farmers relied on their long-term experience in farm operations to implement emergency measures by using a sudden increase in the number of dead birds or sudden change in the amount of water consumption data as an indicator of the occurrence of disease. However, damage and loss to farmers have not been reduced with these measures. Therefore, this study aims to address the farmers' consistent desire to develop a disease outbreak prediction model (DOPM) that can be easily utilized in the field and has high precision. The current study developed a disease outbreak prediction model (DOPM) that can find a singularity (golden time) in the data even if only one type of ICT data (Water & Feed) is properly monitored and analyzed. We found that if the time when a sharp decrease (more than 100% decrease from the previous day), confirmed through the daily feed intake data for two consecutive days (golden time = DOPM), is set as the start of the disease outbreak and preventive measures are taken quickly, this model can be used to minimize farm losses and damages better than the existing method that relies on analog data and experience. Additionally, the DOPM was confirmed to be the fastest, most sensitive, and most accurate model in the field poultry farms. As a result, we have found a method to interpret non-verbal signals of the chickens with the transformed ICT-Data in the AI-age.

Utilization of GeoAI Applications in the Health Sector: A Review

This research describes the use of GeoAI, a geospatial data-based artificial intelligence, to improve the understanding and management of health in a global context. GeoAI enables the integration of geographic data such as maps, satellite images, and environmental information with artificial intelligence technology to analyze disease spread, health risk factors, and health resource management more accurately. This research uses a descriptive qualitative approach. The type of research used is a literature study. The literature review database used is by searching on Google Scholar, Scopus, and Google Book. The results of this study show that the basic concept of GeoAI involves more accurate spatial analysis, disease spread monitoring, disease outbreak prediction, and more efficient health resource management. However, challenges such as access to adequate data, lack of understanding among health professionals, and data privacy and security issues need to be addressed for GeoAI to be effectively implemented. In conclusion, GeoAI has great potential in improving public health and addressing global health challenges, but requires careful steps in its implementation.

Quantitative measurement of influenza virus transmission in animal model: an overview of current state.

Influenza virus transmission is a crucial factor in understanding the spread of the virus within populations and developing effective control strategies. Studying the transmission patterns of influenza virus allows for better risk assessment and prediction of disease outbreaks. By monitoring the spread of the virus and identifying high-risk populations and geographic areas, it is possible to allocate resources more effectively, implement timely interventions, and provide targeted healthcare interventions to diminish the burden of influenza virus on vulnerable populations. Theoretical models of virus transmission are used to study and simulate of influenza virus spread within populations. These models aim to capture the complex dynamics of transmission, including factors such as population size, contact patterns, infectiousness, and susceptibility. Animal models serve as valuable tools for studying the dynamics of influenza virus transmission. This article presents a brief overview of existing research on the qualitative and quantitative study of influenza virus transmission in animal models. We discuss the methodologies employed, key insights gained from these studies, and their relevance.

Enhancing the Predictive Power of Google Trends Data Through Network Analysis: Infodemiology Study of COVID-19.

The COVID-19 outbreak has revealed a high demand for timely surveillance of pandemic developments. Google Trends (GT), which provides freely available search volume data, has been proven to be a reliable forecast and nowcast measure for public health issues. Previous studies have tended to use relative search volumes from GT directly to analyze associations and predict the progression of pandemic. However, GT's normalization of the search volumes data and data retrieval restrictions affect the data resolution in reflecting the actual search behaviors, thus limiting the potential for using GT data to predict disease outbreaks. This study aimed to introduce a merged algorithm that helps recover the resolution and accuracy of the search volume data extracted from GT over long observation periods. In addition, this study also aimed to demonstrate the extended application of merged search volumes (MSVs) in combination of network analysis, via tracking the COVID-19 pandemic risk. We collected relative search volumes from GT and transformed them into MSVs using our proposed merged algorithm. The MSVs of the selected coronavirus-related keywords were compiled using the rolling window method. The correlations between the MSVs were calculated to form a dynamic network. The network statistics, including network density and the global clustering coefficients between the MSVs, were also calculated. Our research findings suggested that although GT restricts the search data retrieval into weekly data points over a long period, our proposed approach could recover the daily search volume over the same investigation period to facilitate subsequent research analyses. In addition, the dynamic time warping diagrams show that the dynamic networks were capable of predicting the COVID-19 pandemic trends, in terms of the number of COVID-19 confirmed cases and severity risk scores. The innovative method for handling GT search data and the application of MSVs and network analysis to broaden the potential for GT data are useful for predicting the pandemic risk. Further investigation of the GT dynamic network can focus on noncommunicable diseases, health-related behaviors, and misinformation on the internet.

Climate change and cutaneous leishmaniasis in the province of Ghardaïa in Algeria: A model-based approach to predict disease outbreaks.

Cutaneous leishmaniasis (CL) is a vector-borne disease prevalent in Algeria since 2000. The disease has significant impacts on affected communities, including morbidity and social stigma. Investigate the association between environmental factors and the incidence of CL in the province of Ghardaïa and assess the predictive capacity of these factors for disease occurrence. Retrospective SETTING: The study area included both urban and rural communities. We analyzed a dataset on CL in the province of Ghardaïa, Algeria, spanning from 2000 to 2020. The dataset included climatic variables such as temperature, average humidity, wind speed, rainfall, and the normalized difference vegetation index (NDVI). Using generalized additive models, we examined the relationships and interactions between these variables to predict the emergence of CL in the study area. The identification of the most significant environmental factors associated with the incidence and the predicted incidence rates of CL in the province of Ghardaïa, Algeria. 252 monthly observations of both climatic and epidemiological variables. Relative humidity and wind speed were the primary climatic factors influencing the occurrence of CL epidemics in Ghardaïa, Algeria. Additionally, NDVI was a significant environmental factor associated with CL incidence. Surprisingly, temperature did not show a strong effect on CL occurrence, while rainfall was not statistically significant. The final fitted model predictions were highly correlated with real cases. This study provides a better understanding of the long-term trend in how environmental and climatic factors contribute to the emergence of CL. Our results can inform the development of effective early warning systems for preventing the transmission and emergence of vector-borne diseases. Incorporating additional reservoir statistics such as rodent density and a human development index in the region could improve our understanding of disease transmission.

Soil Conditions an Effective Model Predicts Plant Diseases

Plant diseases can cause significant economic losses for farmers and threaten global food security. Traditional approaches for managing plant diseases, such as using chemical pesticides and fungicides, are often expensive, environmentally harmful, and unsustainable. Leveraging the efficiency of soil conditions to predict plant diseases offers a promising alternative to traditional approaches. In this study, we developed a machine learning-based approach to predict the likelihood and severity of plant diseases based on soil conditions. Soil samples were collected from agricultural fields with healthy and diseased plants, and analyzed for various soil properties, including pH, texture, and nutrient availability, as well as microbial communities. A machine learning model was developed using the soil and disease data, and the model's effectiveness was evaluated in a field trial. The results showed that the machine learning-based approach had high accuracy in predicting disease outbreaks based on soil conditions. This research provides valuable insights into the relationship between soil conditions and plant diseases and offers a sustainable and cost-effective approach for managing plant diseases.

Addressing bias in artificial intelligence for public health surveillance

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the timeliness and robustness of health data. NLP techniques have been...