Enhancing outbreak early warning surveillance in resource-limited Pacific island countries and territories

Comprehensive, timely, and accurate health data are essential for the detection of outbreak-prone diseases. If these go unnoticed or are identified late, they pose significant risks to the health of a population. In the Pacific islands, a syndrome-based surveillance strategy, known as the Pacific Syndromic Surveillance System (PSSS), is employed for the early detection of outbreaks. The PSSS, implemented in 2010, has provided a mechanism by which resource-limited Pacific island governments have been able to perform routine surveillance activities and address many of their national and international health protection needs and obligations. Despite being a cornerstone of health protection for many Pacific islands, the surveillance system had not been comprehensively evaluated. Nor had behavioural, technical, or upstream health system factors that influence its performance been investigated. This thesis assesses whether the PSSS is meeting its stated objectives and produces evidence to augment technical and operational elements of the system. The thesis answers the following questions: (i) is the PSSS meeting its stated objectives? (ii) are algorithm-based approaches to outbreak detection appropriate in the Pacific island systems and context?; (iii) how can the PSSS be enhanced to meet information needs during public health emergencies?; and (iv) what factors enable and constrain surveillance nurses’data collection and reporting practice? The thesis found that the surveillance system is simple, well regarded, trusted, and context-relevant mechanism that Pacific island governments from across the development spectrum have been able to adopt and maintain with minimal external technical or financial support. Despite these positive findings, the research identified several statistical, procedural, and broader systems barriers to optimal performance, including chronic staffing and other resource constraints, insufficient data on which to base outbreak detection analysis, and poor integration of health information systems. Looking ahead, the thesis identifies practical opportunities for system improvement and highlights areas for further research

Improving Antibiotic Resistant Infection Transmission Situational Awareness in Enclosed Facilities with a Novel Graphical User Interface for Tactical Biosurveillance

Serious challenges associated with antibiotic resistant infections (ABRIs) force healthcare practitioners (HCP) to seek innovative approaches that will slow the emergence of new ABRIs and prevent their spread. It was realized that traditional approaches to infection prevention based on education, retrospective reports, and biosurveillance often fail to ensure reliable compliance with infection prevention guidelines and real-time problem solving. The objective of this original research was to develop and test the conceptual design of a situational awareness (SA)-oriented information system for coping with healthcare-associated infection transmission. Constantly changing patterns in spatial distribution of patients, prevalence of infectious cases, clustering of contacts, and frequency of contacts may compromise the effectiveness of infection prevention and control in hospitals. It was hypothesized that providing HCPs with a graphical user interface (GUI) to visualize spatial information on the risks of exposure to ABRIs would effectively increase HCPs’ SA. Increased SA may enhance biosurveillance and result in tactical decisions leading to better patient outcomes. The study employed a mixed qualitative-quantitative research method encompassing conceptualization of GUI content, transcription of electronic health record and biosurveillance data into GUI visual artifacts, and evaluation of the GUI’s impact on HCPs’ perception and comprehension of the conditions that increase the risk of ABRI transmission. The study provided pilot evidence that visualization of spatial disease distribution and spatially-linked exposures and interventions significantly increases HCPs’ SA when compared to current practice. The research demonstrates that the SA-oriented GUI enables the HCPs to promptly answer the question, “At a given location, what are the risks of infection transmission there?” This research provides a new form of medical knowledge representation for spatial population-based decision-making within enclosed environments. The next steps include rapid application development and further hypothesis testing concerning the impact of this GUI on decsion-making

Transfer Learning for Bayesian Case Detection Systems

In this age of big biomedical data, a variety of data has been produced worldwide. If we could combine that data more effectively, we might well develop a deeper understanding of biomedical problems and their solutions. Compared to traditional machine learning techniques, transfer learning techniques explicitly model differences among origins of data to provide a smooth transfer of knowledge. Most techniques focus on the transfer of data, while more recent techniques have begun to explore the possibility of transfer of models. Model-transfer techniques are especially appealing in biomedicine because they involve fewer privacy risks. Unfortunately, most model-transfer techniques are unable to handle heterogeneous scenarios where models differ in the features they contain, which occur commonly with biomedical data. This dissertation develops an innovative transfer learning framework to share both data and models under a variety of conditions, while allowing the inclusion of features that are unique to and informative about the target context. I used both synthetic and real-world datasets to test two hypotheses: 1) a transfer learning model that is learned using source knowledge and target data performs classification in the target context better than a target model that is learned solely from target data; 2) a transfer learning model performs classification in the target context better than a source model. I conducted a comprehensive analysis to investigate conditions where these two hypotheses hold, and more generally the factors that affect the effectiveness of transfer learning, providing empirical opinions about when and what to share. My research enables knowledge sharing under heterogeneous scenarios and provides an approach for understanding transfer learning performance in terms of differences of features, distributions, and sample sizes between source and target. The model-transfer algorithm can be viewed as a new Bayesian network learning algorithm with a flexible representation of prior knowledge. In concrete terms, this work shows the potential for transfer learning to assist in the rapid development of a case detection system for an emergent unknown disease. More generally, to my knowledge, this research is the first investigation of model-based transfer learning in biomedicine under heterogeneous scenarios

Emerg Infect Dis

Emerging Infectious Diseases is providing access to these abstracts on behalf of the ICEID 2022 program committee (http://www.iceid.org), which performed peer review. ICEID is organized by the Centers for Disease Control and Prevention and Task Force for Global Health, Inc.Emerging Infectious Diseases has not edited or proofread these materials and is not responsible for inaccuracies or omissions. All information is subject to change. Comments and corrections should be brought to the attention of the authors.Suggested citation: Authors. Title [abstract]. International Conference on Emerging Infectious Diseases 2022 poster and oral presentation abstracts. Emerg Infect Dis. 2022 Sep [date cited]. http://www.cdc.gov/EID/pdfs/ICEID2022.pdf2022PMC94238981187

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI

Long-Term Spatiotemporal Changes in Endemic Threshold Populations in England and Wales – A Multi-Disease Study

Metapopulation dynamics play a critical role in driving endemic persistence and transmission of childhood infections. The endemic threshold is defined as the minimum population size required to sustain a continuous chain of infection transmission. The concept is fundamental to the implementation of effective vaccine-based disease control programmes. Vaccination serves to increase endemic threshold population size, promoting disease fadeout and eventual elimination of infection. To date, empirical geographical investigations of endemic threshold populations have tended to focus on isolated populations in island communities. Few studies have examined endemic threshold dynamics in ‘mainland’ regional populations with divergent spatial structures and varying levels of connectivity between subpopulations. This thesis presents a geographical analysis of spatiotemporal changes in endemic threshold populations for three childhood infections (measles, pertussis and scarlet fever) in two regional metapopulations of England and Wales: Lancashire and South Wales. Drawing upon weekly disease records of the Registrar-General of England and Wales over a 30-year period (January 1940–December 1969), empirical regression techniques were used to estimate the endemic threshold populations for childhood infections in the two study regions. Hotspot and survival analyses were performed to compare disease fadeout duration and probability for both regions in the pre-vaccine and vaccine eras, respectively. Endemic-epidemic modelling was undertaken to identify and analyse potential drivers of disease persistence. The findings reveal strong regional differences in estimates of endemic threshold populations over time and space for all three childhood infections. Regional differences in endemic threshold populations reflect significant regional variations in spatial connectivity, population dispersion and level of geographical isolation. Significant growth in fadeout duration was observed in the vaccine era for pertussis non-hotspots in both regions, consistent with geographical synchronisation of epidemic activity

Long-Term Spatiotemporal Changes in Endemic Threshold Populations in England and Wales – A Multi-Disease Study

Metapopulation dynamics play a critical role in driving endemic persistence and transmission of childhood infections. The endemic threshold is defined as the minimum population size required to sustain a continuous chain of infection transmission. The concept is fundamental to the implementation of effective vaccine-based disease control programmes. Vaccination serves to increase endemic threshold population size, promoting disease fadeout and eventual elimination of infection. To date, empirical geographical investigations of endemic threshold populations have tended to focus on isolated populations in island communities. Few studies have examined endemic threshold dynamics in ‘mainland’ regional populations with divergent spatial structures and varying levels of connectivity between subpopulations. This thesis presents a geographical analysis of spatiotemporal changes in endemic threshold populations for three childhood infections (measles, pertussis and scarlet fever) in two regional metapopulations of England and Wales: Lancashire and South Wales. Drawing upon weekly disease records of the Registrar-General of England and Wales over a 30-year period (January 1940–December 1969), empirical regression techniques were used to estimate the endemic threshold populations for childhood infections in the two study regions. Hotspot and survival analyses were performed to compare disease fadeout duration and probability for both regions in the pre-vaccine and vaccine eras, respectively. Endemic-epidemic modelling was undertaken to identify and analyse potential drivers of disease persistence. The findings reveal strong regional differences in estimates of endemic threshold populations over time and space for all three childhood infections. Regional differences in endemic threshold populations reflect significant regional variations in spatial connectivity, population dispersion and level of geographical isolation. Significant growth in fadeout duration was observed in the vaccine era for pertussis non-hotspots in both regions, consistent with geographical synchronisation of epidemic activity

Temas de actualidad en salud pública

La salud pública es una disciplina importante para la salud en el mundo. Se define como la ciencia y el arte de prevenir las enfermedades, prolongar la vida y promover la salud a través de los esfuerzos organizados y decisiones con conocimiento de la sociedad, las organizaciones (públicas y privadas), las comunidades y los individuos, esta disciplina se ha renovado con la incorporación de múltiples actores, profesiones, áreas de conocimiento, además de ser afectados y promovido por múltiples tecnologías, en particular los de información. Como un campo cambiante del conocimiento, la salud pública requiere la información basada en la evidencia y actualizaciones regulares, más aún en el contexto de un mundo en transición epidemiológica. "Temas actuales en la salud pública", presenta información actualizada sobre varios temas relacionados con las áreas reales de interés en esta ciencia médica creciente y emocionante, con la concepción y la filosofía que estamos trabajando para mejorar la salud de la población, en lugar que el tratamiento de las enfermedades de los pacientes individuales, la toma de decisiones sobre el cuidado de la salud colectiva que se basan en la mejor evidencia disponible, actualizada, válida y pertinente, y, finalmente, en el contexto de los recursos disponibles. La salud pública debe ser una ciencia compleja, ayudando en las decisiones, acciones y cambios en la salud del mundo. En una sociedad globalizada esto se hizo hincapié no sólo en una nación en particular, sino en todo el mundo.Public health is a major health discipline in the world. Defined as the science and art of preventing diseases, prolonging life and promoting health through the organized efforts and informed choices of the society, organizations (public and private), communities and individuals, this discipline has been renewed by the incorporation of multiple actors, professions, knowledge areas, as well as being impacted and promoted by multiple technologies, particularly information ones. As a changing field of knowledge, public health requires evidence-based information and regular updates, even more in the context of a world in epidemiological transition. Health impacts of climate change are currently in the quest of the Millennium Development Goals, and most of them are related to the activities of public health. “Current Topics in Public Health” presents updated information on multiple topics related to actual areas of interest in this growing and exciting medical science, with the conception and philosophy that we are working to improve the health of the population, rather than treating diseases of individual patients; taking decisions about collective health care that are based on the best available, current, valid and relevant evidence; and finally within the context of available resources. Public health should be a complex science helping in the decision, actions and changes in the health of the world. In a globalized society this is emphasized not just in a particular nation but in the whole world