Progress in practice of infectious disease epidemiology in China

European Collaboration in Infectious Diseases Surveillance: Where to Go?

Unlocking pathogen genotyping information for public health by mathematical modeling

BackgroundInfectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling.ObjectiveThe aim of this study was to synthesize research and identify hotspots of big data in infectious disease epidemiology.MethodsBibliometric data from 3054 documents that satisfied the inclusion criteria retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents.ResultsThe bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. The analysis also placed US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes.ConclusionsProposals for future studies are made based on these findings. This study will provide health care informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.

Geographic Information Systems (GIS) represent a key tool in modern research in veterinary epidemiology, enabling precise integration, analysis, and visualization of spatial data related to the spread and control of infectious animal diseases. QGIS is a free and open-source software platform used for map creation, spatial analysis, and data processing relevant to the surveillance and control of infectious diseases in domestic animals. The application of QGIS technology enables the identification of spatial patterns in the spread of infectious disease agents, identification of high-risk areas, and the assessment of various environmental factors that may influence transmission pathways and the spread of infectious diseases. QGIS can be used for planning and implementing biosecurity measures in different production systems, as well as for preparing and conducting vaccination campaigns, leading to more effective surveillance, control, and prevention of infectious diseases in domestic animals. The study presents practical examples of QGIS applications in veterinary epidemiology, with a particular focus on the spatial spread analysis of African swine fever (ASF) in the Republic of Serbia. QGIS was used to perform spatial analysis of risk factors such as the number of pigs in various regions and the structure of farms. This approach enabled the identification of potential high-risk zones and improved understanding of population dynamics in ASF spread. The analysis was supported by statistical data processing, including visualization with pie charts integrated into spatial maps, providing a clearer overview of risk factors. This approach may contribute to more informed decision-making in the control and prevention of infectious diseases in domestic animals. Geospatial analyses can play a significant role in national plans for the control and prevention of infectious diseases in domestic animals. Therefore, it is essential that the use of such tools be actively considered as part of national programs for the control, prevention, and eradication of infectious animal diseases.

Systematic Review: National Notifiable Infectious Disease Surveillance System in China

Understanding the local burden and epidemiology of infectious diseases is crucial to guide public health policy and prioritize interventions. Typically, infectious disease surveillance relies on capturing clinical cases within a healthcare system, classifying cases by etiology and enumerating cases over a period of time. Disease burden is often then extrapolated to the general population. Serology (i.e., examining serum for the presence of pathogen-specific antibodies) has long been used to inform about individuals past exposure and immunity to specific pathogens. However, it has been underutilized as a tool to evaluate the infectious disease burden landscape at the population level and guide public health decisions. In this review, we outline how serology provides a powerful tool to complement case-based surveillance for determining disease burden and epidemiology of infectious diseases, highlighting its benefits and limitations. We describe the current serology-based technologies and illustrate their use with examples from both the pre- and post- COVID-19-pandemic context. In particular, we review the challenges to and opportunities in implementing serological surveillance in low- and middle-income countries (LMICs), which bear the brunt of the global infectious disease burden. Finally, we discuss the relevance of serology data for public health decision-making and describe scenarios in which this data could be used, either independently or in conjunction with case-based surveillance. We conclude that public health systems would greatly benefit from the inclusion of serology to supplement and strengthen existing case-based infectious disease surveillance strategies.

Advances in high-throughput technologies, digital phenotyping, and increased accessibility of publicly available datasets offer opportunities for big data to be applied in infectious disease surveillance, diagnosis, treatment, and outcome prediction. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to analyze complex clinical and molecular data. However, it remains unclear which AI or ML models are most suitable for infectious disease management, as most existing studies use non-scoping literature reviews to recommend AI and ML models for data analysis. This scoping literature review thus examines the ML models and applications that are most relevant for infectious disease management, with a proposed actionable workflow for implementing ML models in clinical practice. We conducted a literature search on PubMed, Google Scholar, and ScienceDirect, including papers published in English between January 2020 and April 2024. Search keywords included AI, ML, public health, surveillance, diagnosis, prognosis, and infectious disease, to identify published studies using AI and ML in infectious disease management. Studies without public datasets or lacking descriptions of the ML models were excluded. This review included a total of 77 studies applied in surveillance, prognosis, and diagnosis. Different types of input data from infectious disease surveillance, clinical diagnosis, and prognosis required different ML and AI models to achieve the maximum performance in infectious disease management. Our findings highlight the potential of Explainable AI and ensemble learning models to be more broadly applicable in different aspects of infectious disease management, which can be integrated in clinical workflows to improve infectious disease surveillance, diagnosis, and prognosis. Explainable AI and ensemble learning models can be suitably used to achieve high accuracy in prediction. However, as most of the studies have not been validated in different cohorts, it remains unclear whether these ML models can be broadly applicable to different populations. Nonetheless, the findings encourage deploying ML and AI to complement clinicians and augment clinical decision-making.

With frequent outbreaks of various epidemic infectious diseases across the globe, infectious disease surveillance plays a vital role in stopping the spread of infectious diseases. Privacy-preserving data aggregation is often used to avoid user privacy leakage caused by the transmission of infectious disease data. However, existing data aggregation schemes still have some security problems, such as untrusted aggregation nodes. To solve above problems, we propose a lightweight verifiable privacy-preserving infectious disease surveillance data aggregation scheme with fault tolerance. First, the improved Paillier homomorphic algorithm based on CRT and the signature algorithm with batch verification are used to efficiently encrypt and sign the infectious disease data to protect the data privacy and data integrity during data transmission. Second, the commitment mechanism is used to solve the problem of untrustworthiness of aggregate nodes. In addition, this scheme supports fault tolerance, and the aggregation work can continue even if some users and aggregation nodes do not upload data on time. In particular, this scheme can resist collusion attacks and meet higher security requirements. Since this scheme does not use time-consuming computational operations, such as bilinear mapping, simulation experiments show that the proposed scheme has excellent computational and communication overhead and can be safely and effectively applied to infectious disease surveillance systems.

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI.

Background - The special geographical position of Yunnan Province as the key area and frontline of "external prevention and importation", the difficulty and pressure of prevention and control in border areas is high, the prevention and control of the epidemic in Yunnan Province is crucial to the overall situation of the province and even the whole country. Covid-19 exposes alarming gaps in Infectious Disease Surverillance in Border Areas. Objectives - Infectious disease surveillance is an important tool for early identification and prevention and control of infectious diseases. Regular disease surveillance of emerging infectious diseases is essential to be able to respond to pandemics and control them early through the construction of infectious disease surveillance systems to build stronger and better health systems. Method - Summarize and sort out the existing monitoring system and problems using a combination of qualitative research methods such as literature methods, group interviews, and expert consultation. Using a combination of typical and stratified sampling methods, a questionnaire was used to analyze the current status of monitoring and management in border counties in Yunnan Province. Results - There is no statistical difference in surveillance and prevention between the state and county levels in response to major public health emergencies, and the current surveillance system is difficult for epidemic dissemination, prediction and timely and effective follow-up. Conclusion - The border areas of Yunnan Province have not established an effective epidemic surveillance system, lack of diversified channels and ways to disseminate epidemic information, difficulty in accurately grasping the progress and changing trends of the epidemic, and risk awareness has not been strengthened and transformed to the detriment of active prevention, control and management of infectious diseases.

Infectious diseases pose a significant threat to public health worldwide, requiring constant vigilance, innovative strategies, and rapid responses to mitigate their impact.In recent years, Artificial Intelligence (AI) has emerged as a powerful tool in the fight against infectious diseases, revolutionizing the way we surveil, prevent, and control outbreaks.This book, Surveillance, Prevention, and Control of Infectious Diseases: An AI Perspective, explores the intersection of AI and infectious disease management, offering insights into the latest advancements, challenges, and opportunities in this rapidly evolving field.The chapters in this book provide a comprehensive overview of various aspects of infectious disease management through the lens of AI.We begin with an exploration of the prospects and challenges of using AI for infectious disease detection, highlighting its potential to expedite outbreak identification and facilitate proactive public health interventions.We then delve into the development and application of AI algorithms for rapid pathogen detection and surveillance, discussing innovative approaches for identifying and monitoring infectious diseases in real-time.One of the key areas covered in this book is the use of wearable devices and continuous physiological signal monitoring for early disease detection.We examine how wearable sensors and AI algorithms can be utilized to analyze physiological data and identify early symptoms of infectious diseases, enabling timely intervention and improved patient outcomes.Additionally, we explore the development of low-cost, automated digital microscopes for detecting diseases such as malaria, showcasing the potential of AI-driven technologies to improve diagnostic capabilities in resource-limited settings.This book addresses the challenge of under-vaccination in developing countries, proposing a comprehensive framework to identify and support children who miss essential vaccinations.It reviews research on vaccine gaps, synthesizes effective intervention strategies, and explores the potential of advanced technologies like AI and blockchain.By offering a systematic approach, the framework aims to improve vaccination coverage and promote global health equity by ensuring equitable access to vaccines for all children.Another highlight of this book is the discussion on the interpretability of deep learning models for tuberculosis detection using X-ray images.We explore how lightweight parallel v vi Preface CNN models can enhance diagnostic accuracy while providing insights into the prediction process, paving the way for more effective disease diagnosis and management.Furthermore, we investigate the use of AI in the surveillance of seasonal respiratory infections, showcasing its role in automating outbreak identification and reducing transmission rates through early detection and intervention.Throughout this book, we emphasize the importance of collaboration between healthcare professionals, researchers, policymakers, and technologists in harnessing the full potential of AI for infectious disease management.We hope that the insights and findings presented in this book will inspire further research and innovation in this critical area, ultimately leading to more effective strategies for surveilling, preventing, and controlling infectious diseases on a global scale.We are immensely grateful for the dedicated efforts of our contributors in shaping this comprehensive volume on Surveillance, Prevention, and Control of Infectious Diseases: An AI Perspective.Your expertise and commitment have ensured the quality and relevance of each chapter, enriching the book with diverse insights and innovative approaches.As we move forward with the editing process, we encourage thoroughness and attention to detail to maintain the book's excellence.We extend our heartfelt appreciation to our esteemed reviewers for their invaluable feedback and constructive criticism, which have been instrumental in refining the manuscript.Your thorough reviews have enhanced the rigor and clarity of the content, contributing significantly to the scholarly integrity of the book.We are deeply grateful for your time, expertise, and dedication to advancing knowledge in this critical field.

The research initiative CLINF addresses a central issue in planning for the responsible development of the North: an understanding of the impact of climate change on the geographic distribution and epidemiology of climate sensitive infectious diseases (CSIs), and their associated consequences for Arctic health, economic growth, and societal prosperity. Changes in infectious diseases transmission patterns are a likely consequence of changing climates, a neglected problem that is likely to have a profound effect on northern societies, including indigenous cultures. There is an urgent need to learn more about the complex underlying dynamic relationships, and apply this information to the prediction of future CSI impacts, using more complete, better validated, and integrated data and models. This chapter provides an overview of the thoughts behind the CLINF NCoE (Nordic Centre of Excellence), and the integrative context expressed therein. The most recent findings regarding climate change in the Arctic, as published by IPCC and other global networks, are presented. In the international CLINF consortium of researchers, nine human and 18 animal husbandry diseases have been selected for study due to their potential for being climate sensitive. The human infections were selected by an international consortium of researchers, to represent fundamentally different transmission processes. The main CLINF objectives are the construction of practical tools for the decision-makers who are responsible for the development of northern societies. By contributing to the development of an early warning system for increased risks for CSIs to spread at the local level effective policy responses may be formulated. The overall aim of CLINF is to support the sustainability of Arctic development.

This review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, Neglected Tropical Diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context. Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives. Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics. In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa. Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies. Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.

BackgroundWe developed the Apollo Structured Vocabulary (Apollo-SV)—an OWL2 ontology of phenomena in infectious disease epidemiology and population biology—as part of a project whose goal is to increase the use of epidemic simulators in public health practice. Apollo-SV defines a terminology for use in simulator configuration. Apollo-SV is the product of an ontological analysis of the domain of infectious disease epidemiology, with particular attention to the inputs and outputs of nine simulators.ResultsApollo-SV contains 802 classes for representing the inputs and outputs of simulators, of which approximately half are new and half are imported from existing ontologies. The most important Apollo-SV class for users of simulators is infectious disease scenario, which is a representation of an ecosystem at simulator time zero that has at least one infection process (a class) affecting at least one population (also a class). Other important classes represent ecosystem elements (e.g., households), ecosystem processes (e.g., infection acquisition and infectious disease), censuses of ecosystem elements (e.g., censuses of populations), and infectious disease control measures.In the larger project, which created an end-user application that can send the same infectious disease scenario to multiple simulators, Apollo-SV serves as the controlled terminology and strongly influences the design of the message syntax used to represent an infectious disease scenario. As we added simulators for different pathogens (e.g., malaria and dengue), the core classes of Apollo-SV have remained stable, suggesting that our conceptualization of the information required by simulators is sound.Despite adhering to the OBO Foundry principle of orthogonality, we could not reuse Infectious Disease Ontology classes as the basis for infectious disease scenarios. We thus defined new classes in Apollo-SV for host, pathogen, infection, infectious disease, colonization, and infection acquisition. Unlike IDO, our ontological analysis extended to existing mathematical models of key biological phenomena studied by infectious disease epidemiology and population biology.ConclusionOur ontological analysis as expressed in Apollo-SV was instrumental in developing a simulator-independent representation of infectious disease scenarios that can be run on multiple epidemic simulators. Our experience suggests the importance of extending ontological analysis of a domain to include existing mathematical models of the phenomena studied by the domain. Apollo-SV is freely available at: http://purl.obolibrary.org/obo/apollo_sv.owl.

Migration is an important factor impacting on infectious disease epidemiology. The timely identification of groups at risk and prevention needs resulting from migration is indispensable to adequately design and implement public health measures. It remains to be assessed to which extent surveillance data for notifiable diseases can directly generate meaningful migration-specific information. The objectives of this study are to review indicators of migration background utilized in the German infectious disease surveillance, as well as to assess their limitations. We describe the indicators of migration used for mandatorily notifiable diseases and pathogens and their legal basis in the Protection against Infection Act and conduct a descriptive analysis of surveillance data for tuberculosis (TB), HIV and syphilis from 2002-2013. Migration status is collected only for five infectious diseases and operationalization varies. For TB (country of birth) and HIV (country of origin) a foreign origin was more frequent than for syphilis (country of origin); namely 46, 30 and 13% of cases with available information, respectively. In all three examples, there are indications of risk profiles that are specific for particular groups of migrants. A standardization of indicators of migration in infectious disease surveillance is important to enhance data comparability between diseases and pathogens as well as across countries. Routine surveillance already partly allows migration sensitive analyses, yet further research is needed to guide interpretation of the complex relationship between migration and infectious diseases and plan public health measures adequately.