Whose knowledge counts? Equity, epistemic justice, and reforming infectious disease research culture.

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.

Residential segregation and the epidemiology of infectious diseases

We're on mute! Exclusion of nurses' voices in national decisions and responses to COVID-19: An international perspective.

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.

Climate change, environmental factors, and COVID-19: Current evidence and urgent actions

Modern infectious disease epidemiology makes heavy use of computational model–based approaches and a dynamical systems perspective. The importance of analyzing infectious diseases in such a way keeps increasing. However, infectious disease epidemiology is still often taught mainly from a medical and classical epidemiological study design (e.g., cohort, case-control) perspective. While textbooks and other resources that teach a model-based approach to infectious diseases exist, almost any such teaching material requires students to work with mathematical models and write computer code. This is a significant barrier for students who do not have a strong mathematical background or prior coding experience, which applies to many students in public health and related biomedical disciplines. It limits the number of students who can or want to engage with infectious disease epidemiology by using modern, systems modeling–based approaches. New tools and approaches are needed to reach a wider audience and allow students to learn concepts such as the reproductive number, herd immunity, critical community size, and the population-level impact of interventions from a dynamical systems and model perspective, without the obstacles of coding or having to formulate and analyze differential equations. Here, I describe a new software package for the widely used R language that allows individuals to explore and study concepts of infectious disease epidemiology by using a modern, dynamical systems model framework, without the need to read or write computer code. The package includes documentation and material to serve as a stand-alone tool—supplemented as needed with provided references—for students to get an introduction to important modern infectious disease concepts. The package is built in a modular way that allows a student to seamlessly continue on their journey of learning infectious disease modeling if they choose to do so. The different ways to use the package are described in detail, and examples are provided.

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.

Throughout the history of epidemiology, visualizations have been used as the interface between public-health professionals and epidemiological data. The aim of this study was to examine the impact of the level of abstraction when using visualizations on routine infectious disease control. We developed three interactive visualization prototypes at increasing levels of abstraction to communicate subsets of influenza outbreak surveillance information. The visualizations were assessed through workshops in an exploratory evaluation with infectious disease epidemiologists. The results show that despite the potential of processed, abstract, and information-dense representations, increased levels of abstraction decreased epidemiologists' understanding and confidence in visualizations. Highly abstract representations were deemed not applicable in routine practice without training. Infectious disease epidemiologists' work routines and decision-making need to be further studied in order to develop visualizations that meet both the quality requirements imposed by policy-makers and the contextual nature of work practice.

The EU Response to COVID-19: From Reactive Policies to Strategic Decision-Making.

With the change of infectious disease incidence pattern and the development of related technologies, progresses have been made in the research of infectious disease epidemiology. In recent years, due to the change in the requirements of infectious disease prevention and control, the research focus has expanded from common infectious diseases to diseases which have been eliminated or might be eliminated, as well as emerging and re-emerging infectious diseases. Infectious disease data has been characterized by multiple sources and modalities. Along with the rapid development of pathogen detection methods, infectious disease surveillance has shifted from a single disease-targted one to a comprehensive one. Moreover, novel technologies such as multi-omics and artificial intelligence have been applied in infectious disease epidemiology research. The international cooperation in this field has become increasingly crucial, and the revision of the International Health Regulations and the negotiation of pandemic agreement will have a profound impact. In the future, infectious disease epidemiology research will develop with more powerful tools to improve its capabilities.

The increasing number of mothers of young children in the work force and the resultant escalated use of child-care facilities has had a marked effect on the epidemiology of infectious diseases in young children. Children attending child care are at high risk for respiratory and gastrointestinal tract illnesses. The high prevalence of infectious diseases in the child-care setting is accompanied by high usage of antibiotics, which in turn has resulted in spread of antibiotic-resistant organisms. The infectious disease standards of the American Public Health Association/American Academy of Pediatrics guidelines were developed to prevent and limit transmission of infectious diseases in the child-care setting. Adherence to these standards is essential but will not completely eliminate the increased risk of infectious diseases in child-care settings. New challenges need to be addressed to assure that optimal health promotion and disease prevention is practiced in child-care settings. We approach the 21st century with a vast amount of medical knowledge, molecular technology, highly effective vaccines, and powerful antimicrobial agents. However, at the same time we face many unsolved serious problems, such as preventing or controlling the emergence and spread of antibiotic-resistant organisms that adversely affect our ability to treat infectious diseases. Further research is needed concerning the relations between child care, the use of antibiotics, and transmission of antibiotic-resistant organisms in order to design and implement the most effective strategies for preventing or controlling antibiotic resistance. The potential risk for transmission of HIV in the child-care setting also needs to be recognized, and procedures to prevent transmission of blood-borne pathogens need to be followed. Monitoring compliance with national standards for child-care facilities, dissemination of information concerning infectious diseases and use of antibiotics, and development and use of new vaccines are strategies which should be used to help protect the health of children in child-care environments.

An inclusive research culture is vital towards the maturity of Health and Education Precincts into an active innovation ecosystem. To date, substantial investments have been made in 13 upcoming Health and Education Precincts in varying stages of development in the Greater Sydney region, New South Wales. The political commitment to create an innovative environment for teaching and a vibrant research culture is noticeable. However, it is unclear to what extent government policy engages the breadth of clinical personnel in teaching and research-related activities and contributes towards improving research culture. Based on a study conducted at the central river district of the Greater Sydney region, we argue that better engagement of clinical personnel in teaching/research-related activities and inclusion of research-related roles within the job description of clinical personnel can substantially drive a positive research culture and thereby contribute towards the overall development of Health and Education Precincts. Opportunities for continued education and training of clinical personnel and involvement in graduate research programs also substantially drives research culture. We argue that future policy and practice solutions for upcoming Health and Education Precincts need to foster an inclusive research culture and should be tailored to meet the needs of an innovative ecosystem. Future solutions will need to contribute towards improving research culture as well as the health and wellbeing of people in the region.

When modern epidemiology first took shape, there was only one kind of epidemiology – epidemiology, period. Over time has come specialisation into chronic and infectious disease epidemiology. Does this segregation...

Frederick Griffith (1879-1941) was an English bacteriologist at the Pathological Laboratory of the Ministry of Health in London who believed that progress in the epidemiology and control of infectious diseases would come only with more precise knowledge of the identity of the causative microorganisms. Over the years, Griffith developed and expanded a serological technique for identifying pathogenic microorganisms, which allowed the tracing of the sources of infectious disease outbreaks: slide agglutination. Yet Griffith is not remembered for his contributions to the biology and epidemiology of infectious diseases so much as for discovering the phenomenon known as 'transformation'. Griffith's discovery, for many, was a pure case of serendipity whose biological relevance had also largely escaped him. In this paper, I argue that the key to understanding the significance of bacterial transformation - and the scientific legacy of Fred Griffith - rests not only on it initiating a cascade of events leading to molecular genetics but also on its implications for epidemiology based on the biology of host-parasite interactions. Looking at Griffith's entire career, instead of focusing only on the transformation study, we can better appreciate the place of the latter within Griffith's overall contributions. Presented in this way, Griffith's experiment on bacterial transformation also ceases to appear as an anomaly, which in turn leads us to rethink some of the most prevalent historical conceptions about his work.

Background The crisis in research culture is well documented, covering issues such as a tendency for quantity over quality, unhealthy competitive environments, and assessment based on publications, journal prestige and funding. In response, research institutions need to assess their own practices to promote and advocate for change in the current research ecosystem. The purpose of the scoping review was to explore ‘What does the evidence say about the ‘problem’ with ‘poor’ research culture, what are the benefits of ‘good’ research culture, and what does ‘good’ look like?’ Aims To examine the peer-reviewed and grey literature to explore the interplay between research culture, open research, career paths, recognition and rewards, and equality, diversity, and inclusion, as part of a larger programme of activity for a research institution. Methods A scoping review was undertaken. Six databases were searched along with grey literature. Eligible literature had relevance to academic research institutions, addressed research culture, and were published between January 2017 to May 2022. Evidence was mapped and themed to specific categories. The search strategy, screening and analysis took place between April-May 2022. Results 1666 titles and abstracts, and 924 full text articles were assessed for eligibility. Of these, 253 articles met the eligibility criteria for inclusion. A purposive sampling of relevant websites was drawn from to complement the review, resulting in 102 records included in the review. Key areas for consideration were identified across the four themes of job security, wellbeing and equality of opportunity, teamwork and interdisciplinary, and research quality and accountability. Conclusions There are opportunities for research institutions to improve their own practice, however institutional solutions cannot act in isolation. Research institutions and research funders need to work together to build a more sustainable and inclusive research culture that is diverse in nature and supports individuals’ well-being, career progression and performance.