Visualization and analytics tools for infectious disease epidemiology: a systematic review

Innovating health prevention models in detecting infectious disease outbreaks through social media data: an umbrella review of the evidence

Introduction and objective

The number of literature reviews examining the use of social media in detecting emerging infectious diseases has recently experienced an unprecedented growth. Yet, a higher-level integration of the evidence is still lacking. This study aimed to synthesize existing systematic literature reviews published on this topic, offering an overview that can help policymakers and public health authorities to select appropriate policies and guidelines.

Methods

We conducted an umbrella review: a review of systematic reviews published between 2011 and 2023 following the PRISMA statement guidelines. The review protocol was registered in the PROSPERO database (CRD42021254568). As part of the search strategy, three database searches were conducted, specifically in PubMed, Web of Science, and Google Scholar. The quality of the included reviews was determined using A Measurement Tool to Assess Systematic Reviews 2.

Results

Synthesis included 32 systematic reviews and 3,704 primary studies that investigated how the social media listening could improve the healthcare system's efficiency in terms of a timely response to treat epidemic situations. Most of the included systematic reviews concluded showing positive outcomes when using social media data for infectious disease surveillance.

Conclusion

Systematic reviews showed the important role of social media in predicting and detecting disease outbreaks, potentially reducing morbidity and mortality through swift public health action. The policy interventions strongly benefit from the continued use of online data in public health surveillance systems because they can help in recognizing important patterns for disease surveillance and significantly improve the disease prediction abilities of the traditional surveillance systems.

Systematic Review Registration

http://www.crd.york.ac.uk/PROSPERO, identifier [CRD42021254568].

Visualizing multilayer spatiotemporal epidemiological data with animated geocircles

OBJECTIVE

The COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect utility for both experts and non-experts.

MATERIALS AND METHODS

We propose encoding variables with animated, concentric, hollow circles, allowing multiple variables via color encoding and avoiding occlusion problems, and we implement this method in a browser-based tool called CoronaViz. We conduct task-based evaluations with non-experts, as well as in-depth interviews and observational sessions with epidemiologists, covering a range of tools and encodings.

RESULTS

Sessions with epidemiologists confirmed the importance of multivariate, spatiotemporal queries and the utility of CoronaViz for answering them, while providing direction for future development. Non-experts tasked with performing spatiotemporal queries unanimously preferred animation to multi-view dashboards.

DISCUSSION

We find that conveying complex, multivariate data necessarily involves trade-offs. Yet, our studies suggest the importance of complementary visualization strategies, with our animated multivariate spatiotemporal encoding filling important needs for exploration and presentation.

CONCLUSION

CoronaViz's unique ability to convey multiple, time-varying, geospatial variables makes it both a valuable addition to interactive COVID-19 dashboards and a platform for empowering experts and the public during future disease outbreaks. CoronaViz is open-source and a live instance is freely hosted at http://coronaviz.umiacs.io.

Enhancing GeoHealth: A step-by-step procedure for spatiotemporal disease mapping

Cartography, or geographical visualization of disease is an essential aspect of the field of GeoHealth, yet there is limited guidance on the visualization of spatiotemporal disease maps. In order to adequately contribute to understanding disease outbreaks, disease maps should be crafted carefully and according to relevant cartographic guidelines. This article aims to increase the understanding of space-time visualization techniques that are relevant to the field of GeoHealth, by providing a step-by-step framework for the creation of space-time disease visualizations. This study introduces a systematic approach to spatiotemporal disease mapping by integrating operations from the Generalized Space Time Cube (GSTC) Framework with established cartographic symbology guidelines. This resulted in an overview table that contains both the relevant GSTC operations and cartographic guidelines, as well as a step-by-step procedure that guides users through the process of creating informative spatiotemporal disease maps. The practical application of this step-by-step procedure is demonstrated with an example using Dutch COVID-19 data. By providing a clear, practical step by step procedure, this study enhances the capacity of public health professionals, policymakers, and researchers to monitor, understand, and respond to the spatial and temporal dynamics of diseases.

Epidemiological Characteristics and Spatiotemporal Clustering of Pulmonary Tuberculosis Among Students in Southwest China From 2016 to 2022: Analysis of Population-Based Surveillance Data

Background

Pulmonary tuberculosis (PTB), as a respiratory infectious disease, poses significant risks of covert transmission and dissemination. The high aggregation and close contact among students in Chinese schools exacerbate the transmission risk of PTB outbreaks.

Objective

This study investigated the epidemiological characteristics, geographic distribution, and spatiotemporal evolution of student PTB in Chongqing, Southwest China, aiming to delineate the incidence risks and clustering patterns of PTB among students.

Methods

PTB case data from students monitored and reported in the Tuberculosis Information Management System within the China Information System for Disease Control and Prevention were used for this study. Descriptive analyses were conducted to characterize the epidemiological features of student PTB. Spatial trend surface analysis, global and local spatial autocorrelation analyses, and disease rate mapping were performed using ArcGIS 10.3. SaTScan 9.6 software was used to identify spatiotemporal clusters of PTB cases.

Results

From 2016 to 2022, a total of 9920 student TB cases were reported in Chongqing, Southwest China, with an average incidence rate of 24.89/100,000. The incidence of student TB showed an initial increase followed by a decline, yet it remained relatively high. High school students (age: 13-18 years; 6649/9920, 67.03%) and college students (age: ≥19 years; 2921/9920, 29.45%) accounted for the majority of student PTB cases. Patient identification primarily relied on passive detection, with a high proportion of delayed diagnosis and positive etiological results. COVID-19 prevention measures have had some impact on reducing incidence levels, but the primary factor appears to be the implementation of screening measures, which facilitated earlier case detection. Global spatial autocorrelation analysis indicated Moran I values of >0 for all years except 2018, ranging from 0.1908 to 0.4645 (all P values were <.05), suggesting strong positive spatial clustering of student PTB cases across Chongqing. Local spatial autocorrelation identified 7 high-high clusters, 13 low-low clusters, 5 high-low clusters, and 4 low-high clusters. High-high clusters were predominantly located in the southeast and northeast parts of Chongqing, consistent with spatial trend surface analysis and spatiotemporal clustering results. Spatiotemporal scan analysis revealed 4 statistically significant spatiotemporal clusters, with the most likely cluster in the southeast (relative risk [RR]=2.87, log likelihood ratio [LLR]=574.29, P<.001) and a secondary cluster in the northeast (RR=1.99, LLR=234.67, P<.001), indicating higher reported student TB cases and elevated risks of epidemic spread within these regions.

Conclusions

Future efforts should comprehensively enhance prevention and control measures in high-risk areas of PTB in Chongqing to mitigate the incidence risk among students. Additionally, implementing proactive screening strategies and enhancing screening measures are crucial for early identification of student patients to prevent PTB outbreaks in schools.

Advancing Public Health Surveillance in Child Care Centers: Stakeholder-Informed Redesign and User Satisfaction Evaluation of the MCRISP Network

Unlabelled

Leveraging user feedback, we redesigned a novel disease monitoring utility to allow for bidirectional data flow and in this letter offer insights into that process as well as lessons learned.

Project SIGNAL: A Dashboard for Supporting Community Confidence in Making Data-Driven Decisions

CONTEXT

Data dashboards have emerged as critical tools for surveillance and informing resource allocation. Despite their utility and popularity during COVID-19, there is a growing need to understand what tools and training are tailored to nonprofit community-based organizations that may partner with public health officials.

PROGRAM

In June 2021, the Rhode Island Department of Health and Brown University partnered to create Project SIGNAL (Spatiotemporal Insights to Guide Nuanced Actions Locally), which utilizes spatiotemporal analytics to identify Rhode Island's largest disparities in COVID-19-related outcomes (eg, testing, diagnosis, vaccinations) at the neighborhood level. Results were hosted in an interactive online dashboard (signal-ri.org) designed using principles of the CDC Clear Communication Index. The target audience included a network of 15 geographic areas called Health Equity Zones, funded by the health department to provide critical grassroots public health programs to address social, health, and economic outcomes in their communities.

IMPLEMENTATION

To disseminate the dashboard, a 6-hour virtual workshop series was created to train leaders to use the dashboard and increase their confidence in understanding common public health data terminology and concepts and better prepare attendees for rapid decision making during future public health emergencies.

EVALUATION

The Project SIGNAL dashboard was launched in August 2022 and has been accessed over 7500 times. A total of 84 community leaders were trained to use this dashboard, increasing their confidence in applying common public health metrics to make decisions about their COVID-19-related activities.

DISCUSSION

While several studies have outlined best practices for data dashboards, this is among the first to examine incorporating these practices into a spatiotemporal decision tool designed specifically for community organizations. Project SIGNAL demonstrates that by incorporating design best practices and pairing data dashboards with hands-on training, we can empower community leaders to utilize advanced spatiotemporal methods to identify health disparities and take localized action.

Precision prognosis of colorectal cancer: a multi-tiered model integrating microsatellite instability genes and clinical parameters

Background

Prognostic assessment for colorectal cancer (CRC) displays substantial heterogeneity, as reliance solely on traditional TNM staging falls short of achieving precise individualized predictions. The integration of diverse biological information sources holds the potential to enhance prognostic accuracy.

Objective

To establish a comprehensive multi-tiered precision prognostic evaluation system for CRC by amalgamating gene expression profiles, clinical characteristics, and tumor microsatellite instability (MSI) status in CRC patients.

Methods

We integrated genomic data, clinical information, and survival follow-up data from 483 CRC patients obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. MSI-related gene modules were identified using differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Three prognostic models were constructed: MSI-Related Gene Prognostic Model (Model I), Clinical Prognostic Model (Model II), and Integrated Multi-Layered Prognostic Model (Model III) by combining clinical features. Model performance was assessed and compared using Receiver Operating Characteristic (ROC) curves, Kaplan-Meier analysis, and other methods.

Results

Six MSI-related genes were selected for constructing Model I (AUC = 0.724); Model II used two clinical features (AUC = 0.684). Compared to individual models, the integrated Model III exhibited superior performance (AUC = 0.825) and demonstrated good stability in an independent dataset (AUC = 0.767).

Conclusion

This study successfully developed and validated a comprehensive multi-tiered precision prognostic assessment model for CRC, providing an effective tool for personalized medical management of CRC.

Network development in workplace health promotion - empirically based insights from a cross-company network promoting physical activity in Germany

BACKGROUND

In the field of health promotion, interorganisational networks are of growing relevance. However, systematic and target-oriented network management is of utmost importance for network development. The aim of this article is to report on the development of a cross-company network promoting physical activity, and to identify necessary activities and competencies for a systematic network management.

METHODS

The network was systematically planned and implemented in a German technology park comprising different companies. To assess and describe the development of the network, quantitative social network analysis was conducted. To answer the question on the activities and competencies for systematic network development semi-structured interviews with participating stakeholders, and a focus group discussion with health promotion experts were conducted. The interviews were analysed deductively and inductively with the structuring content analysis method and the focus group discussion was analysed deductively by summarising key aspects of the discussion.

RESULTS

Network metrics showed that the network became larger and denser during the planning phase, and stagnated during the implementation phase. As key facilitators for network development, participation of all stakeholders, a kick-off event, and the driving role of a network manager were identified. Necessary activities of the network manager were related to structural organisation, workplace health promotion offers, and cross-sectional tasks. The results suggested that not only professional and methodological competencies, but also social and self-competencies were required by the manager.

CONCLUSIONS

Our study provides initial guidance regarding the activities and required competencies of an interorganisational network manager. The results are of particular relevance for the context of workplace health promotion, since a network manager can be considered as a driving role for planning and implementing a cross-company network.

TRIAL REGISTRATION

The study is registered in the German Clinical Trials Register (DRKS00020956, 18/06/2020).

The Solutions in Health Analytics for Rural Equity Across the Northwest (SHARE-NW) Dashboard for Health Equity in Rural Public Health: Usability Evaluation

BACKGROUND

Given the dearth of resources to support rural public health practice, the solutions in health analytics for rural equity across the northwest dashboard (SHAREdash) was created to support rural county public health departments in northwestern United States with accessible and relevant data to identify and address health disparities in their jurisdictions. To ensure the development of useful dashboards, assessment of usability should occur at multiple stages throughout the system development life cycle. SHAREdash was refined via user-centered design methods, and upon completion, it is critical to evaluate the usability of SHAREdash.

OBJECTIVE

This study aims to evaluate the usability of SHAREdash based on the system development lifecycle stage 3 evaluation goals of efficiency, satisfaction, and validity.

METHODS

Public health professionals from rural health departments from Washington, Idaho, Oregon, and Alaska were enrolled in the usability study from January to April 2022. The web-based evaluation consisted of 2 think-aloud tasks and a semistructured qualitative interview. Think-aloud tasks assessed efficiency and effectiveness, and the interview investigated satisfaction and overall usability. Verbatim transcripts from the tasks and interviews were analyzed using directed content analysis.

RESULTS

Of the 9 participants, all were female and most worked at a local health department (7/9, 78%). A mean of 10.1 (SD 1.4) clicks for task 1 (could be completed in 7 clicks) and 11.4 (SD 2.0) clicks for task 2 (could be completed in 9 clicks) were recorded. For both tasks, most participants required no prompting-89% (n=8) participants for task 1 and 67% (n=6) participants for task 2, respectively. For effectiveness, all participants were able to complete each task accurately and comprehensively. Overall, the participants were highly satisfied with the dashboard with everyone remarking on the utility of using it to support their work, particularly to compare their jurisdiction to others. Finally, half of the participants stated that the ability to share the graphs from the dashboard would be "extremely useful" for their work. The only aspect of the dashboard cited as problematic is the amount of missing data that was present, which was a constraint of the data available about rural jurisdictions.

CONCLUSIONS

Think-aloud tasks showed that the SHAREdash allows users to complete tasks efficiently. Overall, participants reported being very satisfied with the dashboard and provided multiple ways they planned to use it to support their work. The main usability issue identified was the lack of available data indicating the importance of addressing the ongoing issues of missing and fragmented public health data, particularly for rural communities.

National Public Health Dashboards: Protocol for a Scoping Review

BACKGROUND

The COVID-19 pandemic highlighted the importance of robust public health data systems and the potential utility of data dashboards for ensuring access to critical public health data for diverse groups of stakeholders and decision makers. As dashboards are becoming ubiquitous, it is imperative to consider how they may be best integrated with public health data systems and the decision-making routines of diverse audiences. However, additional progress on the continued development, improvement, and sustainability of these tools requires the integration and synthesis of a largely fragmented scholarship regarding the purpose, design principles and features, successful implementation, and decision-making supports provided by effective public health data dashboards across diverse users and applications.

OBJECTIVE

This scoping review aims to provide a descriptive and thematic overview of national public health data dashboards including their purpose, intended audiences, health topics, design elements, impact, and underlying mechanisms of use and usefulness of these tools in decision-making processes. It seeks to identify gaps in the current literature on the topic and provide the first-of-its-kind systematic treatment of actionability as a critical design element of public health data dashboards.

METHODS

The scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The review considers English-language, peer-reviewed journal papers, conference proceedings, book chapters, and reports that describe the design, implementation, and evaluation of a public health dashboard published between 2000 and 2023. The search strategy covers scholarly databases (CINAHL, PubMed, Medline, and Web of Science) and gray literature sources and uses snowballing techniques. An iterative process of testing for and improving intercoder reliability was implemented to ensure that coders are properly trained to screen documents according to the inclusion criteria prior to beginning the full review of relevant papers.

RESULTS

The search process initially identified 2544 documents, including papers located via databases, gray literature searching, and snowballing. Following the removal of duplicate documents (n=1416), nonrelevant items (n=839), and items classified as literature reviews and background information (n=73), 216 documents met the inclusion criteria: US case studies (n=90) and non-US case studies (n=126). Data extraction will focus on key variables, including public health data characteristics; dashboard design elements and functionalities; intended users, usability, logistics, and operation; and indicators of usefulness and impact reported.

CONCLUSIONS

The scoping review will analyze the goals, design, use, usefulness, and impact of public health data dashboards. The review will also inform the continued development and improvement of these tools by analyzing and synthesizing current practices and lessons emerging from the literature on the topic and proposing a theory-grounded and evidence-informed framework for designing, implementing, and evaluating public health data dashboards.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/52843.

Utilizing geospatial artificial intelligence to map cancer disparities across health regions

We have developed an innovative tool, the Intelligent Catchment Analysis Tool (iCAT), designed to identify and address healthcare disparities across specific regions. Powered by Artificial Intelligence and Machine Learning, our tool employs a robust Geographic Information System (GIS) to map healthcare outcomes and disease disparities. iCAT allows users to query publicly available data sources, health system data, and treatment data, offering insights into gaps and disparities in diagnosis and treatment paradigms. This project aims to promote best practices to bridge the gap in healthcare access, resources, education, and economic opportunities. The project aims to engage local and regional stakeholders in data collection and evaluation, including patients, providers, and organizations. Their active involvement helps refine the platform and guides targeted interventions for more effective outcomes. In this paper, we present two sample illustrations demonstrating how iCAT identifies healthcare disparities and analyzes the impact of social and environmental variables on outcomes. Over time, this platform can help communities make decisions to optimize resource allocation.

Public Health Dashboards in Overdose Prevention: The Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action

As the field of public health rises to the demands of real-time surveillance and rapid data-sharing needs in a postpandemic world, it is time to examine our approaches to the dissemination and accessibility of such data. Distinct challenges exist when working to develop a shared public health language and narratives based on data. It requires that we assess our understanding of public health data literacy, revisit our approach to communication and engagement, and continuously evaluate our impact and relevance. Key stakeholders and cocreators are critical to this process and include people with lived experience, community organizations, governmental partners, and research institutions. In this viewpoint paper, we offer an instructive approach to the tools we used, assessed, and adapted across 3 unique overdose data dashboard projects in Rhode Island, United States. We are calling this model the "Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action." This approach reflects the iterative lessons learned about the improvement of data dashboards through collaboration and strong partnerships across community members, state agencies, and an academic research team. We will highlight key tools and approaches that are accessible and engaging and allow developers and stakeholders to self-assess their goals for their data dashboards and evaluate engagement with these tools by their desired audiences and users.

Modelling the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas in Toronto, Canada

Modelling the spatiotemporal spread of a highly transmissible disease is challenging. We developed a novel spatiotemporal spread model, and the neighbourhood-level data of COVID-19 in Toronto was fitted into the model to visualize the spread of the disease in the study area within two weeks of the onset of first outbreaks from index neighbourhood to its first-order neighbourhoods (called dispersed neighbourhoods). We also model the data to classify hotspots based on the overall incidence rate and persistence of the cases during the study period. The spatiotemporal spread model shows that the disease spread to 1-4 neighbourhoods bordering the index neighbourhood within two weeks. Some dispersed neighbourhoods became index neighbourhoods and further spread the disease to their nearby neighbourhoods. Most of the sources of infection in the dispersed neighbourhood were households and communities (49%), and after excluding the healthcare institutions (40%), it becomes 82%, suggesting the expansion of transmission was from close contacts. The classification of hotspots informs high-priority areas concentrated in the northwestern and northeastern parts of Toronto. The spatiotemporal spread model along with the hotspot classification approach, could be useful for a deeper understanding of spatiotemporal dynamics of infectious diseases and planning for an effective mitigation strategy where local-level spatially enabled data are available.

Space-time cluster detection techniques for infectious diseases: A systematic review

BACKGROUND

Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives.

METHODS

We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion.

RESULTS

Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability.

CONCLUSION

This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information

BACKGROUND

Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects.

METHODS

This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality.

RESULTS

Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations.

CONCLUSIONS

This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Data Analytics in Healthcare: A Tertiary Study

The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer's disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25-100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.

Development of a usability checklist for public health dashboards to identify violations of usability principles

OBJECTIVE

To develop a usability checklist for public health dashboards.

MATERIALS AND METHODS

This study systematically evaluated all publicly available dashboards for sexually transmitted infections on state health department websites in the United States (N = 13). A set of 11 principles derived from the information visualization literature were used to identify usability problems that violate critical usability principles: spatial organization, information coding, consistency, removal of extraneous ink, recognition rather than recall, minimal action, dataset reduction, flexibility to user experience, understandability of contents, scientific integrity, and readability. Three user groups were considered for public health dashboards: public health practitioners, academic researchers, and the general public. Six reviewers with usability knowledge and diverse domain expertise examined the dashboards using a rubric based on the 11 principles. Data analysis included quantitative analysis of experts' usability scores and qualitative synthesis of their textual comments.

RESULTS

The dashboards had varying levels of complexity, and the usability scores were dependent on the dashboards' complexity. Overall, understandability of contents, flexibility, and scientific integrity were the areas with the most major usability problems. The usability problems informed a checklist to improve performance in the 11 areas.

DISCUSSION

The varying complexity of the dashboards suggests a diversity of target audiences. However, the identified usability problems suggest that dashboards' effectiveness for different groups of users was limited.

CONCLUSIONS

The usability of public health data dashboards can be improved to accommodate different user groups. This checklist can guide the development of future public health dashboards to engage diverse audiences.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

We report on an ongoing collaboration between epidemiological modellers and visualization researchers by documenting and reflecting upon knowledge constructs-a series of ideas, approaches and methods taken from existing visualization research and practice-deployed and developed to support modelling of the COVID-19 pandemic. Structured independent commentary on these efforts is synthesized through iterative reflection to develop: evidence of the effectiveness and value of visualization in this context; open problems upon which the research communities may focus; guidance for future activity of this type and recommendations to safeguard the achievements and promote, advance, secure and prepare for future collaborations of this kind. In describing and comparing a series of related projects that were undertaken in unprecedented conditions, our hope is that this unique report, and its rich interactive supplementary materials, will guide the scientific community in embracing visualization in its observation, analysis and modelling of data as well as in disseminating findings. Equally we hope to encourage the visualization community to engage with impactful science in addressing its emerging data challenges. If we are successful, this showcase of activity may stimulate mutually beneficial engagement between communities with complementary expertise to address problems of significance in epidemiology and beyond. See https://ramp-vis.github.io/RAMPVIS-PhilTransA-Supplement/. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Uncovering chains of infections through spatio-temporal and visual analysis of COVID-19 contact traces

A major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV-2 infection clusters. Prevention of further disease spread requires a comprehensive registration of the connections between individuals and clusters. Due to the high number of infections with unknown origin, the healthcare analysts need to identify connected cases and clusters through accumulated epidemiological knowledge and the metadata of the infections in their database. Here we contribute a visual analytics dashboard to identify, assess and visualize clusters in COVID-19 contact tracing networks. Additionally, we demonstrate how graph-based machine learning methods can be used to find missing links between infection clusters and thus support the mission to get a comprehensive view on infection events. This work was developed through close collaboration with DPHs in Germany. We argue how our dashboard supports the identification of clusters by public health experts, discuss ongoing developments and possible extensions.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Making data map-worthy-enhancing routine malaria data to support surveillance and mapping of Plasmodium falciparum anti-malarial resistance in a pre-elimination sub-Saharan African setting: a molecular and spatiotemporal epidemiology study

Background

Independent emergence and spread of artemisinin-resistant Plasmodium falciparum malaria have recently been confirmed in Africa, with molecular markers associated with artemisinin resistance increasingly detected. Surveillance to promptly detect and effectively respond to anti-malarial resistance is generally suboptimal in Africa, especially in low transmission settings where therapeutic efficacy studies are often not feasible due to recruitment challenges. However, these communities may be at higher risk of anti-malarial resistance.

Methods

From March 2018 to February 2020, a sequential mixed-methods study was conducted to evaluate the feasibility of the near-real-time linkage of individual patient anti-malarial resistance profiles with their case notifications and treatment response reports, and map these to fine scales in Nkomazi sub-district, Mpumalanga, a pre-elimination area in South Africa.

Results

Plasmodium falciparum molecular marker resistance profiles were linked to 55.1% (2636/4787) of notified malaria cases, 85% (2240/2636) of which were mapped to healthcare facility, ward and locality levels. Over time, linkage of individual malaria case demographic and molecular data increased to 75.1%. No artemisinin resistant validated/associated  Kelch-13 mutations were detected in the 2385 PCR positive samples. Almost all 2812 samples assessed for lumefantrine susceptibility carried the wildtype mdr86ASN and crt76LYS alleles, potentially associated with decreased lumefantrine susceptibility.

Conclusion

Routine near-real-time mapping of molecular markers associated with anti-malarial drug resistance on a fine spatial scale provides a rapid and efficient early warning system for emerging resistance. The lessons learnt here could inform scale-up to provincial, national and regional malaria elimination programmes, and may be relevant for other antimicrobial resistance surveillance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-022-04224-4.

Transmission visualizations of healthcare infection clusters: A scoping review

OBJECTIVE

To evaluate infectious pathogen transmission data visualizations in outbreak publications.

DESIGN

Scoping review.

METHODS

Medline was searched for outbreak investigations of infectious diseases within healthcare facilities that included ≥1 data visualization of transmission using data observable by an infection preventionist showing temporal and/or spatial relationships. Abstracted data included the nature of the cluster(s) (pathogen, scope of transmission, and individuals involved) and data visualization characteristics including visualization type, transmission elements, and software.

RESULTS

From 1,957 articles retrieved, we analyzed 30 articles including 37 data visualizations. The median cluster size was 20.5 individuals (range, 7-1,963) and lasted a median of 214 days (range, 12-5,204). Among the data visualization types, 10 (27%) were floor-plan transmission maps, 6 (16%) were timelines, 11 (30%) were transmission networks, 3 (8%) were Gantt charts, 4 (11%) were cluster map, and 4 (11%) were other types. In addition, 26 data visualizations (70%) contained spatial elements, 26 (70%) included person type, and 19 (51%) contained time elements. None of the data visualizations contained contagious periods and only 2 (5%) contained symptom-onset date.

CONCLUSIONS

The data visualizations of healthcare-associated infectious disease outbreaks in the systematic review were diverse in type and visualization elements, though no data visualization contained all elements important to deriving hypotheses about transmission pathways. These findings aid in understanding the visualizing transmission pathways by describing essential elements of the data visualization and will inform the creation of a standardized mapping tool to aid in earlier initiation of interventions to prevent transmission.

Detecting and Responding to Information Overload With an Adaptive User Interface

OBJECTIVE

The objective of this study was to develop and evaluate an adaptive user interface that could detect states of operator information overload and calibrate the amount of information on the screen.

BACKGROUND

Machine learning can detect changes in operating context and trigger adaptive user interfaces (AUIs) to accommodate those changes. Operator attentional state represents a promising aspect of operating context for triggering AUIs. Behavioral rather than physiological indices can be used to infer operator attentional state.

METHOD

In Experiment 1, a network analysis task sought to induce states of information overload relative to a baseline. Streams of interaction data were taken from these two states and used to train machine learning classifiers. We implemented these classifiers in Experiment 2 to drive an AUI that automatically calibrated the amount of information displayed to operators.

RESULTS

Experiment 1 successfully induced information overload in participants, resulting in lower accuracy, slower completion time, and higher workload. A series of machine learning classifiers detected states of information overload significantly above chance level. Experiment 2 identified four clusters of users who responded significantly differently to the AUIs. The AUIs benefited performance, completion time, and workload in three clusters.

CONCLUSION

Behavioral indices can successfully detect states of information overload and be used to effectively drive an AUI for some user groups. The success of AUIs may be contingent on characteristics of the user group.

APPLICATION

This research applies to domains seeking real-time assessments of user attentional or psychological state.

Visual Analytics for the Coronavirus COVID-19 Pandemic

The coronavirus disease COVID-19 was first reported in Wuhan, China, on December 31, 2019. The disease has since spread throughout the world, affecting 227.2 million individuals and resulting in 4,672,629 deaths as of September 9, 2021, according to the Johns Hopkins University Center for Systems Science and Engineering. Numerous sources track and report information on the disease, including Johns Hopkins itself, with its well-known Novel Coronavirus Dashboard. We were also interested in providing information on the pandemic. However, rather than duplicating existing resources, we focused on integrating sophisticated data analytics and visualization for region-to-region comparison, trend prediction, and testing and vaccination analysis. Our high-level goal is to provide visualizations of predictive analytics that offer policymakers and the general public insight into the current pandemic state and how it may progress into the future. Data are visualized using a web-based jQuery+Tableau dashboard. The dashboard allows both novice viewers and domain experts to gain useful insights into COVID-19's current and predicted future state for different countries and regions of interest throughout the world.

Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year

The present work aims to give an overview on the international scientific papers related to the territorial spreading of SARS-CoV-2, with a specific focus upon applied quantitative geography and territorial analysis, to define a general structure for epidemiological geography research. The target publications were based on GIS spatial analysis, both in the sense of topological analysis and descriptive statistics or lato sensu geographical approaches. The first basic purpose was to organize and enhance the vast knowledge developments generated hitherto by the first pandemic that was studied "on-the-fly" all over the world. The consequent target was to investigate to what extent researchers in geography were able to draw scientifically consistent conclusions about the pandemic evolution, as well as whether wider generalizations could be reasonably claimed. This implied an analysis and a comparison of their findings. Finally, we tested what geographic approaches can say about the pandemic and whether a reliable spatial analysis routine for mapping infectious diseases could be extrapolated. We selected papers proposed for publication during 2020 and 209 articles complied with our parameters of query. The articles were divided in seven categories to enhance existing commonalities. In some cases, converging conclusions were extracted, and generalizations were derived. In other cases, contrasting or inconsistent findings were found, and possible explanations were provided. From the results of our survey, we extrapolated a routine for the production of epidemiological geography analyses, we highlighted the different steps of investigation that were attained, and we underlined the most critical nodes of the methodology. Our findings may help to point out what are the most critical conceptual challenges of epidemiological mapping, and where it might improve to engender informed conclusions and aware outcomes.

Interactive Visualization Applications in Population Health and Health Services Research: Systematic Scoping Review

BACKGROUND

Simple visualizations in health research data, such as scatter plots, heat maps, and bar charts, typically present relationships between 2 variables. Interactive visualization methods allow for multiple related facets such as numerous risk factors to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big health care data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support.

OBJECTIVE

The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods, and tools being used in population health and health services research (HSR) and their subdomains in the last 15 years, from January 1, 2005, to March 30, 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals, and co-design of applications.

METHODS

We adapted standard scoping review guidelines with a peer-reviewed search strategy: 2 independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sectors. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and HSR, as well as their subdomains such as epidemiologic surveillance, health resource planning, access, and use and costs among diverse clinical and demographic populations.

RESULTS

In this companion review to our earlier systematic synthesis of the literature on visual analytics applications, we present findings in 6 major themes of interactive visualization applications developed for 8 major problem categories. We found a wide application of interactive visualization methods, the major ones being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality, and studying medication use patterns. The data sources included mostly secondary administrative and electronic medical record data. In addition, at least two-thirds of the applications involved participatory co-design approaches while introducing a distinct category, embedded research, within co-design initiatives. These applications were in response to an identified need for data-driven insights into knowledge generation and decision support. We further discuss the opportunities stemming from the use of interactive visualization methods in studying global health; inequities, including social determinants of health; and other related areas. We also allude to the challenges in the uptake of these methods.

CONCLUSIONS

Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and HSR. Such applications are being fast used by academic and health care agencies for knowledge discovery, hypotheses generation, and decision support.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/14019.

Overview of national health reporting in the EU and quality criteria for public health reports - results of the Joint Action InfAct

BACKGROUND

Health reporting shall provide up-to-date health-related data to inform policy-makers, researchers and the public. To this end, health reporting formats should be tailored to the needs and competencies of the target groups and provide comparable and high-quality information. Within the Joint Action on Health Information 'InfAct', we aimed at gaining an overview of health reporting practices in the EU Member States and associated countries, and developed quality criteria for the preparation of public health reports. The results are intended to facilitate making health information adequately available while reducing inequalities in health reporting across the EU.

METHODS

A web-based desk research was conducted among EU Member States and associated countries to generate an overview of different formats of national health reporting and their respective target groups. To identify possible quality criteria for public health reports, an exploratory literature review was performed and earlier projects were analysed. The final set of criteria was developed in exchange with experts from the InfAct consortium.

RESULTS

The web-based desk research showed that public health reports are the most frequently used format across countries (94%), most often addressed to scientists and researchers (51%), politicians and decision-makers (41%). However, across all reporting formats, the general public is the most frequently addressed target group. With regards to quality criteria for public health reports, the literature review has yielded few results. Therefore, two earlier projects served as main sources: the 'Evaluation of National and Regional Public Health Reports' and the guideline 'Good Practice in Health Reporting'from Germany. In collaboration with experts, quality criteria were identified and grouped into eight categories, ranging from topic selection to presentation of results, and compiled in a checklist for easy reference.

CONCLUSION

Health reporting practices in the EU are heterogeneous across Member States. The assembled quality criteria are intended to facilitate the preparation, dissemination and access to better comparable high-quality public health reports as a basis for evidence-based decision-making. A comprehensive conceptual and integrative approach that incorporates the policy perspective would be useful to investigate which dissemination strategies are the most suitable for specific requirements of the targeted groups.

Evaluating the usability and acceptability of a geographical information system (GIS) prototype to visualise socio-economic and public health data

BACKGROUND

Understanding the impact of socio-economic inequality on health outcomes is arguably more relevant than ever before given the global repercussions of Covid-19. With limited resources, innovative methods to track disease, population needs, and current health and social service provision are essential. To best make use of currently available data, there is an increasing reliance on technology. One approach of interest is the implementation and integration of mapping software. This research aimed to determine the usability and acceptability of a methodology for mapping public health data using GIS technology.

METHODS

Prototype multi-layered interactive maps were created demonstrating relationships between socio-economic and health data (vaccination and admission rates). A semi-structured interview schedule was developed, including a validated tool known as the System Usability Scale (SUS), which assessed the usability of the mapping model with five stakeholder (SH) groups. Fifteen interviews were conducted across the 5 SH and analysed using content analysis. A Kruskal-Wallis H test was performed to determine any statistically significant difference for the SUS scores across SH. The acceptability of the model was not affected by the individual use of smart technology among SHs.

RESULTS

The mean score from the SUS for the prototype mapping models was 83.17 out of 100, indicating good usability. There was no statistically significant difference in the usability of the maps among SH (p = 0.094). Three major themes emerged with respective sub-themes from the interviews including: (1) Barriers to current use of data (2) Design strengths and improvements (3) Multiple benefits and usability of the mapping model.

CONCLUSION

Irrespective of variations in demographics or use of smart technology amongst interviewees, there was no significant difference in the usability of the model across the stakeholder groups. The average SUS score for a new system is 68. A score of 83.17 was calculated, indicative of a "good" system, as falling within the top 10% of scores. This study has provided a potential digital model for mapping public health data. Furthermore, it demonstrated the need for such a digital solution, as well as its usability and future utilisation avenues among SH.

The Visual Dictionary of Antimicrobial Stewardship, Infection Control, and Institutional Surveillance Data

Objectives: Data and data visualization are integral parts of (clinical) decision-making in general and stewardship (antimicrobial stewardship, infection control, and institutional surveillance) in particular. However, systematic research on the use of data visualization in stewardship is lacking. This study aimed at filling this gap by creating a visual dictionary of stewardship through an assessment of data visualization (i.e., graphical representation of quantitative information) in stewardship research. Methods: A random sample of 150 data visualizations from published research articles on stewardship were assessed (excluding geographical maps and flowcharts). The visualization vocabulary (content) and design space (design elements) were combined to create a visual dictionary. Additionally, visualization errors, chart junk, and quality were assessed to identify problems in current visualizations and to provide improvement recommendations. Results: Despite a heterogeneous use of data visualization, distinct combinations of graphical elements to reflect stewardship data were identified. In general, bar (n = 54; 36.0%) and line charts (n = 42; 28.1%) were preferred visualization types. Visualization problems comprised color scheme mismatches, double y-axis, hidden data points through overlaps, and chart junk. Recommendations were derived that can help to clarify visual communication, improve color use for grouping/stratifying, improve the display of magnitude, and match visualizations to scientific standards. Conclusion: Results of this study can be used to guide data visualization creators in designing visualizations that fit the data and visual habits of the stewardship target audience. Additionally, the results can provide the basis to further expand the visual dictionary of stewardship toward more effective visualizations that improve data insights, knowledge, and clinical decision-making.

Dynamic graph and polynomial chaos based models for contact tracing data analysis and optimal testing prescription

In this study, we address three important challenges related to disease transmissions such as the COVID-19 pandemic, namely, (a) providing an early warning to likely exposed individuals, (b) identifying individuals who are asymptomatic, and (c) prescription of optimal testing when testing capacity is limited. First, we present a dynamic-graph based SEIR epidemiological model in order to describe the dynamics of the disease propagation. Our model considers a dynamic graph/network that accounts for the interactions between individuals over time, such as the ones obtained by manual or automated contact tracing, and uses a diffusion-reaction mechanism to describe the state dynamics. This dynamic graph model helps identify likely exposed/infected individuals to whom we can provide early warnings, even before they display any symptoms and/or are asymptomatic. Moreover, when the testing capacity is limited compared to the population size, reliable estimation of individual's health state and disease transmissibility using epidemiological models is extremely challenging. Thus, estimation of state uncertainty is paramount for both eminent risk assessment, as well as for closing the tracing-testing loop by optimal testing prescription. Therefore, we propose the use of arbitrary Polynomial Chaos Expansion, a popular technique used for uncertainty quantification, to represent the states, and quantify the uncertainties in the dynamic model. This design enables us to assign uncertainty of the state of each individual, and consequently optimize the testing as to reduce the overall uncertainty given a constrained testing budget. These tools can also be used to optimize vaccine distribution to curb the disease spread when limited vaccines are available. We present a few simulation results that illustrate the performance of the proposed framework, and estimate the impact of incomplete contact tracing data.

Electronic data collection, management and analysis tools used for outbreak response in low- and middle-income countries: a systematic review and stakeholder survey

BACKGROUND

Use of electronic data collection, management and analysis tools to support outbreak response is limited, especially in low income countries. This can hamper timely decision-making during outbreak response. Identifying available tools and assessing their functions in the context of outbreak response would support appropriate selection and use, and likely more timely data-driven decision-making during outbreaks.

METHODS

We conducted a systematic review and a stakeholder survey of the Global Outbreak Alert and Response Network and other partners to identify and describe the use of, and technical characteristics of, electronic data tools used for outbreak response in low- and middle-income countries. Databases included were MEDLINE, EMBASE, Global Health, Web of Science and CINAHL with publications related to tools for outbreak response included from January 2010-May 2020. Software tool websites of identified tools were also reviewed. Inclusion and exclusion criteria were applied and counts, and proportions of data obtained from the review or stakeholder survey were calculated.

RESULTS

We identified 75 electronic tools including for data collection (33/75), management (13/75) and analysis (49/75) based on data from the review and survey. Twenty-eight tools integrated all three functionalities upon collection of additional information from the tool developer websites. The majority were open source, capable of offline data collection and data visualisation. EpiInfo, KoBoCollect and Open Data Kit had the broadest use, including for health promotion, infection prevention and control, and surveillance data capture. Survey participants highlighted harmonisation of data tools as a key challenge in outbreaks and the need for preparedness through training front-line responders on data tools. In partnership with the Global Health Network, we created an online interactive decision-making tool using data derived from the survey and review.

CONCLUSIONS

Many electronic tools are available for data -collection, -management and -analysis in outbreak response, but appropriate tool selection depends on knowledge of tools' functionalities and capabilities. The online decision-making tool created to assist selection of the most appropriate tool(s) for outbreak response helps by matching requirements with functionality. Applying the tool together with harmonisation of data formats, and training of front-line responders outside of epidemic periods can support more timely data-driven decision making in outbreaks.

Building an Interactive Geospatial Visualization Application for National Health Care-Associated Infection Surveillance: Development Study

Background

The Centers for Disease Control and Prevention’s (CDC’s) National Healthcare Safety Network (NHSN) is the most widely used health care–associated infection (HAI) and antimicrobial use and resistance surveillance program in the United States. Over 37,000 health care facilities participate in the program and submit a large volume of surveillance data. These data are used by the facilities themselves, the CDC, and other agencies and organizations for a variety of purposes, including infection prevention, antimicrobial stewardship, and clinical quality measurement. Among the summary metrics made available by the NHSN are standardized infection ratios, which are used to identify HAI prevention needs and measure progress at the national, regional, state, and local levels.

Objective

To extend the use of geospatial methods and tools to NHSN data, and in turn to promote and inspire new uses of the rendered data for analysis and prevention purposes, we developed a web-enabled system that enables integrated visualization of HAI metrics and supporting data.

Methods

We leveraged geocoding and visualization technologies that are readily available and in current use to develop a web-enabled system designed to support visualization and interpretation of data submitted to the NHSN from geographically dispersed sites. The server–client model–based system enables users to access the application via a web browser.

Results

We integrated multiple data sets into a single-page dashboard designed to enable users to navigate across different HAI event types, choose specific health care facility or geographic locations for data displays, and scale across time units within identified periods. We launched the system for internal CDC use in January 2019.

Conclusions

CDC NHSN statisticians, data analysts, and subject matter experts identified opportunities to extend the use of geospatial methods and tools to NHSN data and provided the impetus to develop NHSNViz. The development effort proceeded iteratively, with the developer adding or enhancing functionality and including additional data sets in a series of prototype versions, each of which incorporated user feedback. The initial production version of NHSNViz provides a new geospatial analytic resource built in accordance with CDC user requirements and extensible to additional users and uses in subsequent versions.

OUTBREAK: a user-friendly georeferencing online tool for disease surveillance

The current COVID-19 pandemic has already claimed more than 3.7 million victims and it will cause more deaths in the coming months. Tools that track the number and locations of cases are critical for surveillance and help in making policy decisions for controlling the outbreak. However, the current surveillance web-based dashboards run on proprietary platforms, which are often expensive and require specific computational knowledge. We developed a user-friendly web tool, named OUTBREAK, that facilitates epidemic surveillance by showing in an animated graph the timeline and geolocations of cases of an outbreak. It permits even non-specialist users to input data most conveniently and track outbreaks in real-time. We applied our tool to visualize the SARS 2003, MERS, and COVID19 epidemics, and provided them as examples on the website. Through the zoom feature, it is also possible to visualize cases at city and even neighborhood levels. We made the tool freely available at https://outbreak.sysbio.tools/ . OUTBREAK has the potential to guide and help health authorities to intervene and minimize the effects of outbreaks.

Predicting Spatial Patterns of Sindbis Virus (SINV) Infection Risk in Finland Using Vector, Host and Environmental Data

Pogosta disease is a mosquito-borne infection, caused by Sindbis virus (SINV), which causes epidemics of febrile rash and arthritis in Northern Europe and South Africa. Resident grouse and migratory birds play a significant role as amplifying hosts and various mosquito species, including Aedes cinereus, Culex pipiens, Cx. torrentium and Culiseta morsitans are documented vectors. As specific treatments are not available for SINV infections, and joint symptoms may persist, the public health burden is considerable in endemic areas. To predict the environmental suitability for SINV infections in Finland, we applied a suite of geospatial and statistical modeling techniques to disease occurrence data. Using an ensemble approach, we first produced environmental suitability maps for potential SINV vectors in Finland. These suitability maps were then combined with grouse densities and environmental data to identify the influential determinants for SINV infections and to predict the risk of Pogosta disease in Finnish municipalities. Our predictions suggest that both the environmental suitability for vectors and the high risk of Pogosta disease are focused in geographically restricted areas. This provides evidence that the presence of both SINV vector species and grouse densities can predict the occurrence of the disease. The results support material for public-health officials when determining area-specific recommendations and deliver information to health care personnel to raise awareness of the disease among physicians.

Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities

The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect "active" and "emerging" space-time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25-June 7, 2020, and February 25-July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 "active" clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.

Real-Time Standard Analysis of Disease Investigation (SADI)-A Toolbox Approach to Inform Disease Outbreak Response

An incursion of an important exotic transboundary animal disease requires a prompt and intensive response. The routine analysis of up-to-date data, as near to real time as possible, is essential for the objective assessment of the patterns of disease spread or effectiveness of control measures and the formulation of alternative control strategies. In this paper, we describe the Standard Analysis of Disease Investigation (SADI), a toolbox for informing disease outbreak response, which was developed as part of New Zealand's biosecurity preparedness. SADI was generically designed on a web-based software platform, Integrated Real-time Information System (IRIS). We demonstrated the use of SADI for a hypothetical foot-and-mouth disease (FMD) outbreak scenario in New Zealand. The data standards were set within SADI, accommodating a single relational database that integrated the national livestock population data, outbreak data, and tracing data. We collected a well-researched, standardised set of 16 epidemiologically relevant analyses for informing the FMD outbreak response, including farm response timelines, interactive outbreak/network maps, stratified epidemic curves, estimated dissemination rates, estimated reproduction numbers, and areal attack rates. The analyses were programmed within SADI to automate the process to generate the reports at a regular interval (daily) using the most up-to-date data. Having SADI prepared in advance and the process streamlined for data collection, analysis and reporting would free a wider group of epidemiologists during an actual disease outbreak from solving data inconsistency among response teams, daily “number crunching,” or providing largely retrospective analyses. Instead, the focus could be directed into enhancing data collection strategies, improving data quality, understanding the limitations of the data available, interpreting the set of analyses, and communicating their meaning with response teams, decision makers and public in the context of the epidemic.

COVID19-world: a shiny application to perform comprehensive country-specific data visualization for SARS-CoV-2 epidemic

Background

Data analysis and visualization is an essential tool for exploring and communicating findings in medical research, especially in epidemiological surveillance.

Results

Data on COVID-19 diagnosed cases and mortality, from January 1st, 2020, onwards is collected automatically from the European Centre for Disease Prevention and Control (ECDC). We have developed a Shiny application for data visualization and analysis of several indicators to follow the SARS-CoV-2 epidemic using ECDC data. A country-specific tool for basic epidemiological surveillance, in an interactive and user-friendly manner. The available analyses cover time trends and projections, attack rate, population fatality rate, case fatality rate, and basic reproduction number.

Conclusions

The COVID19-World online web application systematically produces daily updated country-specific data visualization and analysis of the SARS-CoV-2 epidemic worldwide. The application may help for a better understanding of the SARS-CoV-2 epidemic worldwide.

[Health reporting as part of public health surveillance: the example of diabetes]

The continuous collection and analysis of health data on relevant diseases (surveillance) is at the core of public health. The surveillance enables the implementation of measures to protect the populations' health. Therefore, relevant information needs to be provided in a timely and target-group-specific manner to the respective stakeholders.A dissemination strategy supports the effective communication of results and considers three key questions: (1) "What content is relevant to the surveillance?", (2) "Who requires which information?" and (3) "How are the results disseminated to the target audience?" In this context, digitalisation allows for novel possibilities in the design of publication formats.Since 2015, diabetes surveillance has been established at the Robert Koch Institute. Within a structured process of consensus, we defined four fields of action relevant for health policy including 40 indicators. Thereafter, we developed the first publication formats in collaboration with the scientific advisory board of the project that reflected novel possibilities offered by digitalisation. In addition to articles in scientific journals, the essential formats of the first project phase comprise the report "Diabetes in Germany" and a website including interactive visualisations of results. Additional posts on Twitter and YouTube are used to increase coverage.In addition to the further development of the indicator set, the focus of the next project phase is the advancement of the dissemination towards user- and action-oriented reporting. In close exchange with the scientific advisory board, we aim to explore the requirements of the target audience and reflect them in the design of further publication formats.

Poultry Professionals' Perception of Participation in Voluntary Disease Mapping and Monitoring Programs in the United States: A Cluster Analysis

The development and implementation of disease mapping and monitoring programs can be useful tools for rapid communication and control of endemic and epidemic infectious diseases affecting the food animal industry. Commercial livestock producers have traditionally been reluctant to share information related to animal health, challenging the large-scale implementation of such monitoring and mapping programs. The main objective of this study was to assess the perception of poultry professionals toward disease mapping and monitoring programs and to identify groups of poultry professionals with similar perceptions and attitudes toward these projects. We conducted a survey to identify the perceived risks and benefits to be able to properly address them and encourage industry participation in the future. An anonymous online survey was developed and distributed to poultry professionals through industry and professional associations. The participant's demographic information and perceptions of risk and benefits from participation on voluntary poultry disease mapping and monitoring programs were collected. Multiple correspondence analysis and hierarchical clustering on principal components were performed to identify groups of professionals with similar characteristics. A total of 63 participants from 21 states filled out the survey. The cluster analysis yielded two distinct groups of respondents, each including approximately 50% of respondents. Cluster 1 subjects could be characterized as optimistic, perceiving major benefits of sharing farm-level poultry disease information. However, they also had major concerns, mostly related to potential accidental data release and providing competitive advantages to rival companies. Cluster 2 subjects were characterized as perceiving a lesser degree of benefits from sharing farm-level poultry disease information. This second cluster mostly included production and service technicians. The roles and perceptions of risk and benefits of the participants contributed significantly to cluster assignment, while the represented commodity and geographic location in the United States did not. Successful development of voluntary poultry disease mapping and monitoring programs in the future will require that different sectors of poultry professionals be approached in different manners in order to highlight the benefits of the programs and to achieve maximum participation.

Epidemic investigations within an arm's reach - role of google maps during an epidemic outbreak

Epidemics such as novel Coronavirus 2019 (COVID-19) can be contained and the rate of infection reduced by public health measures such as epidemiologic inquiries and social distancing. Epidemiologic inquiry requires resources and time which may not be available or reduced when the outbreak is excessive. We evaluated the use of Google Maps Timeline (GMTL) for creating spatial epidemiologic timelines. The study compares locations, routes, and means of transport between GMTL and user recall for 17 suitable users who were recruited during March 2020. They were interviewed about their timeline using the Timeline Follow-Back (TLFB) method which was then compared to their GMTL and discrepancies between both methods were addressed. Interviewer conclusions were divided into categories: (1) participant recalled, (2) no recall (until shown). Categories were subdivided by GMTL accuracy: [a] GMTL accurate, [b] GMTL inaccurate, [c] GMTL data missing. A total of 362 locations were compared. Participants recalled 322 (88.95% SD = 8.55) locations compared with 40 (11.05%, SD = 2.05) locations not recalled. There were 304 locations found accurate on GMTL (83.98%, SD = 9.49), 29 (8.01%, SD = 1.11) inaccurate locations, and 29 (8.01%, SD = 0.54) missing locations. The total discrepancy between GMTL and TLFB recall was 95 cases (26.24%, SD = 3.25). Despite variations between users, Google Maps with GMTL technology may be useful in identifying potentially exposed individuals in a pandemic. It is especially useful when resources are limited. Further research is required with a larger number of users who are undergoing a real epidemiologic investigation to corroborate findings and establish further recommendations.

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques provides a list of options for researchers who are new to population-level spatial eco-epidemiology. Here, we are conducting a narrative review to provide an overview of the multiple available SATs, and introducing a framework for choosing among them when addressing common research questions across disciplines. The framework is comprised of three stages: (a) pre-hypothesis testing stage, in which hypotheses regarding the spatial dependence of events are generated; (b) primary hypothesis testing stage, in which the existence of spatial dependence and patterns are tested; and (c) secondary-hypothesis testing and spatial modeling stage, in which predictions and inferences were made based on the identified spatial dependences and associated covariates. In this step-wise process, six key research questions are formulated, and the answers to those questions should lead researchers to select one or more methods from four broad categories of SATs: (T1) visualization and descriptive analysis; (T2) spatial/spatiotemporal dependence and pattern recognition; (T3) spatial smoothing and interpolation; and (T4) geographic correlation studies (i.e., spatial modeling and regression). The SATs described here include both those used for decades and also other relatively new tools. Through this framework review, we intend to facilitate the choice among available SATs and promote their interdisciplinary use to support improving human, animal, and ecosystem health.

Exploring Urban Spatial Features of COVID-19 Transmission in Wuhan Based on Social Media Data

During the early stage of the COVID-19 outbreak in Wuhan, there was a short run of medical resources, and Sina Weibo, a social media platform in China, built a channel for novel coronavirus pneumonia patients to seek help. Based on the geo-tagging Sina Weibo data from February 3rd to 12th, 2020, this paper analyzes the spatiotemporal distribution of COVID-19 cases in the main urban area of Wuhan and explores the urban spatial features of COVID-19 transmission in Wuhan. The results show that the elderly population accounts for more than half of the total number of Weibo help seekers, and a close correlation between them has also been found in terms of spatial distribution features, which confirms that the elderly population is the group of high-risk and high-prevalence in the COVID-19 outbreak, needing more attention of public health and epidemic prevention policies. On the other hand, the early transmission of COVID-19 in Wuhan could be divide into three phrases: Scattered infection, community spread, and full-scale outbreak. This paper can help to understand the spatial transmission of COVID-19 in Wuhan, so as to propose an effective public health preventive strategy for urban space optimization.

Social Network Analysis of Patient Movement Across Health Care Entities in Orange County, Florida

OBJECTIVE

Multidrug-resistant organisms (MDROs) are continually emerging and threatening health care systems. Little attention has been paid to the effect of patient transfers on MDRO dissemination among health care entities in health care systems. In this study, the Florida Department of Health in Orange County (DOH-Orange) developed a baseline social network analysis of patient movement across health care entities in Orange County, Florida, and regionally, within 6 surrounding counties in Central Florida.

MATERIALS AND METHODS

DOH-Orange constructed 2 directed network sociograms-graphic visualizations that show the direction of relationships (ie, county and regional)-by using 2016 health insurance data from the Centers for Medicare & Medicaid Services, which include metrics that could be useful for local public health interventions, such as MDRO outbreaks.

RESULTS

We found that both our county and regional networks were sparse and centralized. The county-level network showed that acute-care hospitals had the highest influence on controlling the flow of patients between health care entities that would otherwise not be connected. The regional-level network showed that post-acute-care hospitals and other facilities (behavioral hospitals and mental health/substance abuse facilities) served as the primary controls for flow of patients between health care entities. The most prominent health care entities in both networks were the same 2 acute-care hospitals.

PRACTICE IMPLICATIONS

Social network analysis can help local public health officials respond to MDRO outbreak investigations by determining which health care facilities are the main contributors of dissemination of MDROs or are at high risk of receiving patients with MDROs. This information can help epidemiologists prioritize prevention efforts and develop county- or regional-specific interventions to control and halt MDRO transmission across a health care network.

Heat map visualization for electrocardiogram data analysis

BACKGROUND

Most electrocardiogram (ECG) studies still take advantage of traditional statistical functions, and the results are mostly presented in tables, histograms, and curves. Few papers display ECG data by visual means. The aim of this study was to analyze and show data for electrocardiographic left ventricular hypertrophy (LVH) with ST-segment elevation (STE) by a heat map in order to explore the feasibility and clinical value of heat mapping for ECG data visualization.

METHODS

We sequentially collected the electrocardiograms of inpatients in the First Affiliated Hospital of Shantou University Medical College from July 2015 to December 2015 in order to screen cases of LVH with STE. HemI 1.0 software was used to draw heat maps to display the STE of each lead of each collected ECG. Cluster analysis was carried out based on the heat map and the results were drawn as tree maps (pedigree maps) in the heat map.

RESULTS

In total, 60 cases of electrocardiographic LVH with STE were screened and analyzed. STE leads were mainly in the V₁, V₂ and V₃ leads. The ST-segment shifts of each lead of each collected ECG could be conveniently visualized in the heat map. According to cluster analysis in the heat map, STE leads were clustered into two categories, comprising of the right precordial leads (V₁, V₂, V₃) and others (V₄, V₅, V₆, I, II, III, aVF, aVL, aVR). Moreover, the STE amplitude in 40% (24 out of 60) of cases reached the threshold specified in the STEMI guideline. These cases also could be fully displayed and visualized in the heat map. Cluster analysis in the heat map showed that the III, aVF and aVR leads could be clustered together, the V₁, V₂, V₃ and V₄ leads could be clustered together, and the V₅, V₆, I and aVL leads could be clustered together.

CONCLUSION

Heat maps and cluster analysis can be used to fully display every lead of each electrocardiogram and provide relatively comprehensive information.

Using GIS for Disease Mapping and Clustering in Jeddah, Saudi Arabia

Geographic information systems (GIS) can be used to map the geographical distribution of the prevalence of disease, trends in disease transmission, and to spatially model environmental aspects of disease occurrence. The aim of this study is to discuss a GIS application created to produce mapping and cluster modeling of three diseases in Jeddah, Saudi Arabia: diabetes, asthma, and hypertension. Data about these diseases were obtained from health centers’ registered patient records. These data were spatially evaluated using several spatial–statistical analytical models, including kernel and hotspot models. These models were created to explore and display the disparate patterns of the selected diseases and to illustrate areas of high concentration, and may be invaluable in understanding local patterns of diseases and their geographical associations.