A pandemic influenza modeling and visualization tool

Are interactive and tailored data visualizations effective in promoting flu vaccination among the elderly? Evidence from a randomized experiment

OBJECTIVE

Although interactive data visualizations are increasingly popular for health communication, it remains to be seen what design features improve psychological and behavioral targets. This study experimentally tested how interactivity and descriptive titles may influence perceived susceptibility to the flu, intention to vaccinate, and information recall, particularly among older adults.

MATERIALS AND METHODS

We created data visualization dashboards on flu vaccinations, tested in a 2 (explanatory text vs none) × 3 (interactive + tailored, static + tailored, static + nontailored) + questionnaire-only control randomized between-participant online experiment (N   = 1378).

RESULTS

The flu dashboards significantly increased perceived susceptibility to the flu compared to the control: static+nontailored dashboard, b = 0.14, P = .049; static-tailored, b = 0.16, P = .028; and interactive+tailored, b = 0.15, P = .039. Interactive dashboards potentially decreased recall particularly among the elderly (moderation by age: b = -0.03, P = .073). The benefits of descriptive text on recall were larger among the elderly (interaction effects: b = 0.03, P = .025).

DISCUSSION

Interactive dashboards with complex statistics and limited textual information are widely used in health and public health but may be suboptimal for older individuals. We experimentally showed that adding explanatory text on visualizations can increase information recall particularly for older populations.

CONCLUSION

We did not find evidence to support the effectiveness of interactivity in data visualizations on flu vaccination intentions or on information recall. Future research should examine what types of explanatory text can best support improved health outcomes and intentions in other contexts. Practitioners should consider whether interactivity is optimal in data visualization dashboards for their populations.

Designing an infographic webtool for public health

OBJECTIVE

To create and evaluate a public health informatics tool, Florence, for communicating information to the public.

MATERIALS AND METHODS

This user-centered design study included 3 phases: (1) an interview and survey study with public health practitioners to assess needs for creating infographics; (2) the application of assessment findings and public health-motivated design guidelines to the design and development of a public health-specific infographic design tool; and (3) a feasibility and usability study to evaluate the feasibility and usability of the tool.

RESULTS

In phase 1, participants noted the importance of tailoring infographics to an audience and wanted flexible tools along with design guidance to help make fewer design decisions. In phase 2, we developed a prototype tool with: (1) layout and functionality familiar to PH users, (2) quick and intuitive ways to add and modify data in visualizations, and (3) health-focused visual elements. In phase 3, participants found Florence to be usable, providing an intuitive and straightforward experience, and that the focus on public health was useful.

DISCUSSION

Based on needs assessments and existing literature, we created Florence along with public health practitioners to address their domain specific needs, ultimately leading to a tool that participants in our study deemed useful. Future research can build on our work to develop user-centered tools to meet their needs.

CONCLUSION

Infographics are important for public health communication. Creating user-centered solutions to address the unique needs of public health can support communication efforts.

Geo-Storylines: Integrating Maps into Storyline Visualizations

Storyline visualizations are a powerful way to compactly visualize how the relationships between people evolve over time. Real-world relationships often also involve space, for example the cities that two political rivals visited together or alone over the years. By default, Storyline visualizations only show implicitly geospatial co-occurrence between people (drawn as lines), by bringing their lines together. Even the few designs that do explicitly show geographic locations only do so in abstract ways (e.g., annotations) and do not communicate geospatial information, such as the direction or extent of their political campains. We introduce Geo-Storylines, a collection of visualisation designs that integrate geospatial context into Storyline visualizations, using different strategies for compositing time and space. Our contribution is twofold. First, we present the results of a sketching workshop with 11 participants, that we used to derive a design space for integrating maps into Storylines. Second, by analyzing the strengths and weaknesses of the potential designs of the design space in terms of legibility and ability to scale to multiple relationships, we extract the three most promising: Time Glyphs, Coordinated Views, and Map Glyphs. We compare these three techniques first in a controlled study with 18 participants, under five different geospatial tasks and two maps of different complexity. We additionally collected informal feedback about their usefulness from domain experts in data journalism. Our results indicate that, as expected, detailed performance depends on the task. Nevertheless, Coordinated Views remain a highly effective and preferred technique across the board.

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'.

An ethical visualization of the NorthCOVID-19 model

When modelling epidemics, the outputs and techniques used may be hard for the general public to understand. This can cause fear mongering and confusion on how to interpret the predictions provided by these models. This article proposes a solution for such a model that was created by a Canadian institute for COVID-19 in their region; namely, the NorthCOVID-19 model. In taking these ethical concerns into consideration, first the web interface of this model is analyzed to see how it may be difficult for a user without a strong mathematical background to understand how to use it. Second, a system is developed that takes this model's outputs as an input and produces a video summarization with an auto-generated audio to address the complexity of the interface, while ensuring that the end user is able to understand the important information produced by this model. A survey conducted on this proposed output asked participants, on a scale of 1 to 5, whether they strongly disagreed (1) or strongly agreed (5) with statements regarding the output of the proposed method. The results showed that the audio in the output was helpful in understanding the results (80% responded with 4 or 5) and that it helped improve overallcomprehension of the model (85% responded with 4 or 5). For the analysis of the NorthCOVID-19 interface, a System Usability Scale (SUS) survey was performed where itreceived a scoring of 70.94 which is slightly above the average of 68.

Visual analytics of COVID-19 dissemination in São Paulo state, Brazil

Visual analytics techniques are useful tools to support decision-making and cope with increasing data, particularly to monitor natural or artificial phenomena. When monitoring disease progression, visual analytics approaches help decision-makers to understand or even prevent dissemination paths. In this paper, we propose a new visual analytics tool for monitoring COVID-19 dissemination. We use k-nearest neighbors of cities to mimic neighboring cities and analyze COVID-19 dissemination based on comparing a city under consideration and its neighborhood. Moreover, such analysis is performed within periods, which facilitates the assessment of isolation policies. We validate our tool by analyzing the progression of COVID-19 in neighboring cities of São Paulo state, Brazil.

CLEVis: A Semantic Driven Visual Analytics System for Community Level Events

Community-level event (CLE) datasets, such as police reports of crime events, contain abundant semantic information of event situations, and descriptions in a geospatial-temporal context. They are critical for frontline users, such as police officers and social workers, to discover and examine insights about community neighborhoods. We propose CLEVis, a neighborhood visual analytics system for CLE datasets, to help frontline users explore events for insights at community regions of interest, namely fine-grained geographical resolutions, such as small neighborhoods around local restaurants, churches, and schools. CLEVis fully utilizes semantic information by integrating automatic algorithms and interactive visualizations. The design and development of CLEVis are conducted with solid collaborations with real-world community workers and social scientists. Case studies and user feedback are presented with real-world datasets and applications.

In Search of Patient Zero: Visual Analytics of Pathogen Transmission Pathways in Hospitals

Pathogen outbreaks (i.e., outbreaks of bacteria and viruses) in hospitals can cause high mortality rates and increase costs for hospitals significantly. An outbreak is generally noticed when the number of infected patients rises above an endemic level or the usual prevalence of a pathogen in a defined population. Reconstructing transmission pathways back to the source of an outbreak - the patient zero or index patient - requires the analysis of microbiological data and patient contacts. This is often manually completed by infection control experts. We present a novel visual analytics approach to support the analysis of transmission pathways, patient contacts, the progression of the outbreak, and patient timelines during hospitalization. Infection control experts applied our solution to a real outbreak of Klebsiella pneumoniae in a large German hospital. Using our system, our experts were able to scale the analysis of transmission pathways to longer time intervals (i.e., several years of data instead of days) and across a larger number of wards. Also, the system is able to reduce the analysis time from days to hours. In our final study, feedback from twenty-five experts from seven German hospitals provides evidence that our solution brings significant benefits for analyzing outbreaks.

An artificial intelligence–based decision support and resource management system for COVID-19 pandemic

COVID-19 crisis has shown that the World is not ready for such a rapid spread of a virus resulting in a catastrophic pandemic. Effective use of information technologies is one of the key aspects in reducing the adverse effects of any epidemic or pandemic. Existing management systems have failed to fulfill requirements for curbing the rapid spread of the virus. This chapter firstly describes the current solutions by giving real-world examples. Then, we propose an epidemic management system (EMS) that relies on unimpeded and timely information flow between nations and organizations to ensure resources are distributed effectively. This system will use mobile technology, blockchain, epidemic modeling, and artificial intelligence technologies. We used the Multiplatform Interoperable Scalable Architecture (MPISA) model that allows the integration of multiple platforms and provides a solution for scalability and interoperability problems. Open data repositories and the MiPasa blockchain are also described. These relevant data can be used to predict the potential future spread of the epidemic. Selecting the correct methods for epidemic modeling is discussed as well. Another challenge is deciding on allocating resources where they are most necessary; we propose deploying automated machine learning and stochastic epidemic model-based decision support systems for such purposes. Citizens should not have privacy concerns about the information systems. These trust issues and privacy concerns can be solved by using decentralized identity and zero-knowledge proof-based mechanisms. These mechanisms will ensure that users are in control of their data. In this chapter, we also discuss choosing the right machine learning method, privacy measures, and how the performance challenges can be addressed. This chapter concludes on a discussion of how we can design and deploy better EMSs and possible future studies.

Exploratory study of existing approaches for analyzing epidemics

The outbreak of epidemic diseases such as COVID-19, H1N1 swine flu, Ebola, and dengue has caused different communities to raise their apprehension over preventing and controlling the infectious diseases, as well as determining methods to diminish the disease propagation percentage. Epidemics are generally contiguous in which the number of cases increases at a very rapid rate. It often results in loss of lives as it affects the respiratory tract and lungs and even causes multiorgan failure. Hence, it is imperative to analyze the spread of any virus to make strategies for situational awareness and intervention. Researchers and medical practitioners have actively performed many studies to model the behavior of viruses with varied perspectives. These studies have guided in analyzing the pattern and speed of virus spread. This chapter presents an exploratory study on the existing approaches, such as classical epidemic approaches and Machine Learning approaches, useful for studying the outbreak patterns of epidemics. Besides, the chapter highlights the available epidemics datasets and describes the varied visualization charts that can help in understanding the patterns of virus spread.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review

Background

Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots.

Objective

This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR).

Methods

Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences.

Results

After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard.

Conclusions

With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage.

International Registered Report Identifier (IRRID)

RR2-10.2196/14019

COVID-19: Understanding the Pandemic Emergence, Impact and Infection Prevalence Worldwide

Coronavirus disease (COVID-19) has showed high transmission across the continents due to Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) with total infected cases of around ~ 44 million people. This communicable virus that initiated from the Wuhan city of China in the month of December 2020 has now spread to 189 different countries with 1.1 million fatalities worldwide (till 28 Oct, 2020). The World Health Organization (WHO) declared this outbreak as Public Health Emergency of International Concern in January, 2020. The infection spreads mainly due to contact with infected droplets or fomites, highlighting flu like symptoms initially, which may further progress into severe pneumonia and respiratory failure, often observed in elderly patients with prehistory of other diseases. The diagnosis is based on detection of viral antigen, human antibody and viral gene (RT-PCR). Further, various other diagnostic tools including X-ray, CT-scan are used for imaging purpose, recently artificial intelligence based imaging (contactless scanning) gained popularity. Generally testing of existing drugs (repurposing) and development of new molecules are the main strategies adopted by researchers. However, as per initial findings, various drugs, monoclonal antibody and plasma therapy were found to show effectiveness against COVID-19. Further, many vaccine candidates have entered or will soon enter phase III clinical testing. This disease has further challenged the global economy. Thus, this review uniquely compares the strategies adopted by developed and developing countries worldwide including protective measures like lockdown, continuous testing, utilizing latest tools (artificial intelligence) in curbing this infection spread.

Visual Analytics for Decision-Making During Pandemics

We introduce a trans-disciplinary collaboration between researchers, healthcare practitioners, and community health partners in the Southwestern U.S. to enable improved management, response, and recovery to our current pandemic and for future health emergencies. Our Center work enables effective and efficient decision-making through interactive, human-guided analytical environments. We discuss our PanViz 2.0 system, a visual analytics application for supporting pandemic preparedness through a tightly coupled epidemiological model and interactive interface. We discuss our framework, current work, and plans to extend the system with exploration of what-if scenarios, interactive machine learning for model parameter inference, and analysis of mitigation strategies to facilitate decision-making during public health crises.

A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks

BACKGROUND

Zoonotic diseases account for a substantial portion of infectious disease outbreaks and burden on public health programs to maintain surveillance and preventative measures. Taking advantage of new modeling approaches and data sources have become necessary in an interconnected global community. To facilitate data collection, analysis, and decision-making, the number of spatial decision support systems reported in the last 10 years has increased. This systematic review aims to describe characteristics of spatial decision support systems developed to assist public health officials in the management of zoonotic disease outbreaks.

METHODS

A systematic search of the Google Scholar database was undertaken for published articles written between 2008 and 2018, with no language restriction. A manual search of titles and abstracts using Boolean logic and keyword search terms was undertaken using predefined inclusion and exclusion criteria. Data extraction included items such as spatial database management, visualizations, and report generation.

RESULTS

For this review we screened 34 full text articles. Design and reporting quality were assessed, resulting in a final set of 12 articles which were evaluated on proposed interventions and identifying characteristics were described. Multisource data integration, and user centered design were inconsistently applied, though indicated diverse utilization of modeling techniques.

CONCLUSIONS

The characteristics, data sources, development and modeling techniques implemented in the design of recent SDSS that target zoonotic disease outbreak were described. There are still many challenges to address during the design process to effectively utilize the value of emerging data sources and modeling methods. In the future, development should adhere to comparable standards for functionality and system development such as user input for system requirements, and flexible interfaces to visualize data that exist on different scales. PROSPERO registration number: CRD42018110466.

A Framework for Externalizing Implicit Error Using Visualization

This paper presents a framework for externalizing and analyzing expert knowledge about discrepancies in data through the use of visualization. Grounded in an 18-month design study with global health experts, the framework formalizes the notion of data discrepancies as implicit error, both in global health data and more broadly. We use the term implicit error to describe measurement error that is inherent to and pervasive throughout a dataset, but that isn't explicitly accounted for or defined. Instead, implicit error exists in the minds of experts, is mainly qualitative, and is accounted for subjectively during expert interpretation of the data. Externalizing knowledge surrounding implicit error can assist in synchronizing, validating, and enhancing interpretation, and can inform error analysis and mitigation. The framework consists of a description of implicit error components that are important for downstream analysis, along with a process model for externalizing and analyzing implicit error using visualization. As a second contribution, we provide a rich, reflective, and verifiable description of our research process as an exemplar summary toward the ongoing inquiry into ways of increasing the validity and transferability of design study research.

Hospital influenza pandemic stockpiling needs: A computer simulation

BACKGROUND

A severe influenza pandemic could overwhelm hospitals but planning guidance that accounts for the dynamic interrelationships between planning elements is lacking. We developed a methodology to calculate pandemic supply needs based on operational considerations in hospitals and then tested the methodology at Mayo Clinic in Rochester, MN.

METHODS

We upgraded a previously designed computer modeling tool and input carefully researched resource data from the hospital to run 10,000 Monte Carlo simulations using various combinations of variables to determine resource needs across a spectrum of scenarios.

RESULTS

Of 10,000 iterations, 1,315 fell within the parameters defined by our simulation design and logical constraints. From these valid iterations, we projected supply requirements by percentile for key supplies, pharmaceuticals, and personal protective equipment requirements needed in a severe pandemic.

DISCUSSION

We projected supplies needs for a range of scenarios that use up to 100% of Mayo Clinic-Rochester's surge capacity of beds and ventilators. The results indicate that there are diminishing patient care benefits for stockpiling on the high side of the range, but that having some stockpile of critical resources, even if it is relatively modest, is most important.

CONCLUSIONS

We were able to display the probabilities of needing various supply levels across a spectrum of scenarios. The tool could be used to model many other hospital preparedness issues, but validation in other settings is needed.

Visual analytics of geo-social interaction patterns for epidemic control

BACKGROUND

Human interaction and population mobility determine the spatio-temporal course of the spread of an airborne disease. This research views such spreads as geo-social interaction problems, because population mobility connects different groups of people over geographical locations via which the viruses transmit. Previous research argued that geo-social interaction patterns identified from population movement data can provide great potential in designing effective pandemic mitigation. However, little work has been done to examine the effectiveness of designing control strategies taking into account geo-social interaction patterns.

METHODS

To address this gap, this research proposes a new framework for effective disease control; specifically this framework proposes that disease control strategies should start from identifying geo-social interaction patterns, designing effective control measures accordingly, and evaluating the efficacy of different control measures. This framework is used to structure design of a new visual analytic tool that consists of three components: a reorderable matrix for geo-social mixing patterns, agent-based epidemic models, and combined visualization methods.

RESULTS

With real world human interaction data in a French primary school as a proof of concept, this research compares the efficacy of vaccination strategies between the spatial-social interaction patterns and the whole areas. The simulation results show that locally targeted vaccination has the potential to keep infection to a small number and prevent spread to other regions. At some small probability, the local control strategies will fail; in these cases other control strategies will be needed. This research further explores the impact of varying spatial-social scales on the success of local vaccination strategies. The results show that a proper spatial-social scale can help achieve the best control efficacy with a limited number of vaccines.

CONCLUSIONS

The case study shows how GS-EpiViz does support the design and testing of advanced control scenarios in airborne disease (e.g., influenza). The geo-social patterns identified through exploring human interaction data can help target critical individuals, locations, and clusters of locations for disease control purposes. The varying spatial-social scales can help geographically and socially prioritize limited resources (e.g., vaccines).

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

BACKGROUND

A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) identify public health user needs and preferences for infectious disease information visualization tools; (2) identify existing infectious disease information visualization tools and characterize their architecture and features; (3) identify commonalities among approaches applied to different data types; and (4) describe tool usability evaluation efforts and barriers to the adoption of such tools.

METHODS

We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review.

RESULTS

A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use.

DISCUSSION AND CONCLUSION

As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload.

State of the art in risk analysis of workforce criticality influencing disaster preparedness for interdependent systems

The objective of this article is to discuss a needed paradigm shift in disaster risk analysis to emphasize the role of the workforce in managing the recovery of interdependent infrastructure and economic systems. Much of the work that has been done on disaster risk analysis has focused primarily on preparedness and recovery strategies for disrupted infrastructure systems. The reliability of systems such as transportation, electric power, and telecommunications is crucial in sustaining business processes, supply chains, and regional livelihoods, as well as ensuring the availability of vital services in the aftermath of disasters. There has been a growing momentum in recognizing workforce criticality in the aftermath of disasters; nevertheless, significant gaps still remain in modeling, assessing, and managing workforce disruptions and their associated ripple effects to other interdependent systems. The workforce plays a pivotal role in ensuring that a disrupted region continues to function and subsequently recover from the adverse effects of disasters. With this in mind, this article presents a review of recent studies that have underscored the criticality of workforce sectors in formulating synergistic preparedness and recovery policies for interdependent infrastructure and regional economic systems.

Network epidemiology and plant trade networks

Models of epidemics in complex networks are improving our predictive understanding of infectious disease outbreaks. Nonetheless, applying network theory to plant pathology is still a challenge. This overview summarizes some key developments in network epidemiology that are likely to facilitate its application in the study and management of plant diseases. Recent surveys have provided much-needed datasets on contact patterns and human mobility in social networks, but plant trade networks are still understudied. Human (and plant) mobility levels across the planet are unprecedented-there is thus much potential in the use of network theory by plant health authorities and researchers. Given the directed and hierarchical nature of plant trade networks, there is a need for plant epidemiologists to further develop models based on undirected and homogeneous networks. More realistic plant health scenarios would also be obtained by developing epidemic models in dynamic, rather than static, networks. For plant diseases spread by the horticultural and ornamental trade, there is the challenge of developing spatio-temporal epidemic simulations integrating network data. The use of network theory in plant epidemiology is a promising avenue and could contribute to anticipating and preventing plant health emergencies such as European ash dieback.

Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: The AsiaFluCap Simulator

BACKGROUND

Health care planning for pandemic influenza is a challenging task which requires predictive models by which the impact of different response strategies can be evaluated. However, current preparedness plans and simulations exercises, as well as freely available simulation models previously made for policy makers, do not explicitly address the availability of health care resources or determine the impact of shortages on public health. Nevertheless, the feasibility of health systems to implement response measures or interventions described in plans and trained in exercises depends on the available resource capacity. As part of the AsiaFluCap project, we developed a comprehensive and flexible resource modelling tool to support public health officials in understanding and preparing for surges in resource demand during future pandemics.

RESULTS

The AsiaFluCap Simulator is a combination of a resource model containing 28 health care resources and an epidemiological model. The tool was built in MS Excel© and contains a user-friendly interface which allows users to select mild or severe pandemic scenarios, change resource parameters and run simulations for one or multiple regions. Besides epidemiological estimations, the simulator provides indications on resource gaps or surpluses, and the impact of shortages on public health for each selected region. It allows for a comparative analysis of the effects of resource availability and consequences of different strategies of resource use, which can provide guidance on resource prioritising and/or mobilisation. Simulation results are displayed in various tables and graphs, and can also be easily exported to GIS software to create maps for geographical analysis of the distribution of resources.

CONCLUSIONS

The AsiaFluCap Simulator is freely available software (http://www.cdprg.org) which can be used by policy makers, policy advisors, donors and other stakeholders involved in preparedness for providing evidence based and illustrative information on health care resource capacities during future pandemics. The tool can inform both preparedness plans and simulation exercises and can help increase the general understanding of dynamics in resource capacities during a pandemic. The combination of a mathematical model with multiple resources and the linkage to GIS for creating maps makes the tool unique compared to other available software.