Translation of Real-Time Infectious Disease Modeling into Routine Public Health Practice

An inaugural forum on epidemiological modeling for public health stakeholders in Arizona

Epidemiological models-which help us understand and forecast the spread of infectious disease-can be valuable tools for public health. However, barriers exist that can make it difficult to employ epidemiological models routinely within the repertoire of public health planning. These barriers include technical challenges associated with constructing the models, challenges in obtaining appropriate data for model parameterization, and problems with clear communication of modeling outputs and uncertainty. To learn about the unique barriers and opportunities within the state of Arizona, we gathered a diverse set of 48 public health stakeholders for a day-and-a-half forum. Our research group was motivated specifically by our work building software for public health-relevant modeling and by our earnest desire to collaborate closely with stakeholders to ensure that our software tools are practical and useful in the face of evolving public health needs. Here we outline the planning and structure of the forum, and we highlight as a case study some of the lessons learned from breakout discussions. While unique barriers exist for implementing modeling for public health, there is also keen interest in doing so across diverse sectors of State and Local government, although issues of equal and fair access to modeling knowledge and technologies remain key issues for future development. We found this forum to be useful for building relationships and informing our software development, and we plan to continue such meetings annually to create a continual feedback loop between academic molders and public health practitioners.

Early detection of emerging infectious diseases - implications for vaccine development

Vast quantities of open-source data from news reports, social media and other sources can be harnessed using artificial intelligence and machine learning, and utilised to generate valid early warning signals of emerging epidemics. Early warning signals from open-source data are not a replacement for traditional, validated disease surveillance, but provide a trigger for earlier investigation and diagnostics. This may yield earlier pathogen characterisation and genomic data, which can enable earlier vaccine development or deployment of vaccines. Early warning also provides a more feasible prospect of stamping out epidemics before they spread. There are several of such systems currently, but they are not used widely in public health practice, and only some are publicly available. Routine and widespread use of open-source intelligence, as well as training and capacity building in digital surveillance, will improve pandemic preparedness and early response capability.

Modelling outbreak response impact in human vaccine-preventable diseases: A systematic review of differences in practices between collaboration types before COVID-19

BACKGROUND

Outbreak response modelling often involves collaboration among academics, and experts from governmental and non-governmental organizations. We conducted a systematic review of modelling studies on human vaccine-preventable disease (VPD) outbreaks to identify patterns in modelling practices between two collaboration types. We complemented this with a mini comparison of foot-and-mouth disease (FMD), a veterinary disease that is controllable by vaccination.

METHODS

We searched three databases for modelling studies that assessed the impact of an outbreak response. We extracted data on author affiliation type (academic institution, governmental, and non-governmental organizations), location studied, and whether at least one author was affiliated to the studied location. We also extracted the outcomes and interventions studied, and model characteristics. Included studies were grouped into two collaboration types: purely academic (papers with only academic affiliations), and mixed (all other combinations) to help investigate differences in modelling patterns between collaboration types in the human disease literature and overall differences with FMD collaboration practices.

RESULTS

Human VPDs formed 227 of 252 included studies. Purely academic collaborations dominated the human disease studies (56%). Notably, mixed collaborations increased in the last seven years (2013-2019). Most studies had an author affiliated to an institution in the country studied (75.2%) but this was more likely among the mixed collaborations. Contrasted to the human VPDs, mixed collaborations dominated the FMD literature (56%). Furthermore, FMD studies more often had an author with an affiliation to the country studied (92%) and used complex model design, including stochasticity, and model parametrization and validation.

CONCLUSION

The increase in mixed collaboration studies over the past seven years could suggest an increase in the uptake of modelling for outbreak response decision-making. We encourage more mixed collaborations between academic and non-academic institutions and the involvement of locally affiliated authors to help ensure that the studies suit local contexts.

Preventing the next pandemic: Use of artificial intelligence for epidemic monitoring and alerts

Emerging infections are a continual threat to public health security, which can be improved by use of rapid epidemic intelligence and open-source data. Artificial intelligence systems to enable earlier detection and rapid response by governments and health can feasibly mitigate health and economic impacts of serious epidemics and pandemics. EPIWATCH is an artificial intelligence-driven outbreak early-detection and monitoring system, proven to provide early signals of epidemics before official detection by health authorities.

Infectious Disease Modeling: Recommendations for Public Health Decision-Makers

Infectious disease modeling plays an important role in the response to infectious disease outbreaks, perhaps most notably during the coronavirus disease 2019 (COVID-19) pandemic. In our experience working with state and local governments during COVID-19 and previous public health crises, we have observed that, while the scientific literature focuses on models' accuracy and underlying assumptions, an important limitation on the effective application of modeling to public health decision-making is the ability of decision-makers and modelers to work together productively. We therefore propose a set of guiding principles, informed by our experience, for working relationships between decision-makers and modelers. We hypothesize that these guidelines will improve the utility of infectious disease modeling for public health decision-making, irrespective of the particular outbreak in question and of the precise modeling approaches being used.

Evaluating online and tangible interfaces for engaging stakeholders in forecasting and control of biological invasions

Ecological forecasts will be best suited to inform intervention strategies if they are accessible to a diversity of decision-makers. Researchers are developing intuitive forecasting interfaces to guide stakeholders through the development of intervention strategies and visualization of results. Yet, few studies to date have evaluated how user interface design facilitates the coordinated, cross-boundary management required for controlling biological invasions. We used a participatory approach to develop complementary tangible and online interfaces for collaboratively forecasting biological invasions and devising control strategies. A diverse group of stakeholders evaluated both systems in the real-world context of controlling sudden oak death, an emerging forest disease killing millions of trees in California and Oregon. Our findings suggest that while both interfaces encouraged adaptive experimentation, tangible interfaces are particularly well suited to support collaborative decision-making. Reflecting on the strengths of both systems, we suggest workbench-style interfaces that support simultaneous interactions and dynamic geospatial visualizations.

Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19

BACKGROUND

Mathematical modeling constitutes an important tool for planning robust responses to epidemics. This study was conducted to guide the Qatari national response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. The study investigated the epidemic's time-course, forecasted health care needs, predicted the impact of social and physical distancing restrictions, and rationalized and justified easing of restrictions.

METHODS

An age-structured deterministic model was constructed to describe SARS-CoV-2 transmission dynamics and disease progression throughout the population.

RESULTS

The enforced social and physical distancing interventions flattened the epidemic curve, reducing the peaks for incidence, prevalence, acute-care hospitalization, and intensive care unit (ICU) hospitalizations by 87%, 86%, 76%, and 78%, respectively. The daily number of new infections was predicted to peak at 12 750 on May 23, and active-infection prevalence was predicted to peak at 3.2% on May 25. Daily acute-care and ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number R0 had varied between 1.07-2.78, and 50.8% of the population were estimated to have been infected (1.43 million infections). The proportion of actual infections diagnosed was estimated at 11.6%. Applying the concept of Rt tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when Rt declined to 0.7, to buffer the increased interpersonal contact with easing of restrictions and to minimize the risk of a second wave. No second wave has materialized as of October 15, 2020, five months after the epidemic peak.

CONCLUSIONS

Use of modeling and forecasting to guide the national response proved to be a successful strategy, reducing the toll of the epidemic to a manageable level for the health care system.

A decision support framework for prediction of avian influenza

For years, avian influenza has influenced economies and human health around the world. The emergence and spread of avian influenza virus have been uncertain and sudden. The virus is likely to spread through several pathways such as poultry transportation and wild bird migration. The complicated and global spread of avian influenza calls for surveillance tools for timely and reliable prediction of disease events. These tools can increase situational awareness and lead to faster reaction to events. Here, we aimed to design and evaluate a decision support framework that aids decision makers by answering their questions regarding the future risk of events at various geographical scales. Risk patterns were driven from pre-built components and combined in a knowledge base. Subsequently, questions were answered by direct queries on the knowledge base or through a built-in algorithm. The evaluation of the system in detecting events resulted in average sensitivity and specificity of 69.70% and 85.50%, respectively. The presented framework here can support health care authorities by providing them with an opportunity for early control of emergency situations.

A Dominance-Based Rough Set Approach for an Enhanced Assessment of Seasonal Influenza Risk

Accounting for about 290,000-650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision-making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S-OLAP) technology. Although the S-OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well-known multicriteria classification method, the dominance-based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S-OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria.

Forecasting and control of emerging infectious forest disease through participatory modelling

Epidemiological models are powerful tools for evaluating scenarios and visualizing patterns of disease spread, especially when comparing intervention strategies. However, the technical skill required to synthesize and operate computational models frequently renders them beyond the command of the stakeholders who are most impacted by the results. Participatory modelling (PM) strives to restructure the power relationship between modellers and the stakeholders who rely on model insights by involving these stakeholders directly in model development and application; yet, a systematic literature review indicates little adoption of these techniques in epidemiology, especially plant epidemiology. We investigate the potential for PM to integrate stakeholder and researcher knowledge, using Phytophthora ramorum and the resulting sudden oak death disease as a case study. Recent introduction of a novel strain (European 1 or EU1) in southwestern Oregon has prompted significant concern and presents an opportunity for coordinated management to minimize regional pathogen impacts. Using a PM framework, we worked with local stakeholders to develop an interactive forecasting tool for evaluating landscape-scale control strategies. We find that model co-development has great potential to empower stakeholders in the design, development and application of epidemiological models for disease control.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Genomic Epidemiology as a Public Health Tool to Combat Mosquito-Borne Virus Outbreaks

Next-generation sequencing technologies, exponential increases in the availability of virus genomic data, and ongoing advances in phylogenomic methods have made genomic epidemiology an increasingly powerful tool for public health response to a range of mosquito-borne virus outbreaks. In this review, we offer a brief primer on the scope and methods of phylogenomic analyses that can answer key epidemiological questions during mosquito-borne virus public health emergencies. We then focus on case examples of outbreaks, including those caused by dengue, Zika, yellow fever, West Nile, and chikungunya viruses, to demonstrate the utility of genomic epidemiology to support the prevention and control of mosquito-borne virus threats. We extend these case studies with operational perspectives on how to best incorporate genomic epidemiology into structured surveillance and response programs for mosquito-borne virus control. Many tools for genomic epidemiology already exist, but so do technical and nontechnical challenges to advancing their use. Frameworks to support the rapid sharing of multidimensional data and increased cross-sector partnerships, networks, and collaborations can support advancement on all scales, from research and development to implementation by public health agencies.

Flucast: A Real-Time Tool to Predict Severity of an Influenza Season

Background

Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicate a preference for forecasting tools that are easy to use and understand to assist with surge capacity planning for influenza.

Objective

This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season.

Methods

Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports from the Department of Health, Australia. We tested Flucast using retrospective seasonal data for 11 Australian influenza seasons. We compared five different models using parameters known early in the season that may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. The model with the highest predictive accuracy against retrospective seasonal activity was chosen as a best-fit model to develop the Flucast tool. The tool was prospectively tested against the 2018 and the emerging 2019 influenza season.

Results

The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. With the use of real-time data, the tool provided a reasonable early prediction of a low to moderate season for the 2018 and severe seasonal activity for the upcoming 2019 season. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning.

Conclusions

The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity, and intervention programs in real time, and can be adapted for different settings and geographic locations.

Using "outbreak science" to strengthen the use of models during epidemics

Infectious disease modeling has played a prominent role in recent outbreaks, yet integrating these analyses into public health decision-making has been challenging. We recommend establishing ‘outbreak science’ as an inter-disciplinary field to improve applied epidemic modeling.

Anatomy of a seasonal influenza epidemic forecast

Bayesian methods have been used to predict the timing of infectious disease epidemics in various settings and for many infectious diseases, including seasonal influenza. But integrating these techniques into public health practice remains an ongoing challenge, and requires close collaboration between modellers, epidemiologists, and public health staff. During the 2016 and 2017 Australian influenza seasons, weekly seasonal influenza forecasts were produced for cities in the three states with the largest populations: Victoria, New South Wales, and Queensland. Forecast results were presented to Health Department disease surveillance units in these jurisdictions, who provided feedback about the plausibility and public health utility of these predictions. In earlier studies we found that delays in reporting and processing of surveillance data substantially limited forecast performance, and that incorporating climatic effects on transmission improved forecast performance. In this study of the 2016 and 2017 seasons, we sought to refine the forecasting method to account for delays in receiving the data, and used meteorological data from past years to modulate the force of infection. We demonstrate how these refinements improved the forecast’s predictive capacity, and use the 2017 influenza season to highlight challenges in accounting for population and clinician behaviour changes in response to a severe season.

Epidemic forecasts as a tool for public health: interpretation and (re)calibration

OBJECTIVE

Recent studies have used Bayesian methods to predict timing of influenza epidemics many weeks in advance, but there is no documented evaluation of how such forecasts might support the day-to-day operations of public health staff.

METHODS

During the 2015 influenza season in Melbourne, Australia, weekly forecasts were presented at Health Department surveillance unit meetings, where they were evaluated and updated in light of expert opinion to improve their accuracy and usefulness.

RESULTS

Predictive capacity of the model was substantially limited by delays in reporting and processing arising from an unprecedented number of notifications, disproportionate to seasonal intensity. Adjustment of the predictive algorithm to account for these delays and increased reporting propensity improved both current situational awareness and forecasting accuracy.

CONCLUSIONS

Collaborative engagement with public health practitioners in model development improved understanding of the context and limitations of emerging surveillance data. Incorporation of these insights in a quantitative model resulted in more robust estimates of disease activity for public health use. Implications for public health: In addition to predicting future disease trends, forecasting methods can quantify the impact of delays in data availability and variable reporting practice on the accuracy of current epidemic assessment. Such evidence supports investment in systems capacity.

Converging and emerging threats to health security

Advances in biological sciences have outpaced regulatory and legal frameworks for biosecurity. Simultaneously, there has been a convergence of scientific disciplines such as synthetic biology, data science, advanced computing and many other technologies, which all have applications in health. For example, advances in cybercrime methods have created ransomware attacks on hospitals, which can cripple health systems and threaten human life. New kinds of biological weapons which fall outside of traditional Cold War era thinking can be created synthetically using genetic code. These convergent trajectories are dramatically expanding the repertoire of methods which can be used for benefit or harm. We describe a new risk landscape for which there are few precedents, and where regulation and mitigation are a challenge. Rapidly evolving patterns of technology convergence and proliferation of dual-use risks expose inadequate societal preparedness. We outline examples in the areas of biological weapons, antimicrobial resistance, laboratory security and cybersecurity in health care. New challenges in health security such as precision harm in medicine can no longer be addressed within the isolated vertical silo of health, but require cross-disciplinary solutions from other fields. Nor can they cannot be managed effectively by individual countries. We outline the case for new cross-disciplinary approaches in risk analysis to an altered risk landscape.