Reducing disease burden in an influenza pandemic by targeted delivery of neuraminidase inhibitors: mathematical models in the Australian context

Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19

BACKGROUND

During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts.

METHODS

In this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number ([Formula: see text]), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios.

RESULTS

We found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number [Formula: see text]. Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters.

CONCLUSION

For the scenarios where the value of information was high enough to justify monitoring, if CS and [Formula: see text] are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information.

Influenza antivirals and their role in pandemic preparedness

Effective antivirals provide crucial benefits during the early phase of an influenza pandemic, when vaccines are still being developed and manufactured. Currently, two classes of viral protein-targeting drugs, neuraminidase inhibitors and polymerase inhibitors, are approved for influenza treatment and post-exposure prophylaxis. Resistance to both classes has been documented, highlighting the need to develop novel antiviral options that may include both viral and host-targeted inhibitors. Such efforts will form the basis of management of seasonal influenza infections and of strategic planning for future influenza pandemics. This review focuses on the two classes of approved antivirals, their drawbacks, and ongoing work to characterize novel agents or combination therapy approaches to address these shortcomings. The importance of these topics in the ongoing process of influenza pandemic planning is also discussed.

Development of an influenza pandemic decision support tool linking situational analytics to national response policy

National influenza pandemic plans have evolved substantially over recent decades, as has the scientific research that underpins the advice contained within them. While the knowledge generated by many research activities has been directly incorporated into the current generation of pandemic plans, scientists and policymakers are yet to capitalise fully on the potential for near real-time analytics to formally contribute to epidemic decision-making. Theoretical studies demonstrate that it is now possible to make robust estimates of pandemic impact in the earliest stages of a pandemic using first few hundred household cohort (FFX) studies and algorithms designed specifically for analysing FFX data. Pandemic plans already recognise the importance of both situational awareness i.e., knowing pandemic impact and its key drivers, and the need for pandemic special studies and related analytic methods for estimating these drivers. An important next step is considering how information from these situational assessment activities can be integrated into the decision-making processes articulated in pandemic planning documents. Here we introduce a decision support tool that directly uses outputs from FFX algorithms to present recommendations on response options, including a quantification of uncertainty, to decision makers. We illustrate this approach using response information from within the Australian influenza pandemic plan.

Constructing an ethical framework for priority allocation of pandemic vaccines

Background

Allocation of scarce resources during a pandemic extends to the allocation of vaccines when they eventually become available. We describe a framework for priority vaccine allocation that employed a cross-disciplinary approach, guided by ethical considerations and informed by local risk assessment.

Methods

Published and grey literature was reviewed, and augmented by consultation with key informants, to collate past experience, existing guidelines and emerging strategies for pandemic vaccine deployment. Identified ethical issues and decision-making processes were also included. Concurrently, simulation modelling studies estimated the likely impacts of alternative vaccine allocation approaches. Assembled evidence was presented to a workshop of national experts in pandemic preparedness, vaccine strategy, implementation and ethics. All of this evidence was then used to generate a proposed ethical framework for vaccine priorities best suited to the Australian context.

Findings

Published and emerging guidance for priority pandemic vaccine distribution differed widely with respect to strategic objectives, specification of target groups, and explicit discussion of ethical considerations and decision-making processes. Flexibility in response was universally emphasised, informed by real-time assessment of the pandemic impact level, and identification of disproportionately affected groups. Model outputs aided identification of vaccine approaches most likely to achieve overarching goals in pandemics of varying transmissibility and severity. Pandemic response aims deemed most relevant for an Australian framework were: creating and maintaining trust, promoting equity, and reducing harmful outcomes.

Interpretation

Defining clear and ethically-defendable objectives for pandemic response in context aids development of flexible and adaptive decision support frameworks and facilitates clear communication and engagement activities.

Coronavirus Disease Model to Inform Transmission-Reducing Measures and Health System Preparedness, Australia

The ability of health systems to cope with coronavirus disease (COVID-19) cases is of major concern. In preparation, we used clinical pathway models to estimate healthcare requirements for COVID-19 patients in the context of broader public health measures in Australia. An age- and risk-stratified transmission model of COVID-19 demonstrated that an unmitigated epidemic would dramatically exceed the capacity of the health system of Australia over a prolonged period. Case isolation and contact quarantine alone are insufficient to constrain healthcare needs within feasible levels of expansion of health sector capacity. Overlaid social restrictions must be applied over the course of the epidemic to ensure systems do not become overwhelmed and essential health sector functions, including care of COVID-19 patients, can be maintained. Attention to the full pathway of clinical care is needed, along with ongoing strengthening of capacity.

Interfering with influenza: nonlinear coupling of reactive and static mitigation strategies

When new, highly infectious strains of influenza emerge, global pandemics can occur before an effective vaccine is developed. Without a strain-specific vaccine, pandemics can only be mitigated by employing combinations of low-efficacy pre-pandemic vaccines and reactive response measures that are carried out as the pandemic unfolds. Unfortunately, the application of reactive interventions can lead to unintended consequences that may exacerbate unpredictable spreading dynamics and cause more drawn-out epidemics. Here, we employ a detailed model of pandemic influenza in Australia to simulate the combination of pre-pandemic vaccination and reactive antiviral prophylaxis. This study focuses on population-level coupling effects between the respective methods, and the associated spatio-temporal fluctuations in pandemic dynamics produced by reactive strategies. Our results show that combining strategies can produce either mutual improvement of performance or interference that reduces the effectiveness of each strategy when they are used together. We demonstrate that these coupling effects between intervention strategies are extremely sensitive to delay times, compliance rates and the type of contact targeting used to administer prophylaxis.

Coordinating the real-time use of global influenza activity data for better public health planning

Health planners from global to local levels must anticipate year-to-year and week-to-week variation in seasonal influenza activity when planning for and responding to epidemics to mitigate their impact. To help with this, countries routinely collect incidence of mild and severe respiratory illness and virologic data on circulating subtypes and use these data for situational awareness, burden of disease estimates and severity assessments. Advanced analytics and modelling are increasingly used to aid planning and response activities by describing key features of influenza activity for a given location and generating forecasts that can be translated to useful actions such as enhanced risk communications, and informing clinical supply chains. Here, we describe the formation of the Influenza Incidence Analytics Group (IIAG), a coordinated global effort to apply advanced analytics and modelling to public influenza data, both epidemiological and virologic, in real-time and thus provide additional insights to countries who provide routine surveillance data to WHO. Our objectives are to systematically increase the value of data to health planners by applying advanced analytics and forecasting and for results to be immediately reproducible and deployable using an open repository of data and code. We expect the resources we develop and the associated community to provide an attractive option for the open analysis of key epidemiological data during seasonal epidemics and the early stages of an influenza pandemic.

Infectious disease pandemic planning and response: Incorporating decision analysis

Freya Shearer and co-authors discuss the use of decision analysis in planning for infectious disease pandemics.

Antiviral Therapy for the Next Influenza Pandemic

Influenza antivirals will play a critical role in the treatment of outpatients and hospitalised patients in the next pandemic. In the past decade, a number of new influenza antivirals have been licensed for seasonal influenza, which can now be considered for inclusion into antiviral stockpiles held by the World Health Organization (WHO) and individual countries. However, data gaps remain regarding the effectiveness of new and existing antivirals in severely ill patients, and regarding which monotherapy or combinations of antivirals may yield the greatest improvement in outcomes. Regardless of the drug being used, influenza antivirals are most effective when treatment is initiated early in the course of infection, and therefore in a pandemic, effective strategies which enable rapid diagnosis and prompt delivery will yield the greatest benefits.

Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts

For diseases such as influenza, where the majority of infected persons experience mild (if any) symptoms, surveillance systems are sensitive to changes in healthcare-seeking and clinical decision-making behaviours. This presents a challenge when trying to interpret surveillance data in near-real-time (e.g., to provide public health decision-support). Australia experienced a particularly large and severe influenza season in 2017, perhaps in part due to: (a) mild cases being more likely to seek healthcare; and (b) clinicians being more likely to collect specimens for reverse transcription polymerase chain reaction (RT-PCR) influenza tests. In this study, we used weekly Flutracking surveillance data to estimate the probability that a person with influenza-like illness (ILI) would seek healthcare and have a specimen collected. We then used this estimated probability to calibrate near-real-time seasonal influenza forecasts at each week of the 2017 season, to see whether predictive skill could be improved. While the number of self-reported influenza tests in the weekly surveys are typically very low, we were able to detect a substantial change in healthcare seeking behaviour and clinician testing behaviour prior to the high epidemic peak. Adjusting for these changes in behaviour in the forecasting framework improved predictive skill. Our analysis demonstrates a unique value of community-level surveillance systems, such as Flutracking, when interpreting traditional surveillance data. These methods are also applicable beyond the Australian context, as similar community-level surveillance systems operate in other countries.

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.