Introduction

With growing pressure on ecosystems, an increasing risk of spillovers of pathogens from animals to humans, and multiple global changes that may influence the dynamics of infectious diseases, there is a constant risk of a new infectious disease outbreak or pandemic [1]. Early recognition coupled with prompt response to an emerging pathogen is imperative as this can prevent or mitigate a (larger) public health crisis. Public health measures recommended during the COVID-19 pandemic were associated with potential detrimental societal and economic impact, and the need to mitigate these effects was increasingly recognized. [2–9]. While the need for multidisciplinary advice as part of pandemic preparedness had been recognized prior to the COVID-19 pandemic, few countries had broad expertise from scientific domains beyond biomedical sciences in their advisory bodies [10,11].

In The Netherlands, since 1995, the Dutch Outbreak Management Team (OMT) -consisting of a core of experts in infectious disease preparedness, treatment and control- has advised the Minister of Health in the event of an outbreak of concern, including before and during the COVID-19 pandemic [12]. An OMT zoonoses including an extended zoonoses structure is in place in case of zoonotic threats [13]. While many additional experts from other disciplines have been recruited for the OMT depending on the outbreak, such as social science experts, the focus of the OMT has been primarily biomedical. Logically, the OMT was not sufficiently equipped to provide the necessary recommendations to limit the societal and economic impact of the long-lasting COVID-19 outbreak.

Therefore, next to the valuable biomedical advice, social, behavioral and economic advice was provided by multiple existing official government advisory bodies such as the National Institute for Social Research (SCP), Netherlands Bureau for Economic Policy Analysis (CPB), and the behavioral unit of the National Institute for Public Health and the Environment (RIVM) during the COVID-19 pandemic. When the pandemic evolved, an early attempt was made to harmonize societal, behavioral and economic advice in a newly developed Societal Impact Team (in Dutch ‘Maatschappelijk Impact Team’ or MIT). This team consisting of societal and economic experts, provided advice to the Minister of Social Affairs and Employment from 1 September 2022 on wards [14]. The OMT and the MIT separately advised the ministers during the later phase of the COVID-19 pandemic. The two ministries were tasked with a balanced decision and implementation of the separate advised public health measures (see Fig 1, left). However, without exchange between advisory bodies, there is a risk that recommended measures will be presented in a one-sided manner or may even be conflicting. This could lead to unbalanced advice, resulting in a sub optimal outbreak management policy.

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Fig 1. Left: Current prevailed form of advising bodies during an infectious disease outbreak of (inter)national concern in the Netherlands; right: proposition of an alternative advising approach which needs to be further investigated [15].

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In the post-COVID-19 period, multiple evaluation reports and papers have been published with lessons learned from the COVID-19 pandemic about the pathogen, interventions, measures, accessibility, infrastructures, data collection et cetera [16–22]. These lessons may provide valuable insights to optimize outbreak management strategies and prepare for new pandemic threats. To also learn from the used advisory structures, as a pilot study, we recently conducted a multidisciplinary table-top exercise to evaluate the expert advice provided during two specific phases of the COVID-19 pandemic [6]. This study concluded that, compared to the current prevailed situation (Fig 1 left panel), an integrated advice can provide a multi-perspective substantiation of an advice request and might potentially provide better quality of support to policy makers (see Fig 1 right panel).

Here, our aim was to further explore the organization of such integrated advice in the context of a current threat with pandemic potential, using an outbreak simulation exercise as a basis. H5N1 Highly pathogenic avian influenza (HPAI) has been detected in several mammalian species, including goats and dairy cattle in the USA in 2024, and recently in kittens in the Netherlands [23,24]. Suspected and confirmed cases of HPAI in wild and domestic birds are monitored very closely in the Netherlands [25]. In the event of a confirmed case among domestic poultry in the Netherlands, with its densely populated intensive poultry farming industry, strict measures are taken [25]. Despite the limited number of human cases worldwide, the WHO has placed HPAI on its list of priority pathogens and considers the risk of a public health emergency of international concern caused by HPAI to be high [26,27].

We conducted two table-top simulation exercises of a fictitious but realistic HPAI outbreak originating in the Netherlands. The fictitious outbreak occurred in two pandemic phases, corresponding to WHO pandemic influenza phases 4 and 6, respectively-(supplement S1 Fig), requiring different levels of response intensity. To develop an interdisciplinary HPAI knowledge agenda, we sought to understand key knowledge and process concepts, data and analyses that would need to be considered for policy advice. We addressed the following exploratory research questions:

1. What key knowledge and know-how (the knowledge of how to implement expertise or methods) are provided through the separate biomedical and socio-economic expert groups?
2. Are there topics where biomedical and socio-economic expert advice may lead to conflicting recommendations?
3. Are there characteristics of separate biomedical and socio-economic expert advice that may lead to discord in the advice process?
4. Can we identify key themes that contribute to the alignment of conflicting recommendations or process?
5. What are domain-overarching knowledge and know-how gaps that contribute to integrated HPAI pandemic preparedness?

Methods

We conducted two tabletop simulation exercises (i.e., simulated emergency situations designed to facilitate discussion among participants) regarding an HPAI sub-type H5N1 outbreak originating in wild birds and with a spill-over to pigs and humans in the Netherlands. Dutch experts from various disciplines were recruited through purposive sampling from the existing network of the research group, and complemented by targeted invitations. Criteria to invite an expert to participate were: 1) the invited experts represented disciplines which can be requested into a scientific advisory committee in a case of an avian influenza outbreak such as virology, veterinary sciences, epidemiology, public health, economics, sociology, communication, and behavioral sciences, 2) the expert has demonstrable experience in his/her field of expertise (e.g., principal investigator or professor) and/or 3) has experience as a member of an Outbreak Management Team or Societal team advising the Dutch government during COVID-19 or previous large outbreaks. To prevent over-representation during the exercises, only one or two experts from a similar discipline were invited. To prevent disbalances or under-representation of disciplines, new invites were send to other experts within a similar discipline upon a decline or non-response of an eligible participant. Optimal group size for deliberation was considered at eight to ten participants per group.

Design tabletop simulation exercises

The simulation exercises were held in the Netherlands on 17 April and 24 May 2024, and each consisted of a full-day program. The exercises consisted of introductory presentations, a morning session in which participants were grouped according to their disciplines (mimicking the current prevailing governance framework on scientific advice during outbreaks in two separate bodies), and an afternoon session in which participants were mixed in two or three groups respectively. A modified WHO-Integrate COVID-19 (WICID) Evidence-to-Decision framework was used for integrated advice discussions [28]. Briefly, the framework guides systemic reflection on interventions within their specific contexts using eleven different criteria such as health benefits and harms: equity, equality and feasibility and implementation. No specific criteria were set for the integrated advice, providing each mixed group the liberty to explore a preferred joint approach – whether consensus, merging recommendations or substantiating (separate) recommendations, or a combination thereof – for the additional integrated advice. Each group session was followed by a plenary reflection and each simulation exercise was completed with a reflection and evaluation session.

The (background) presentations provided the participating experts with biomedical, social, behavioral and economic information about the simulation scenario.

In the morning exercise, the experts from the virology, veterinary sciences, epidemiology and public health were united in the biomedical team. The experts of the economics, sociology, communication and behavioral sciences were united in a societal team. The biomedical and societal team were invited to respond separately to a request for advice from the Ministry of Health. This request had been formulated ahead of the simulation exercise by representatives of the Ministry of Health based on the simulation scenario. The request included questions and sub-questions regarding the policy goal ‘guard healthcare access for the entire population’ and ‘maintain social and economic continuity and vitality.’

In the afternoon session, participants regrouped into mixed teams of biomedical and socio-economic scientists and the draft advice from the morning session were provided to all participants in printed form. Participants were then asked to discuss these, and to seek over-arching alignment by performing integrated assessments (see also Fig 2 for the design of the table-top simulation exercises). Subsequently, they were asked to jointly review (a selection of) the adjusted advice using the modified WICID assessment framework (see further Waltz et al [29]). Finally, they were asked to draft an additional integrated scientific advice.

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Fig 2. Design of the table-top simulation exercises of the fictitious HPAI outbreak.

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Scenarios

The scenario was written by a selection of research authors. Thirty-three experts from various disciplines were interviewed in the preparation for the fictitious but realistic HPAI outbreak scenario. This allowed us to present an outbreak scenario from the perspective of a society as a whole. Validation took place by having the scenario assessed for potential feasibility by two influenza experts.

Simulation exercise April 2024.

The following scenario description was prepared: HPAI is found in a small cluster of three pig farms in the northern part of the Netherlands, but not in other animals. Also, two mild human cases are detected, but no secondary human cases. For the following four months, no new cases are detected as in the scenario and swift public health measures are implemented in accordance with Dutch guidelines (e.g., the culling of pigs from contaminated pig farms, a standstill of pigs and related activities within a specified area, source and contract tracing among exposed humans). The fictitious outbreak pushed the Dutch situation from phase 2 to phase 3 in the WHO pandemic influenza classification (see Supplement S1 Fig).

Then, a new variant of H5N1 -nH5N1- is detected in two hospitalized children from the south of the Netherlands with severe respiratory infections, one of whom lives at a pig farm. Source and contact tracing reveals infections with the new variant in six human contacts −3 family members and 3 classmates- and in pigs from 6 out of 20 surrounding pig farms (see also Fig 2). The outbreak elevates the situation to phase 4 of the WHO pandemic influenza classification (Supplement S1 Fig).

Simulation exercise May 2024.

The fictitious scenario of the second simulation focused only on human infections and picked up one week later where the first scenario had left off. By this time, a total number of 37 human nH5N1 infections −8 family members, 6 classmates, 18 other students, 5 healthcare workers from a pilot screening- had been confirmed. Thirty-five of the cases had no known exposure to pigs. Despite expanded public health measures are taken in the scenario, the total number of confirmed nH5N1 cases increased to 134 only seven days later, including multiple hospitalizations of children and two deaths. Wastewater surveillance detects the virus in major cities and several international cases linked to events in the Netherlands are reported. The outbreak reaches phase 5 of the WHO pandemic influenza classification (Fig 3, Supplement S1 Table).

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Fig 3. Timeline of a fictitious HPAI scenario of two simulation exercises, April and May 2024, the Netherlands.

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Results

In total, 22 of the 81 invited experts participated in the first simulation exercise (of which 11 participants were assigned to either the biomedical or societal teams, respectively) and 19 experts participated in the second simulation exercise (of which 9 and 10 participants were assigned to the biomedical and societal teams, respectively) (Fig 4). Eleven experts participated in both simulation exercises. The biomedical team consisted of experts, including avian influenza specialists, representing the fields of epidemiology, virology, medical microbiology, infectious disease medicine, public health, infectious disease modelling, family medicine, animal epidemiology, animal modelling of infectious disease transmission, veterinary inspections and poultry health care. The societal team consisted of experts representing the fields of (macro) economics, animal health economics, sociology, health communication, health education, health promotion, public health communication, public health risk communication, risk management, crisis, safety and health, and (government) policy.

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Fig 4. Flow diagram for study participants.

* Seventeen of the 33 experts that were interviewed also participated in the simulation exercises. # Eleven (5 biomedical and 7 societal) experts participated in both simulation exercises.

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Key knowledge and know-how of separate advice

In the morning sessions, the biomedical and societal teams deliberated separately. The initial recommendations from the separate advice of the April 2024 and May 24 simulation exercises are listed in Tables 1 and 2. The recommendations of the biomedical team were most consistent with recommendations arising from the perspective of outbreak management, including a containment strategy, and limiting the impact of the outbreak itself. The recommendations of societal team were most consistent with recommendations arising from an appropriate communication and information need and limiting the impact of the outbreak and measures.

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Table 1. The advice provided by the separate biomedical and societal teams during the simulation exercise in April 2024.

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Table 2. The advice provided by the separate biomedical and societal teams during the simulation exercise in May 2024.

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Characteristics of separate advice leading to discord in the process of advising

When reviewing the advice from each expert group in a plenary forum, several differences were noted. First, the starting point for the medical, veterinary and economic experts was captured in existing guidelines or funding programs, but the societal experts could not rely on existing guidelines. As a result, the pace of execution of the intended measures differed considerably between the fields. Second, without a biomedical interpretation, the (potential) severity of the situation was not clear to the societal experts. This forced them to compose a best and worst-case scenario themselves, which led to different best- and worst-case scenarios between the expert groups during the first simulation exercise. The assessment of severity and the required pace of action also differed between the two groups. Third, blind spots were observed. By prioritizing the goals most important to their own expert team -such as virus suppression for the biomedical team and limiting social side effects for the societal team-, public health measures proposed by one expert team seemed to overlook the potential impact or consequences in the other expert team’s field. Finally, in the deliberations, experts referred to negative experiences from the COVID-19 pandemic (including public health measures taken during the pandemic) and included these in their decision making. We have summarized the observed discord factors in Fig 5.

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Fig 5. Discord factors observed in separate biomedical and societal advice during the morning sessions of the fictitious simulation exercises in April and May 2024.

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Key themes contributing towards alignment in advice and process

After the plenary review of the morning sessions, the experts were mixed in two or three groups respectively. The main discussion points, of the simulation exercise in April and May 2024 are listed in Table 3.

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Table 3. The topics of the integrated deliberations during the afternoon sessions of the fictitious simulation exercises in April and 2024.

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We observed that the identified blind spots in these participating groups, the inconsistencies in the approach and characteristics of the proposed public health measures in the separate advising were explored during the integrated deliberations. In all mixed groups, the deliberations included the difference in the assessment of the situation. Recognizing that this was part of the biomedical group’s area of expertise, members of the societal team sought clarification from the biomedical team as to why they were recommending rapid action despite a limited number of cases with relatively mild disease. Members of the biomedical team were keen to provide additional explanations of the potential consequences of a worst-case scenario, and why it was important to act swiftly to preserve the window of opportunity to take effective countermeasures. The integrated deliberations facilitated the identification of underlying disciplinary assumptions and that transparency helped clarify why certain advice was being given.

School closure was one example that was discussed at length. Members of the biomedical team considered the outbreak of a new variant involving children with very little herd immunity to be a very severe situation and preferred to act swiftly and rigorously to prevent the outbreak to evolve in a larger outbreak, while members of the societal team seemed not impressed by the severity due to the limited cases identified and considered school closure to be too impactful, given lessons learned from the COVID-19 pandemic.

“Another point that struck us in particular was the closure of schools as a measure. This is a very extreme measure in the socio-economic world. It is a costly measure in social and economic terms if it is to continue for a long time. The advice now gives the impression that this has been taken lightly.”

Discussing this difference in sense of urgency and the proportionality of school closure allowed the identification of blind spots on both sides. For example, an explanation of the potential severity of the outbreak itself clarified why the biomedical team recommended such stringent and urgent measures.

Member of the societal team in a mixed deliberation: “If we had known earlier how serious it was from the biomedical team’s point of view”

On the other hand, the socioeconomic perspective prompted the biomedical team to reconsider proposed measures after explaining their potential socioeconomic impact.

Member of the societal team in a mixed deliberation: “You are recommending measures that could cause significant mental and social damage. It is important to carefully consider whether to include them in the package.”

The mixed deliberation led to a better understanding of the underlying rationale from the different fields of expertise. The participants were willing to reconsider the proposed measures or to jointly discuss additional measures to minimize the consequences of the outbreak or the measures.

Figure 6 summarizes key factors enabling the integrated decision making. However, the speed and success of these deliberations differed between the mixed groups of experts. Particularly in the April session, one group developed a mutual understanding quickly and smoothly, while another group struggled to find common ground.

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Fig 6. Key themes during the integrated deliberations as potential enablers to overcome potential for conflicting expert advice.

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Remarkable was that the variety of discussion points was less extensive during the second simulation exercise compared to the first exercise.

HPAI knowledge and know-how gaps

As progressive knowledge and know-how may contribute to HPAI preparedness, we sought to identify biomedical, societal, economic and domain-overarching gaps in the preparation for and during our simulation exercises. We observed discordance between the level of knowledge and know-how gaps: most of the identified biomedical gaps were related to fundamental HPAI research and to available HPAI knowledge that has not (yet) been incorporated into guidelines, while societal gaps were mainly related to the lack of availability of general response guidelines, surveillance structures and infrastructures in case of any new outbreak (see supplement S1 Table). Domain-overarching gaps were also more related to general domain-overarching outbreak response gaps than to those specific to HPAI outbreak response. Those that surfaced during the integrated session were primarily related to a lack of cooperation or alignment in the outbreak response. We have listed the domain-overarching gaps identified in the preparation and during the simulation exercise in Table 4.

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Table 4. Domain-overarching knowledge and know-how gaps identified by participants in the interviews and observed by participants or research team during the HPAI simulation exercises in April and May 2024, the Netherlands.

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Discussion

The simulation exercises demonstrated that different deliberation topics and a different interpretation of the acute outbreak situation can lead to considerable discrepancies between the separate scientific advisory teams. Discrepancies were found both between the separate pieces of expert advice as well as in the process of advising. These discrepancies included blind spots towards other expert fields, a different sense of urgency and a different sense of proportionality among the participating experts. During integrated deliberations, the different interpretations converged, which seemed to be a prerequisite for creating space to resolve the observed conflicting topics and characteristics of the advice process.

Multidisciplinary team meetings, providing space for multiple perspectives and consensus building, are well established in many complex clinical situations and are part of standard hospital care settings. Although these teams are often considered as multidisciplinary within the biomedical field, it is not uncommon for disciplines such as social work to be included in such teams. Social perspectives on issues are therefore often secured in such teams. Extending this concept to complex public health situations seems only comprehensible. In an advisory report (2022) of the Royal Netherlands Academy of Arts and Sciences and the Partnership Contribution High-Level Implementation Plan III 2024–2030 of the WHO, collaboration across disciplines to be adequately prepared for large-scale infectious outbreaks is recommended [38,39]. The European Commission already secured experts from various disciplines in their advisory committee for the occurrence and recognition of a public health emergency at Union level in their regulations about serious cross-border threats (2022/2371) [40]. However, there are no clear guidelines how to collaborate across disciplines in pandemic settings when the certainty of scientific evidence can be low and decision-makers need actionable advice to act swiftly. Existing evidence-to-decision/recommendation frameworks such as the Grading of Recommendations Assessment, Development and Evaluation, known as GRADE, assists the process of grading quality (or certainty) of evidence and strength of recommendations [41]. GRADE is considered a standard approach for guideline development in biomedical areas, but not specifically in public health. A GRADE Evidence to Decision framework for health system and public health decisions was developed in 2018, but does not specifically describe the multidisciplinary collaboration, nor does it necessarily produce an integrated advice [41]. The WHO INTEGRATE evidence-to-decision framework (2019) was developed as more holistic approach, but seemed limited adopted by national advisory structures thus far [42].

In our simulation exercises, the diversity of discussion points in the integrated deliberations was less extensive in the second simulation exercise. Possibly, the groups dynamics were different in the second simulation session due to the composition of different teams, but perhaps the interpretation of the severity of the outbreak was more comparable among all experts due to the strongly increased number of (hospitalized) cases -in particular youngsters- in the scenario. A best and worst-case scenario was also presented before the separate deliberations in the second simulation session, creating a similar scenario base for both groups. In addition, several experts had participated in the first simulation exercise, which could have influenced the level of basic knowledge of the participants in the second simulation exercise. We believe that the settings of the second simulation exercises contributed to a more aligned interpretation and goal setting among the participating experts. However, it is arguable whether these deliberations should take place during an acute outbreak situation. Improvement of basic knowledge and identification of blind sports should preferably be overcome in the preparation phase of potential new outbreaks, rather than during a crisis, as this runs the risk of delaying the ability to provide advice in a timely manner. The continuation of simulation exercises with participants from different disciplines -as suggested by the WHO in the preparation of large-scale influenza outbreaks- may be a good way forward to further train the collaborative development of advice [43].

The legacies of the COVID-19 pandemic appeared to be both a great source of acquired pandemic-related know-how and knowledge as well as a barrier to interpret the simulation outbreak without the negative COVID-19 sentiments. As for children, the negative effects of the public health measures taken were considered to be more impactful than the disease during the COVID-19 pandemic by the societal expert group. Therefore, the proposed public health measures affecting children in the outbreak simulation led to an extensive proportionality discussion during the integrated sessions. A discussion of the precautionary approach, or the underlying principles and values of the considerations of advice, could have assisted in this process of alignment. This was not included in the design of the simulation exercise and was therefore not structurally addressed. School closure should be weighed thoroughly as there are still many uncertainties about its health, economic and social impact [44]. However, the school closure data collected in COVID-19 may not be applicable to an influenza outbreak once children are at risk for severe disease, as in our fictitious simulation exercise, or during the 1918 influenza pandemic (Spanish flu) or the 2009 H1N1 influenza outbreak [44,45]. During the latter outbreak, several schools in the United States were closed for a variety of reasons, including high student absenteeism [46]. Pre-simulation framing or context stripping might be considered for future research to minimize potential bias caused by experiences in previous outbreaks.

Preferably, knowledge and know-how gaps are minimized to optimize pandemic preparedness. To contribute to the optimization process, we have tried to identify current knowledge and know-how gaps specific to avian influenza in the preparation for and during our fictitious simulation exercises. Although many goals have already been achieved in the Netherlands, a few goals of the WHO checklist for respiratory pathogen pandemic preparedness planning were identified as gaps [43]. Optimization and incorporation of how to achieve equity of (potentially) scarce resources -such as PPE or vaccines- should be prioritized now that the avian influenza threat is considered as low. In particular as the current 2024 Mpox outbreak demonstrate that equitable access to vaccines is still challenging [47].

To align with the biomedical level of outbreak preparedness, the societal sciences should prepare for swift action when needed, for example by developing guidelines or -as suggested in previous research- ‘field-ready methodological tools’ [48]. The assessment framework developed by the MIT (July 2024) -a tool that can be used to identify the effectiveness and societal impact of measures, together with the key dilemmas and underlying values- is a major step in this process [49].

In addition to the observed misalignment in urgency and potential scenarios, the different implicit policy goals set by the separate teams seemed to have contributed to the divergence of proposed measures. Additionally, the different underlying perspectives of the separate teams on the proposed measures—outbreak management and limiting the outbreak’s impact versus communication and information strategies and limiting the impact of measures—seemed to enhance the misalignment. Therefore, it seems valuable to systematically structure separate deliberations in future research to minimize potential misalignment and blind spots caused by different policy goals, perspectives, or expectations of potential scenarios. To explore how to minimize disciplinary blind spots, future research should also focus on studying different methods of interaction between the two teams.

Our simulation exercises had some limitations. First, due to the design of the simulation exercise, the participants had only limited time to deliberate, and craft separate or integrated advice. Also, conflicting and aligned perspectives were not systematically assessed during the simulation exercise, and an aligned structure of the deliberations of the separate teams was not included. This probably influenced the accuracy of the advice formulated. Future research should incorporate a systematic assessment of potential conflicting topics and ensure alignment in the deliberation processes of the separate teams.

Second, though the scenario was well prepared and certain information will be unknown during an actual outbreak and needs to be investigated -such as the extent of the outbreak, who is most at risk, etc-, some information was unknown in our scenarios that would be known in real life -such as the number of doses of anti-viral medication. Veterinary cases were included in the second simulation exercise to a limited extent for simplicity purposes, but would not be neglected in a real outbreak. Also, the scenarios included biomedical, social and economic perspectives, but it included only limited modeling perspectives and no perspectives from other disciplines. It did not include simulated real-time data for example. Recommendations such as school closure, were drafted in the absence of this relevant data. In addition, the experience of external attention and pressure -e.g., from politicians, foreign countries, the media- is challenging to incorporate into a simulation exercise but cannot be neglected in real-life decision making. These limitations may have influenced the deliberations and advice formulated. We recommend incorporating a fictitious dashboard into future simulation exercises to enable modeling and recommendations based on real-time (fictitious) data.

Third, the results of these exercises were both exercise and participant dependent. A different scenario -or another pandemic phase of this scenario- and/or different participating experts could have led to different group dynamics, different communication styles and/or different perceived authority or hierarchy within the teams and may have led to different results. To include normative guidance, it is recommended to include ethicists and legal experts in future research.

Finally, the knowledge gaps described are only identified for the context of fictitious avian influenza exercise in the Netherlands and are not exhaustive. For effective pandemic preparedness, it would be useful to provide an overview of the urgency of the gaps identified in follow-up research.

Despite the limitations and the fictitious scenario, the identified misalignments in the advisory process, structures and knowledge and know-how gaps reflect current challenges in the scientific advisory process. The conflicting perspective of fictitious recommendations should also be taken seriously, as it provides food for thought on how to prepare for an actual outbreak of concern. The Dutch national advisory structures were used as the basis for our research project. However, since multidisciplinary perspectives should be incorporated into any pandemic advisory structure, we think our results regarding the integration of scientific advice can be generalized and are useful for other countries seeking to optimize outbreak management.

Conclusion

Our simulation exercises of a fictitious acute HPAI outbreak in the Netherlands suggest that an integrated approach to pandemic advice combining biomedical and socio-economic expertise is challenging, but may have value in overcoming conflicting advice and discrepancies in the process of advice. Even though our research was national oriented, and participants and group dynamics could have influenced our results. Further exploration using additional scenario exercises is therefore needed, to determine whether and under what circumstances combined advisory bodies could add value. These outcomes will be beneficial for the Netherlands and beyond. Most of the identified knowledge and know-how gaps should be addressed in the current pre-pandemic phase, to reduce the loss of valuable time during an outbreak of (potential) pandemic threat. Pre-pandemic training and simulation exercises may help train such collaboration to prepare for potential pandemics.

Supporting information

Acknowledgments

The authors gratefully acknowledge all participants for their contribution during the interviews and the simulation exercises.