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One Health and Zoonotic Uncertainty in Singapore and Australia: Examining Different Regimes of Precaution in Outbreak Decision-Making

One Health and Zoonotic Uncertainty in Singapore and Australia: Examining Different Regimes of... Abstract A One Health approach holds great promise for attenuating the risk and burdens of emerging infectious diseases (EIDs) in both human and animal populations. Because the course and costs of EID outbreaks are difficult to predict, One Health policies must deal with scientific uncertainty, whilst addressing the political, economic and ethical dimensions of communication and intervention strategies. Drawing on the outcomes of parallel Delphi surveys conducted with policymakers in Singapore and Australia, we explore the normative dimensions of two different precautionary approaches to EID decision-making—which we call regimes of risk management and organizing uncertainty, respectively. The imperative to act cautiously can be seen as either an epistemic rule or as a decision rule, which has implications for how EID uncertainty is managed. The normative features of each regime, and their implications for One Health approaches to infectious disease risks and outbreaks, are described. As One Health attempts to move upstream to prevent rather than react to emergence of EIDs in humans, we show how the approaches to uncertainty, taken by experts and decision-makers, and their choices about the content and quality of evidence, have implications for who pays the price of precaution, and, thereby, social and global justice. Introduction Science, governance, politics and ethics easily become entangled in infectious disease policymaking, especially during a public health emergency (Verweij, 2011; Jennings and Arras, 2016). When scientists predict or detect an imminent global catastrophe, such as a pandemic, then the political and administrative system must react (Silverstein, 1981; Neustadt and Fineberg, 1983). Decisions must be made as to what response, if any, is the most appropriate, and what level of direct costs, and social and economic upheaval, is justified by the hazard faced. However, if the threat is a novel emerging infectious disease (EID) then the characteristics of the new pathogen are a priori unknown and the accuracy of scientific predictions as to its social, economic and public health implications may only increase (or occasionally decrease) with experience accumulated during the course of the outbreak.1 Looking back at recent pandemics and global infectious disease emergencies it is often possible to see at which point a sub-optimal choice about the content or scope of a public health intervention was made (Phillips et al., 2000; Campbell, 2006; Saunders-Hastings and Krewski, 2016). While the merits of evidence-led policymaking during EID outbreaks are easily understood and valorized, sometimes decisions must be made under conditions in which evidence is absent or unclear and extreme uncertainty surrounds the risks entailed by the threat faced (Silverstein, 1981; Rosella et al., 2013). There is no ‘bright line’ between states of certainty and uncertainty; rather circumstances in which decisions need to be made can be characterized by higher or lower levels of certainty (Weber, 1987). Past experience shows that when decision-makers are forced to make decisions under conditions of higher uncertainty, they tend to fall back upon their own values and pre-determined sets of assumptions for guidance (Schon and Rein, 1994; Rosella et al., 2013; Davis et al., 2014; Baekkeskov, 2016). In the case of EIDs, the effects of uncertainty may complicate the establishment and operation of successful population and syndromic surveillance programs and extend beyond organizing a proportionate and effective response. Integrated syndromic surveillance can potentially provide an early indication that something is happening (Brookes et al., 2015; Meyers et al., 2018), but because these systems can only be alert to known pathogens or patterns of disease manifestations, sometimes we are fundamentally ignorant of emergent communicable disease risks (Gould et al., 2017; Petersen et al., 2018). Often this ignorance (arguably a form of extreme uncertainty) is relatively unimportant because the broader health and socio-economic impacts of such an EID outbreak are minimal. In contrast, in other cases, such as Ebola virus disease, severe acute respiratory syndrome (SARS) or bovine spongiform encephalopathy (BSE), the consequences of ignorance were significant, even catastrophic. Even as anticipatory technologies and scientific knowledge continue to advance, uncertainty about the nature, risks and impacts of many EIDs continues to constrain efforts to move ‘upstream’ to prospectively prevent rather than react to new animal-borne infectious or ‘zoonotic’ risks and significant communicable disease outbreaks (Lakoff, 2007; Scoones, 2010). A One Health approach to EIDs holds great promise. It may provide benefits and attenuate EID burdens in both human and animal populations (Zinsstag et al., 2007). One Health refers to an integrated approach to the health of people, animals and their shared ecosystems. It aims to co-promote the health of humans, animals and ecosystems for mutual benefit, through an explicit recognition that all animal species provide a potentially shared reservoir for pathogen exchange and spread, and that most EIDs are driven by varied and dynamic human–animal interactions (Zinsstag et al., 2011). A One Health approach, like any EID policy, must deal with scientific uncertainty, whilst addressing the political, economic and ethical dimensions of communication and intervention strategies (Capps et al., 2015; Degeling et al., 2015; van Herten et al., 2019). Against this background, policy responses to uncertainty in domains pertinent to public health are increasingly construed through the logic and rhetoric of precaution (Kriebel and Tickner, 2001; Weed, 2004). At its core, precaution is the idea that anticipatory and preventive actions should be preferred when the probability of the outcomes is unknowable or ambiguous (Steel, 2015). Yet, as numerous critiques have pointed out, sometimes the application of precautionary approaches only provides limited guidance for those responsible for expending resources in responding to a dynamic and rapidly changing situation (Sunstein, 2005; Munthe, 2011). To better understand the implications of One Health, for the practice of zoonotic EID management, we conducted parallel Delphi surveys with policymakers, public health researchers, wildlife ecologists and human and animal health practitioners in Singapore (n = 24) and Australia (n = 52), in 2014. The aim was to explore expert perspectives on the priorities and values that should underpin One Health responses to infectious disease risks at the human–animal interface, in each of these settings. In this article, we compare previously unreported outcomes of these studies to gain further insights into how to answer these questions and manage uncertainty, within One Health policymaking and practice in different settings. We then draw on recent scholarship in anthropology, bioethics and policy studies to elucidate and further analyse the normative features of the One Health paradigm for the management of infectious disease risks and outbreaks, as well as some of the problems faced in shifting its domain of practice and focus from outbreak response towards the goal of EID prevention. Experimenting with Experts: Emerging Zoonotic Risks and One Health Policymaking The Delphi studies in Singapore and Australia were independently funded by competitive research grants in each jurisdiction. Singapore is an island City-State of just under 6 million people located on the Southern tip of the Malaysian Peninsula. A major centre in global commerce and trade, Singapore has been affected by and managed some of the most recent major global infectious disease outbreaks e.g. due to Zika virus, SARS, Nipah virus and H1N1 2009 influenza. Australia, in contrast, has a population of 27 million who inhabit a large island continent in the Southern Ocean. Like most countries, Australia is affected by the global circulation of seasonal and pandemic influenza viruses, but its geographic position has meant that it has been relatively protected from most recent EID outbreaks of global importance. The composition of each Delphi panel was designed to cut across stakeholder groups and policy scales, by incorporating physicians, veterinarians, ecologists and public health practitioners who occupied various roles at operational, tactical and strategic levels. The methods employed and results of each of these national studies have been reported previously (Lysaght et al., 2017; Degeling et al., 2017). The survey design was developed by the team in Singapore and then used as a template for the Australian survey. To facilitate the potential for later comparison, the initial rounds of both surveys comprised structured questions regarding the most appropriate response to similar fictional outbreak scenarios. Because Delphi surveys are participant driven through a process of collating, synthesizing and presenting back to participants a synopsis of their responses to structured questions (Adler and Ziglio, 1996; Hsu and Sandford, 2007), the design and content of the surveys in Singapore and Australia diverged over subsequent rounds. Variations were also anticipated because of differences in national policy contexts, and how these structures shape the professional commitments of comparable sets of experts in each group. Below, we report on how each panel responded when asked to indicate when and how they would act if they were responsible for responding to a potential highly significant zoonotic disease outbreak. Text Box 1 A group of dead birds are found in the [Sugei Buroh/Hunter Region] Wetland National Park with 7–10 relatively fresh carcasses. Rangers report that many birds are also ill, with swollen heads and breathing difficulties. The [Local] Health District is reporting a higher than usual rate of people turning up at GP surgeries with upper respiratory symptoms. Local hospitals have also noticed a spike in pneumonia cases. There is a proposal to trace and quarantine all visitors to the National Park area since the birds first became ill, but that will be difficult as there is no way to track visitors and some visitors from overseas may have already left [Singapore /Australia]. There are also proposals to attempt to control migratory birds who visit the reserve. Deciding When to Act As part of the first round, panellists in Singapore and Australia were presented with a locally situated version of the scenario in Text box 1. The scenario describes a potential and co-incident outbreak of a respiratory disease affecting co-located human and avian populations. It was written so that key details about the characteristics of the pathogen or pathogens were not known to participants, in order to simulate the evidentiary conditions when a potentially novel zoonotic disease is first emerging. This level of uncertainty prompted requests for more information from some participants in both groups, but in their comments and discussions many acknowledged that, on occasion, their roles required them to make judgements based on fluid or incomplete evidence. The question: ‘How do you think this scenario will play out in present-day Singapore/Australia?’ elicited 42 free-text responses from participants across both surveys. Responses varied in detailing a range of different outbreak investigation and surveillance techniques, veterinary and public health interventions and health communication strategies. Despite the heterogeneity of the panellist’s descriptions (and acknowledging that some participants’ answers incorporated elements of both), their responses can be categorized by the emphasis placed on two distinct positions or sets of initial priorities: a focus on gathering scientific information to conclusively demonstrate a link between the presence of a respiratory disease in human and animal populations before taking any steps to prevent further infections; and, a focus on acting immediately to minimize the risk of further infections on the assumption that this could be the beginning of a major zoonotic disease outbreak. Notably, the idea that some form of precaution is necessary was common to all of the panellist’s responses. However, this common requirement for precaution did not lead respondents to take the same position on when to act and what to do. As the quotes from participant responses contained in Table 1 demonstrate, at their extremes these positions lead to very different sets of interventions. Table 1. Examples of Delphi Participant Responses to the Question: ‘How do you think this scenario will play out in present-day Singapore/Australia?’ Precautionary strategy . Wait and see . Act immediately . Singapore ‘This is a massive over-reaction. Birds may die of many things and the scenario above has not shown a link between bird deaths and the human respiratory disease’ ‘Quarantine all patients who have had exposure to Sungei Buroh within the last 10 days, monitor all people these patients who have had contact with, prevent people from entering Sungei Buroh until scientific proof has been obtained that the patients did not pick up an agent from this area that caused their pneumonia’ Australia ‘This sort of scenario probably occurs reasonably frequently and the relevant parties are not aware. (so simply goes away)’ ‘There would be an urgent push to get any people reporting symptoms to seek medical advice immediately, with things like hotlines set up. I would also expect immediate veterinary/biosecurity investigations to begin into the birds in the national park and if that showed some kind of disease present’ Precautionary strategy . Wait and see . Act immediately . Singapore ‘This is a massive over-reaction. Birds may die of many things and the scenario above has not shown a link between bird deaths and the human respiratory disease’ ‘Quarantine all patients who have had exposure to Sungei Buroh within the last 10 days, monitor all people these patients who have had contact with, prevent people from entering Sungei Buroh until scientific proof has been obtained that the patients did not pick up an agent from this area that caused their pneumonia’ Australia ‘This sort of scenario probably occurs reasonably frequently and the relevant parties are not aware. (so simply goes away)’ ‘There would be an urgent push to get any people reporting symptoms to seek medical advice immediately, with things like hotlines set up. I would also expect immediate veterinary/biosecurity investigations to begin into the birds in the national park and if that showed some kind of disease present’ Open in new tab Table 1. Examples of Delphi Participant Responses to the Question: ‘How do you think this scenario will play out in present-day Singapore/Australia?’ Precautionary strategy . Wait and see . Act immediately . Singapore ‘This is a massive over-reaction. Birds may die of many things and the scenario above has not shown a link between bird deaths and the human respiratory disease’ ‘Quarantine all patients who have had exposure to Sungei Buroh within the last 10 days, monitor all people these patients who have had contact with, prevent people from entering Sungei Buroh until scientific proof has been obtained that the patients did not pick up an agent from this area that caused their pneumonia’ Australia ‘This sort of scenario probably occurs reasonably frequently and the relevant parties are not aware. (so simply goes away)’ ‘There would be an urgent push to get any people reporting symptoms to seek medical advice immediately, with things like hotlines set up. I would also expect immediate veterinary/biosecurity investigations to begin into the birds in the national park and if that showed some kind of disease present’ Precautionary strategy . Wait and see . Act immediately . Singapore ‘This is a massive over-reaction. Birds may die of many things and the scenario above has not shown a link between bird deaths and the human respiratory disease’ ‘Quarantine all patients who have had exposure to Sungei Buroh within the last 10 days, monitor all people these patients who have had contact with, prevent people from entering Sungei Buroh until scientific proof has been obtained that the patients did not pick up an agent from this area that caused their pneumonia’ Australia ‘This sort of scenario probably occurs reasonably frequently and the relevant parties are not aware. (so simply goes away)’ ‘There would be an urgent push to get any people reporting symptoms to seek medical advice immediately, with things like hotlines set up. I would also expect immediate veterinary/biosecurity investigations to begin into the birds in the national park and if that showed some kind of disease present’ Open in new tab In a subsequent question in round one, Delphi participants in Singapore and Australia were asked to: ‘Indicate when, if ever, the general public should be informed of a significant escalation of the risk of animal-to-human disease transmission?’ The results suggest that both positions 1 and 2 have significant levels of support within each group of panellists (Figure 1). If we collapse the analytic categories down to a choice between deciding to act immediately to protect the public or wait for scientific proof that a spill-over event has taken place, then the majority of participants in both settings prefer to wait until the epidemiological link is clear. However, a third of participants in Singapore and less than a quarter of Australian participants were in favour of informing the public before evidence of the link between the disease in birds and humans was certain. Figure 1. Open in new tabDownload slide Participant responses to multiple choice question as to when should the general public be informed about a significant escalation in zoonotic disease risks? Figure 1. Open in new tabDownload slide Participant responses to multiple choice question as to when should the general public be informed about a significant escalation in zoonotic disease risks? Deciding What Actions to Take In the second round of both Delphi surveys, panellists were asked to rank their key priorities in developing a plan of action when faced with a major zoonotic risk. Table 2 describes the final rankings produced by each Delphi survey. Arguably, the rationale for prioritizing one of these dimensions over others is because they are considered, on balance, to be the ‘worst’ things that could happen in the given circumstances, or are key factors in attenuating or preventing these unwanted outcomes (Finkel, 1995; Duckett et al., 2015). Therefore, items on these lists can be understood as objectives, constraints or hazards that the panellists in each of these settings believe should be central in policy considerations about response planning and implementation. Table 2. The Priorities of Delphi Participants in Emerging Infectious Disease (EID) Response Planning . Singapore . Australia . 1. Impacts on human health Impacts on human health 2. Impacts on animal health or welfare Availability of human and healthcare resources 3. Availability of human and healthcare resources Continuity of food supply/essential services 4. Economic impacts Public education about the risks faced 5. Ease of tracking exposed persons Economic impacts 6. Potential public reaction The financial cost of implementing the plan 7. Impact on public transport Potential public reaction 8. Emotional/psychological stress on individuals Ease of tracking exposed persons 9. Impacts on tourism and travel Impacts on health and welfare of animals 10. Reputation of Singapore Emotional/psychological stress on individuals . Singapore . Australia . 1. Impacts on human health Impacts on human health 2. Impacts on animal health or welfare Availability of human and healthcare resources 3. Availability of human and healthcare resources Continuity of food supply/essential services 4. Economic impacts Public education about the risks faced 5. Ease of tracking exposed persons Economic impacts 6. Potential public reaction The financial cost of implementing the plan 7. Impact on public transport Potential public reaction 8. Emotional/psychological stress on individuals Ease of tracking exposed persons 9. Impacts on tourism and travel Impacts on health and welfare of animals 10. Reputation of Singapore Emotional/psychological stress on individuals The lists of priorities presented to each Delphi panel were slightly different in content and length because each was drawn from the qualitative analyses of panelist’s responses to round one scenario in each setting. Delphi participants in Singapore and Australia were asked to choose from lists of 15 and 19 items, respectively and slightly different scoring systems were used in each setting to generate the final rankings. For further details please see the parent papers (Lysaght et al., 2017; Degeling et al., 2017). Open in new tab Table 2. The Priorities of Delphi Participants in Emerging Infectious Disease (EID) Response Planning . Singapore . Australia . 1. Impacts on human health Impacts on human health 2. Impacts on animal health or welfare Availability of human and healthcare resources 3. Availability of human and healthcare resources Continuity of food supply/essential services 4. Economic impacts Public education about the risks faced 5. Ease of tracking exposed persons Economic impacts 6. Potential public reaction The financial cost of implementing the plan 7. Impact on public transport Potential public reaction 8. Emotional/psychological stress on individuals Ease of tracking exposed persons 9. Impacts on tourism and travel Impacts on health and welfare of animals 10. Reputation of Singapore Emotional/psychological stress on individuals . Singapore . Australia . 1. Impacts on human health Impacts on human health 2. Impacts on animal health or welfare Availability of human and healthcare resources 3. Availability of human and healthcare resources Continuity of food supply/essential services 4. Economic impacts Public education about the risks faced 5. Ease of tracking exposed persons Economic impacts 6. Potential public reaction The financial cost of implementing the plan 7. Impact on public transport Potential public reaction 8. Emotional/psychological stress on individuals Ease of tracking exposed persons 9. Impacts on tourism and travel Impacts on health and welfare of animals 10. Reputation of Singapore Emotional/psychological stress on individuals The lists of priorities presented to each Delphi panel were slightly different in content and length because each was drawn from the qualitative analyses of panelist’s responses to round one scenario in each setting. Delphi participants in Singapore and Australia were asked to choose from lists of 15 and 19 items, respectively and slightly different scoring systems were used in each setting to generate the final rankings. For further details please see the parent papers (Lysaght et al., 2017; Degeling et al., 2017). Open in new tab Unsurprisingly, protecting human health and ensuring that there was sufficient capacity to respond were top priorities in both Delphi surveys. Both parent studies also found significant within-group differences between priorities of participants from human and animal health sectors, which would complicate outbreak decision-making (Lysaght et al., 2017; Degeling et al., 2017). There are, however, some marked differences between the groups, especially in the rankings given to animal health and welfare, and for factors pertaining to tourism, trade and travel, which were given much greater emphasis by participants in Singapore than in Australia. Differences between the groups from Singapore and Australia are likely because policy decisions are not made in a vacuum. Current priorities and heuristics for decision-making are embedded in social, economic, geo-strategic and historical contingencies such as the level of trust political actors have in scientific expertise, their tolerance for risk, and their personal and institutional experiences of outbreak responses and communicable disease policymaking. Institutional and personal factors can colour the advice given and the choices made by decision-makers. With respect to these contingencies, Singapore and Australia have different social, economic and geo-political characteristics, different exposures to global trade, and different histories of pandemic and infectious disease outbreaks. For example, in Singapore it is likely that previous EID outbreaks have prompted a higher prioritization of factors affecting tourism, trade and travel, than in Australia. As noted above, Singapore was an epicentre of the SARS outbreak in 2003. As a global travel hub, the outbreak had enormous and sustained effects on central elements of the Singaporean economy (Wilder-Smith, 2006). Providing support for tourism services and trade became a key government priority during both the SARS response and post-outbreak recovery (Henderson, 2004). Close analysis of post-SARS political discourse in Singapore indicates that balancing the risks and benefits of global connectivity has become a significant concern among decision-makers (Heng, 2013). This is to be expected given Singapore has the world’s highest gross domestic product (GDP) to trade ratio (consistently greater 300 per cent) which means the income from imports and exports is of vital importance to state revenues and, ultimately, the provision of essential social and public services. Another difference was in the prioritization of non-human animal health and welfare. During the height of the SARS crisis, authorities in Singapore began culling the stray feline populations to mitigate the risks of disease transmission. Uncertainty surrounded the natural host of SARS when civets (referred to as ‘cats’) were identified as intermediate hosts, but domestic cats (which are unrelated to civets) were mistakenly believed to be the source (Davis, 2011). Despite the rationale of protecting human health, this response provoked community outrage and a resistance to animal culling. This objection occasionally re-emerges; for example, when native Macaque monkeys are culled to reduce their interactions with human populations (Lysaght et al., 2017). The majority of the Singaporean population are from Asian religious traditions, which tend to view animals differently from the secular or other religious traditions prevalent in Australia. These factors are likely to have influenced the responses of Singaporean panellists, who will have had experiences of being on the frontlines of regional Nipah virus and global Zika virus outbreaks (Ho et al., 2017). In Australia, the focus of the panellists was much more clearly on preparing the public and ensuring that vital systems such as food supply, essential services and the costs of intervention were given sufficient prominence in decision-making. Unlike Singapore, Australia has a large agricultural sector and the location of the fictional outbreak (the Hunter Valley in NSW) is also home to several very large intensive poultry farms. With a wealth of primary industries and large domestic market, the economy in Australia is not as dependent as Singapore on global trade for national income and food security. The valuation of animals in Australian biosecurity policy is oriented around protecting trade concessions and herd productivity, not the health welfare of individual animals (Gray, 2015). Yet in their responses, many panellists emphasized the importance of protecting these industries, while also expressing some surprise at the relatively low ranking of animal health and welfare. Finally, the policy approach to communicable disease in Australia, historically, has been strongly oriented towards maintaining border integrity (Bashford, 2007). Australia’s isolation relative to the population and trading hubs of South East Asia, and the relatively large distances between major metropolitan centres, have protected its population (and decision-makers) from most of the impacts of recent epidemics of global importance. The isolation and geographic scale may have also delayed the onset and slowed the spread, respectively, of pandemic influenza in Australia during each of the three influenza pandemics of the 20th century (Viboud et al., 2004; Bishop et al., 2009). Comparing Two Regimes for Managing EID Risks and Uncertainty Disparities in the responses of the Delphi panels in Singapore and Australia suggest that socio-economic, legal and political contexts influence decision-making during EID outbreaks. Yet, despite these differences, both expert groups also shared a similar range of attitudes and responses to the uncertainty of evidence and ambiguity of risk. As noted earlier, it is rare to have certainty in managing complex situations (such as the incidence and risks of zoonotic disease outbreaks) such that most decisions are made in conditions of greater or lesser certainty. Exposure to higher levels of uncertainty in policy decisions about EIDs is often unavoidable and the political stakes can be high. Post hoc judgement that the response was too early, too late or disproportionate are easy to make, but rarely reflect the ‘fog’ of the conditions under which these decisions have to be made. Therefore, it is hardly surprising that some decision-makers remain cautious about acting precipitously against emergent risks, while also being aware that the worst possible outcome in a political sense is the charge that ‘they did nothing’ (Silverstein, 1981). Acting and not acting both carry significant risks and decisions can be hard to reverse even when the level of threat posed begins to diminish (Tay et al., 2010; Waller et al., 2016). Experience has shown that the social and political impacts of EID uncertainty can be mitigated somewhat by transparency in public communications—but many decision-makers remain more concerned about causing alarm than loosing public trust (Smith, 2006; Hooker et al., 2017). A burgeoning cross-disciplinary field of sociological and philosophical inquiry has examined how institutions, social systems and individuals conceptualize and seek to manage risk and uncertainty (Douglas, 1985; Beck, 1992; Dean, 2010). Scholars from a range of disciplines have drawn on this corpus to examine the logics surrounding communicable disease policy, resulting policy apparatuses and their consequences, in great depth. Synthesizing the work of Lakoff and Collier (2008), Lakoff (2007), Scoones (2010), Baekkeskov (2016) and others, we believe that the need for precaution in EID planning and response can usefully be framed through two idealized and sometimes overlapping approaches to understanding and managing risk and uncertainty (Table 3). We call these two models or systems, for EID control and prevention, the regimes of ‘risk management’ and ‘organizing uncertainty’. Crucially both regimes hinge on different approaches to precaution, and prioritizations and interpretations of knowledge about risk and uncertainty. Table 3. Two Precautionary Regimes in Emerging Infectious Diseases (EID) Decision-Making . Regime of risk management . Regime for organizing uncertainty . Precaution framed as: Sustaining the present by the least restrictive means Securing the future against potential catastrophe Problem definition: The potential link between human and animal cases The potential for a high impact pandemic Overarching goal: Containing EID outbreaks in humans Containing EID emergence Focus is on: What is known What is not known Absence of evidence is: Unquantified or potential risk Uncertainty Decisional orientation: Ideational trajectories Epistemic deliberation Prioritized knowledge: Expert led (exclusionary) Participatory (democratic) Field of surveillance: Statistical and microbiological data Everywhere Implied sentinels: Humans/viruses (genomics) Non-human animals Tipping point for action: Epidemiological /Phylogenetic triggers Syndromic triggers Systems have preference for: False negatives False positives Preferred intervention point: Human-human transmission Animal-environment-human interface Object of protection: The general populations Those at immediate risk from emergence including the marginalized Intended outcome: Maintenance of vital infrastructures System resilience Price of precaution: Unnecessary human/animal morbidity and mortality Unnecessary social/economic upheaval . Regime of risk management . Regime for organizing uncertainty . Precaution framed as: Sustaining the present by the least restrictive means Securing the future against potential catastrophe Problem definition: The potential link between human and animal cases The potential for a high impact pandemic Overarching goal: Containing EID outbreaks in humans Containing EID emergence Focus is on: What is known What is not known Absence of evidence is: Unquantified or potential risk Uncertainty Decisional orientation: Ideational trajectories Epistemic deliberation Prioritized knowledge: Expert led (exclusionary) Participatory (democratic) Field of surveillance: Statistical and microbiological data Everywhere Implied sentinels: Humans/viruses (genomics) Non-human animals Tipping point for action: Epidemiological /Phylogenetic triggers Syndromic triggers Systems have preference for: False negatives False positives Preferred intervention point: Human-human transmission Animal-environment-human interface Object of protection: The general populations Those at immediate risk from emergence including the marginalized Intended outcome: Maintenance of vital infrastructures System resilience Price of precaution: Unnecessary human/animal morbidity and mortality Unnecessary social/economic upheaval Open in new tab Table 3. Two Precautionary Regimes in Emerging Infectious Diseases (EID) Decision-Making . Regime of risk management . Regime for organizing uncertainty . Precaution framed as: Sustaining the present by the least restrictive means Securing the future against potential catastrophe Problem definition: The potential link between human and animal cases The potential for a high impact pandemic Overarching goal: Containing EID outbreaks in humans Containing EID emergence Focus is on: What is known What is not known Absence of evidence is: Unquantified or potential risk Uncertainty Decisional orientation: Ideational trajectories Epistemic deliberation Prioritized knowledge: Expert led (exclusionary) Participatory (democratic) Field of surveillance: Statistical and microbiological data Everywhere Implied sentinels: Humans/viruses (genomics) Non-human animals Tipping point for action: Epidemiological /Phylogenetic triggers Syndromic triggers Systems have preference for: False negatives False positives Preferred intervention point: Human-human transmission Animal-environment-human interface Object of protection: The general populations Those at immediate risk from emergence including the marginalized Intended outcome: Maintenance of vital infrastructures System resilience Price of precaution: Unnecessary human/animal morbidity and mortality Unnecessary social/economic upheaval . Regime of risk management . Regime for organizing uncertainty . Precaution framed as: Sustaining the present by the least restrictive means Securing the future against potential catastrophe Problem definition: The potential link between human and animal cases The potential for a high impact pandemic Overarching goal: Containing EID outbreaks in humans Containing EID emergence Focus is on: What is known What is not known Absence of evidence is: Unquantified or potential risk Uncertainty Decisional orientation: Ideational trajectories Epistemic deliberation Prioritized knowledge: Expert led (exclusionary) Participatory (democratic) Field of surveillance: Statistical and microbiological data Everywhere Implied sentinels: Humans/viruses (genomics) Non-human animals Tipping point for action: Epidemiological /Phylogenetic triggers Syndromic triggers Systems have preference for: False negatives False positives Preferred intervention point: Human-human transmission Animal-environment-human interface Object of protection: The general populations Those at immediate risk from emergence including the marginalized Intended outcome: Maintenance of vital infrastructures System resilience Price of precaution: Unnecessary human/animal morbidity and mortality Unnecessary social/economic upheaval Open in new tab The Regime of Risk Management Arguably, decision-making surrounding EID emergence in most parts of the world, and especially in high-income countries like Singapore and Australia, is currently dominated by a regime of risk management and securitization (Davies, 2008; Wraith and Stephenson, 2009). The focus is on a global and aggregate view of the known and knowable risks and impacts of EID emergence to prepare for, and respond proportionally to mitigate, the impacts of epidemics. Policy responses are pre-determined and based on hard scientific evidence produced by expert systems such as laboratories, databases and population models. Under these conditions precaution acts as an epistemic rule that guides what types of inferences are acceptable in light of the risks of errors. Consequently, when faced with uncertainty there is a preference for false negatives in that it is better not to over-react to a potential outbreak until verifiable evidence is available to support a course of action. Since certainty is rewarded in both science and politics in a way that uncertainty is not, the focus of this regime is on known facts, and not unverified or unknown risks, because they protect decision-makers against over-anticipating the course and impacts of the potential outbreak. The structure and underlying utilitarian ethos of this regime draws the focus of experts and decision-makers to proximally directed technological solutions to mitigate known risks. Because the regime concentrates on and centralizes pathogen transmission between humans and animals, the preferred tools of intervention are culling or vaccinating of animal populations and vaccine and therapeutic development for humans (Capps and Lederman, 2015). Those charged with making decisions about EID risks have a strong interest in using this regime to convert the complexity of responding to outbreaks into a precisely defined and tractable set of problems based on solid scientific principles and data. When the levels of uncertainty are high, the most compelling consideration shaping decision-making is the risk of over-reacting to the threat, such that precaution acts as an epistemic rule. Because actions are only taken once, the outbreak is confirmed and the link between human and animal infection is clear, the price for sustaining this precautionary regime is paid by people who suffer the otherwise preventable morbidity and mortality caused by missing the first few cases. Conversely, delays in acting can also take a greater toll on animal populations through mass culling exercises that could have been avoided or limited if responses were made earlier when the possibility of an outbreak was more confined. The Regime for Organizing Uncertainty Decision-making within the regime for organizing uncertainty, in contrast, is much more responsive to what is not known than in the regime of risk management. Instead of trying to manage risks and reduce hazards by focusing on what is calculable, precaution is a decision rule which provides general guidance on which policy response is most appropriate when decisions must be made in conditions of uncertainty. This regime acknowledges alternative forms of knowledge (such as experienced stewardship and practiced observation) and seeks to include them in policy considerations and decision-making. A set of criteria is still applied to evaluate these alternative sources of evidence, but other ways of knowing the potential impacts and causal pathways are included in considerations, such that there is a greater emphasis on lay and local participation. The sources of uncertainty are made clear such that ‘best practice’ might be acting when the evidence is ‘good enough’, and the decision to limit acceptable evidence to the ‘right kind’ of information is treated as a risk in itself (Japp and Kusche, 2008). Because informal and unverified information from different sources is treated as a form of evidence, decision-making tends to converge through epistemic deliberation as more actionable data emerges. The net effect is that resources are iteratively directed towards an appropriate and proportionate policy response as the seriousness, scope and scale of the outbreak become clearer. The goal of these interventions is to promote system resilience while protecting those at immediate risk from disease emergence, including the most marginalized (Lysaght et al. 2016). Because surveillance and ‘triggers for action’ are both syndromic rather than epidemiological or phylogenetic, as well as initiating earlier responses to uncertain risks, this regime also prompts ‘upstream’ interventions that seek to prevent, rather than just react to, disease emergence at the human–animal–environment interface. Under this regime, there is a strong preference for false positives in that it is believed to be better to over-react to a potential outbreak than to run the risk of missing opportunities for early prevention. The price for this regime of precaution is the dislocations and expenditures related to an unnecessary outbreak response, and the direct and opportunity costs of disrupting social and business activity because of a false positive. Yet, the costs to animal and human health need not be as drastic compared with a delayed response of the risk management regime. Vaccination and culling of animal populations will still be deployed when the risk to human health and systems is very high. The social and economic costs of more frequent responses, as well as the broader scope for anticipatory actions, could also provide justifications for larger structural reforms; measures which can help, for example, to mitigate zoonotic risks by reconfiguring sub-optimal agricultural systems such as the intensive poultry industry (Wallace and Wallace, 2015; Degeling et al., 2016). Discussion Public health structures and systems for managing EID risks can be usefully construed as a civic practice—in that the activities are systematic, rule-bound and aim to achieve specific sets of social and common goods, while also seeking to manage conflicting ethical imperatives and public values (Jennings and Arras, 2016). Historical studies and policy analyses indicate that current practices and systems for the control and prevention of EIDs focus on the worst consequences and are mostly highly utilitarian in orientation (Tay et al., 2010; Davis et al., 2014; Baekkeskov, 2016). But as Table 2 shows, differences between Australian and Singaporean decision-makers priorities, when faced with EID uncertainty indicate that after protecting human health, what is considered to be the greatest good and worst outcomes varies significantly depending on social and political contexts. Against background conditions that valorize technical evidence and expert opinions (Silva et al., 2015), decision-makers tend to see social justice concerns as a constraint on achieving population health outcomes, such that the focus, at times of acute crisis, is on protecting the mean and not the margins (Smith et al., 2019). Nevertheless, drawing on relevant social and anthropological theories we can discern two idealized types of precautionary approaches to EID decision-making apparent in both settings studied. The imperative to act with precaution can perform as either an epistemic or a decision rule, which has implications for how EID surveillance and response systems are operationalized. If precaution is an epistemic rule that constrains decision-makers until there is clear evidence of a link between disease in humans and animal populations, then the overarching imperative becomes to prevent the adverse consequences of a well-intentioned but disproportionate public health response. If precaution is construed as a decision rule that prompts responses that seek to avoid the worst possible outcomes, then the overarching imperative becomes to respond immediately by taking anticipatory actions in order to minimize the possibility of a significant zoonotic outbreak and its consequences. Notably, neither of these approaches to precaution denies that sound factual information and risk assessment can be a foundation for effective and ethical decision-making (Steele, 2006). Indeed, a key finding of both studies was the high value that those charged with responding to EID risks place on accurate microbiological and epidemiological information. However, it is worth noting that the consequences of waiting until there is a conclusive epidemiological or phylogenetic link between disease in animal and human populations does not necessarily prevent a systematic over-reaction to the threat posed. Because epidemiological and microbiological response triggers are typically embedded in technocratic protocols, a disproportionate response to an EID outbreak can also be built into the system regardless of the degree of supporting evidence for causation. The supporting policy architecture for preparedness can preclude rapid and responsive changes in policy direction. With regard to this, policymakers in both Australia and Singapore have expressed their frustration with how they were locked in to ‘over-reacting’ during the 2009 H1N1 ‘swine flu’ pandemic (Tay et al., 2010; Davis et al., 2014; Waller et al., 2016). Rather than waiting for evidence to make the ‘right’ decision, the thresholds for action that operate in this sphere can also be framed around conditions of ‘reasonableness’. Because the stakes are so high, some decision-makers can also take an approach to uncertainty that prioritizes the iterative and careful deliberative assessment of the available options (Rosella et al., 2013; Silva et al., 2018). Differences in approaches to uncertainty among decision-makers also can cause disagreement and controversy, in that the need to exercise precaution focuses attention on and ascribes value to different entities, uses different techniques and tools to generate and evaluate evidence and ultimately directs decision-makers towards different action trigger points and responses. What this means is that, in practice, both modes of precaution can be operationalized simultaneously—either as a deliberate strategy or as an outcome of differences in the priorities of responding agencies; the scope and resources accorded to each ultimately determine the overall nature of the response. Whether precaution acts as a epistemic or decision rule distributes the risks and burdens of infectious disease outbreaks differently such that the ‘price of precaution’ is cashed out in different ways and at different points in social and economic systems (Wildavsky, 1988; Munthe, 2011; Steel, 2015). In the context of pursuing a One Health approach to EIDs, choices about the types of system that are maintained and what counts as evidence have implications for social and global justice, and who bears the burden of risk (Scoones, 2010; Wallace et al. 2015; Lysaght et al., 2017). The normative underpinnings of One Health are yet to be coherently articulated (Johnson and Degeling, 2019; van Herten et al., 2019). Nevertheless, how precaution frames One Health decisions is not just a matter of epistemic conventions and technical expertise, it goes to the core question as to exactly whose health a One Health approach to EID risks meant to protect (Scoones and Forster, 2009). Who should pay the price when society is faced with an imminent threat—the people who might go out of business because of economic disruption, animal populations that could harbour and transmit the disease or the people whose deaths could have been avoided by an earlier intervention? Should decision-making be directed towards avoiding economic losses or protecting the lives of those most at risk? In seeking to monitor and control the emergence and incidence of zoonotic diseases, any strategy of anticipation and precaution should be accompanied by a recognition and justification of all of its costs (Wildavsky, 1988). Furthermore, rather than simply being about human health and well-being, these decisions also have more-than-human dimensions in that the benefits and burdens of interventions will be distributed across species barriers (Capps and Lederman, 2015; Degeling et al., 2016). Here, the concept of justice may be useful in resolving conflicts between the interests and values of human vs non-human health, rural vs urban communities and local vs global responsibilities (Lysaght et al., 2017).2 Because uncertainty and ignorance around EIDs are unavoidable, an emerging focus in One Health programs and polices is the idea of ‘upstream’ prevention. One Health increasingly recognizes that zoonotic disease control programs are most effective when the broader socio-economic and ecological determinants of health are included (Charron, 2012; Zinsstag, 2012). This preventive approach to infectious disease emergence in humans, but also in non-human animals, aligns much more closely with the regime of organizing uncertainty than the regime for managing risks, since it encourages transdisciplinary approaches to expertise and recognizes the value of different epistemologies and knowledge generation outside of the techno-scientific paradigm. It is more inductive and adaptive to exploratory research designs and reflective policymaking that acknowledges what is unknown and makes policy adjustments as new information emerges (Rosella et al., 2013). Rather than rely on precaution as an epistemic rule that draws attention to proximal technological solutions, such as vaccination, the regime for organizing uncertainty lends itself to more structural approaches to EIDs, which shift the focus beyond simply containing, towards attenuating, EID emergence and protecting human life at the margins. Put bluntly, if we wait for certainty, then we are certainly allowing outbreaks to emerge. But by trying to attenuate them, by reacting to the drivers of disease emergence, the focus is not just protecting health in the present—but also the political economy and long-term dynamics of keeping lives safe and maintaining livelihoods within resilient socio-ecological systems (Degeling et al., 2016; van Herten et al., 2019). In this article, we have described two idealized and overlapping models or regimes of precaution in outbreak decision-making. As an organized and evidence-based response to zoonotic risks, the adoption of a One Health approach can transcend scales and systems and redistribute the risks and burdens of interventions both within and across human and non-human animal populations (Buse et al., 2019). Therefore, decision-making in this arena of health protection is complex; and a universally accepted approach to EID uncertainty is not a precondition of a just and effective response to outbreaks. Our studies indicate that these are not constrained by culture or geography but the underlying values behind each approach have yet to be identified. However, neither does this mean that the values that underpin how precaution is operationalized within decision-making should be an institutional convention or a matter of private conviction. Because the evidence that emerges during an EID outbreak does not always conform to plans or prior expectations, the values that underpin EID responses should be a matter for broad, transparent negotiation involving all stakeholders across the globe. Footnotes 1 Examples of circumstances in which the accuracy of scientific predictions have decreased (at least for a time) as experience of the new pathogen accumulates include the 2009 H1N1 swine origin influenza pandemic and the BSE/variant Jakob Creutzfelt (vCJD) event in the 1980s and 1990s in the UK. In the case of swine ‘flu’ pandemic, early predictions from pathogenomic and modelling studies of a high morbidity and mortality event proved to be unfounded, leading to a global overcommitment of resources. In the case of BSE/vCJD, uncertainty as to the public health implications of the zoonotic outbreak increased radically as more information about the prion emerged, causing major precautionary shifts in agricultural, food preparation and blood and organ donation practices. For further details on BSE/vCJD, see: Miller (1999), Phillips et al. (2000) and Forbes (2004). For further details on the 2009 swine ‘flu’ pandemic, see: Rosella et al. (2013) and Ong et al. (2010), for example. 2 One Health challenges conventional paradigms to take into account both human and animal health and re-orientate responses to zoonotic risks around wider community values. As members of our authorship team and others have argued elsewhere, this requires policy reforms that include the explicit consideration of justice to address environmentally linked health disparities both within and across human and relevant non-human animal populations (Rock and Degeling 2015; Lysaght et al., 2017; Degeling et al., 2016). Data availability statement There is no data available for sharing because of restrictions imposed by research ethics approvals in both Australia and Singapore. Acknowledgements We are indebted to the collaborators on both research projects for their contributions to the development of the case scenario, survey design and intellectual insights. Specifically, on the Singapore project, we thank (in alphabetical order) Drs Michele Bailey, David Bickford, Benjamin Capps, Richard Coker and Zohar Lederman; and on the Australian project Profs Michael Ward and Andrew Wilson. Funding This work was supported by the Australian National Health and Medical Research Council under Grants 1083079 and 1102962; and Communicable Diseases-Public Health Research Grant from the Ministry of Health, Singapore under Grant MOH/CDPHRG/0011/2014. Conflict of interest statement The authors have no conflicts of interest to declare. References Adler M. , Ziglio E. ( 1996 ). Gazing into the Oracle: The Delphi Method and Its Application to Social Policy and Public Health . London : Jessica Kingsley Publishers . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Baekkeskov E. ( 2016 ). Explaining Science-Led Policy-Making: Pandemic Deaths, Epistemic Deliberation and Ideational Trajectories . Policy Sciences , 49 , 395 – 419 . Google Scholar Crossref Search ADS WorldCat Bashford A. ( 2007 ). The Age of Universal Contagion’: History, Disease and Globalization. In Medicine at the Border . New York : Springer , pp. 1 – 17 . 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One Health and Zoonotic Uncertainty in Singapore and Australia: Examining Different Regimes of Precaution in Outbreak Decision-Making

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Oxford University Press
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© The Author(s) 2019. Published by Oxford University Press. Available online at www.phe.oxfordjournals.org
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Abstract

Abstract A One Health approach holds great promise for attenuating the risk and burdens of emerging infectious diseases (EIDs) in both human and animal populations. Because the course and costs of EID outbreaks are difficult to predict, One Health policies must deal with scientific uncertainty, whilst addressing the political, economic and ethical dimensions of communication and intervention strategies. Drawing on the outcomes of parallel Delphi surveys conducted with policymakers in Singapore and Australia, we explore the normative dimensions of two different precautionary approaches to EID decision-making—which we call regimes of risk management and organizing uncertainty, respectively. The imperative to act cautiously can be seen as either an epistemic rule or as a decision rule, which has implications for how EID uncertainty is managed. The normative features of each regime, and their implications for One Health approaches to infectious disease risks and outbreaks, are described. As One Health attempts to move upstream to prevent rather than react to emergence of EIDs in humans, we show how the approaches to uncertainty, taken by experts and decision-makers, and their choices about the content and quality of evidence, have implications for who pays the price of precaution, and, thereby, social and global justice. Introduction Science, governance, politics and ethics easily become entangled in infectious disease policymaking, especially during a public health emergency (Verweij, 2011; Jennings and Arras, 2016). When scientists predict or detect an imminent global catastrophe, such as a pandemic, then the political and administrative system must react (Silverstein, 1981; Neustadt and Fineberg, 1983). Decisions must be made as to what response, if any, is the most appropriate, and what level of direct costs, and social and economic upheaval, is justified by the hazard faced. However, if the threat is a novel emerging infectious disease (EID) then the characteristics of the new pathogen are a priori unknown and the accuracy of scientific predictions as to its social, economic and public health implications may only increase (or occasionally decrease) with experience accumulated during the course of the outbreak.1 Looking back at recent pandemics and global infectious disease emergencies it is often possible to see at which point a sub-optimal choice about the content or scope of a public health intervention was made (Phillips et al., 2000; Campbell, 2006; Saunders-Hastings and Krewski, 2016). While the merits of evidence-led policymaking during EID outbreaks are easily understood and valorized, sometimes decisions must be made under conditions in which evidence is absent or unclear and extreme uncertainty surrounds the risks entailed by the threat faced (Silverstein, 1981; Rosella et al., 2013). There is no ‘bright line’ between states of certainty and uncertainty; rather circumstances in which decisions need to be made can be characterized by higher or lower levels of certainty (Weber, 1987). Past experience shows that when decision-makers are forced to make decisions under conditions of higher uncertainty, they tend to fall back upon their own values and pre-determined sets of assumptions for guidance (Schon and Rein, 1994; Rosella et al., 2013; Davis et al., 2014; Baekkeskov, 2016). In the case of EIDs, the effects of uncertainty may complicate the establishment and operation of successful population and syndromic surveillance programs and extend beyond organizing a proportionate and effective response. Integrated syndromic surveillance can potentially provide an early indication that something is happening (Brookes et al., 2015; Meyers et al., 2018), but because these systems can only be alert to known pathogens or patterns of disease manifestations, sometimes we are fundamentally ignorant of emergent communicable disease risks (Gould et al., 2017; Petersen et al., 2018). Often this ignorance (arguably a form of extreme uncertainty) is relatively unimportant because the broader health and socio-economic impacts of such an EID outbreak are minimal. In contrast, in other cases, such as Ebola virus disease, severe acute respiratory syndrome (SARS) or bovine spongiform encephalopathy (BSE), the consequences of ignorance were significant, even catastrophic. Even as anticipatory technologies and scientific knowledge continue to advance, uncertainty about the nature, risks and impacts of many EIDs continues to constrain efforts to move ‘upstream’ to prospectively prevent rather than react to new animal-borne infectious or ‘zoonotic’ risks and significant communicable disease outbreaks (Lakoff, 2007; Scoones, 2010). A One Health approach to EIDs holds great promise. It may provide benefits and attenuate EID burdens in both human and animal populations (Zinsstag et al., 2007). One Health refers to an integrated approach to the health of people, animals and their shared ecosystems. It aims to co-promote the health of humans, animals and ecosystems for mutual benefit, through an explicit recognition that all animal species provide a potentially shared reservoir for pathogen exchange and spread, and that most EIDs are driven by varied and dynamic human–animal interactions (Zinsstag et al., 2011). A One Health approach, like any EID policy, must deal with scientific uncertainty, whilst addressing the political, economic and ethical dimensions of communication and intervention strategies (Capps et al., 2015; Degeling et al., 2015; van Herten et al., 2019). Against this background, policy responses to uncertainty in domains pertinent to public health are increasingly construed through the logic and rhetoric of precaution (Kriebel and Tickner, 2001; Weed, 2004). At its core, precaution is the idea that anticipatory and preventive actions should be preferred when the probability of the outcomes is unknowable or ambiguous (Steel, 2015). Yet, as numerous critiques have pointed out, sometimes the application of precautionary approaches only provides limited guidance for those responsible for expending resources in responding to a dynamic and rapidly changing situation (Sunstein, 2005; Munthe, 2011). To better understand the implications of One Health, for the practice of zoonotic EID management, we conducted parallel Delphi surveys with policymakers, public health researchers, wildlife ecologists and human and animal health practitioners in Singapore (n = 24) and Australia (n = 52), in 2014. The aim was to explore expert perspectives on the priorities and values that should underpin One Health responses to infectious disease risks at the human–animal interface, in each of these settings. In this article, we compare previously unreported outcomes of these studies to gain further insights into how to answer these questions and manage uncertainty, within One Health policymaking and practice in different settings. We then draw on recent scholarship in anthropology, bioethics and policy studies to elucidate and further analyse the normative features of the One Health paradigm for the management of infectious disease risks and outbreaks, as well as some of the problems faced in shifting its domain of practice and focus from outbreak response towards the goal of EID prevention. Experimenting with Experts: Emerging Zoonotic Risks and One Health Policymaking The Delphi studies in Singapore and Australia were independently funded by competitive research grants in each jurisdiction. Singapore is an island City-State of just under 6 million people located on the Southern tip of the Malaysian Peninsula. A major centre in global commerce and trade, Singapore has been affected by and managed some of the most recent major global infectious disease outbreaks e.g. due to Zika virus, SARS, Nipah virus and H1N1 2009 influenza. Australia, in contrast, has a population of 27 million who inhabit a large island continent in the Southern Ocean. Like most countries, Australia is affected by the global circulation of seasonal and pandemic influenza viruses, but its geographic position has meant that it has been relatively protected from most recent EID outbreaks of global importance. The composition of each Delphi panel was designed to cut across stakeholder groups and policy scales, by incorporating physicians, veterinarians, ecologists and public health practitioners who occupied various roles at operational, tactical and strategic levels. The methods employed and results of each of these national studies have been reported previously (Lysaght et al., 2017; Degeling et al., 2017). The survey design was developed by the team in Singapore and then used as a template for the Australian survey. To facilitate the potential for later comparison, the initial rounds of both surveys comprised structured questions regarding the most appropriate response to similar fictional outbreak scenarios. Because Delphi surveys are participant driven through a process of collating, synthesizing and presenting back to participants a synopsis of their responses to structured questions (Adler and Ziglio, 1996; Hsu and Sandford, 2007), the design and content of the surveys in Singapore and Australia diverged over subsequent rounds. Variations were also anticipated because of differences in national policy contexts, and how these structures shape the professional commitments of comparable sets of experts in each group. Below, we report on how each panel responded when asked to indicate when and how they would act if they were responsible for responding to a potential highly significant zoonotic disease outbreak. Text Box 1 A group of dead birds are found in the [Sugei Buroh/Hunter Region] Wetland National Park with 7–10 relatively fresh carcasses. Rangers report that many birds are also ill, with swollen heads and breathing difficulties. The [Local] Health District is reporting a higher than usual rate of people turning up at GP surgeries with upper respiratory symptoms. Local hospitals have also noticed a spike in pneumonia cases. There is a proposal to trace and quarantine all visitors to the National Park area since the birds first became ill, but that will be difficult as there is no way to track visitors and some visitors from overseas may have already left [Singapore /Australia]. There are also proposals to attempt to control migratory birds who visit the reserve. Deciding When to Act As part of the first round, panellists in Singapore and Australia were presented with a locally situated version of the scenario in Text box 1. The scenario describes a potential and co-incident outbreak of a respiratory disease affecting co-located human and avian populations. It was written so that key details about the characteristics of the pathogen or pathogens were not known to participants, in order to simulate the evidentiary conditions when a potentially novel zoonotic disease is first emerging. This level of uncertainty prompted requests for more information from some participants in both groups, but in their comments and discussions many acknowledged that, on occasion, their roles required them to make judgements based on fluid or incomplete evidence. The question: ‘How do you think this scenario will play out in present-day Singapore/Australia?’ elicited 42 free-text responses from participants across both surveys. Responses varied in detailing a range of different outbreak investigation and surveillance techniques, veterinary and public health interventions and health communication strategies. Despite the heterogeneity of the panellist’s descriptions (and acknowledging that some participants’ answers incorporated elements of both), their responses can be categorized by the emphasis placed on two distinct positions or sets of initial priorities: a focus on gathering scientific information to conclusively demonstrate a link between the presence of a respiratory disease in human and animal populations before taking any steps to prevent further infections; and, a focus on acting immediately to minimize the risk of further infections on the assumption that this could be the beginning of a major zoonotic disease outbreak. Notably, the idea that some form of precaution is necessary was common to all of the panellist’s responses. However, this common requirement for precaution did not lead respondents to take the same position on when to act and what to do. As the quotes from participant responses contained in Table 1 demonstrate, at their extremes these positions lead to very different sets of interventions. Table 1. Examples of Delphi Participant Responses to the Question: ‘How do you think this scenario will play out in present-day Singapore/Australia?’ Precautionary strategy . Wait and see . Act immediately . Singapore ‘This is a massive over-reaction. Birds may die of many things and the scenario above has not shown a link between bird deaths and the human respiratory disease’ ‘Quarantine all patients who have had exposure to Sungei Buroh within the last 10 days, monitor all people these patients who have had contact with, prevent people from entering Sungei Buroh until scientific proof has been obtained that the patients did not pick up an agent from this area that caused their pneumonia’ Australia ‘This sort of scenario probably occurs reasonably frequently and the relevant parties are not aware. (so simply goes away)’ ‘There would be an urgent push to get any people reporting symptoms to seek medical advice immediately, with things like hotlines set up. I would also expect immediate veterinary/biosecurity investigations to begin into the birds in the national park and if that showed some kind of disease present’ Precautionary strategy . Wait and see . Act immediately . Singapore ‘This is a massive over-reaction. Birds may die of many things and the scenario above has not shown a link between bird deaths and the human respiratory disease’ ‘Quarantine all patients who have had exposure to Sungei Buroh within the last 10 days, monitor all people these patients who have had contact with, prevent people from entering Sungei Buroh until scientific proof has been obtained that the patients did not pick up an agent from this area that caused their pneumonia’ Australia ‘This sort of scenario probably occurs reasonably frequently and the relevant parties are not aware. (so simply goes away)’ ‘There would be an urgent push to get any people reporting symptoms to seek medical advice immediately, with things like hotlines set up. I would also expect immediate veterinary/biosecurity investigations to begin into the birds in the national park and if that showed some kind of disease present’ Open in new tab Table 1. Examples of Delphi Participant Responses to the Question: ‘How do you think this scenario will play out in present-day Singapore/Australia?’ Precautionary strategy . Wait and see . Act immediately . Singapore ‘This is a massive over-reaction. Birds may die of many things and the scenario above has not shown a link between bird deaths and the human respiratory disease’ ‘Quarantine all patients who have had exposure to Sungei Buroh within the last 10 days, monitor all people these patients who have had contact with, prevent people from entering Sungei Buroh until scientific proof has been obtained that the patients did not pick up an agent from this area that caused their pneumonia’ Australia ‘This sort of scenario probably occurs reasonably frequently and the relevant parties are not aware. (so simply goes away)’ ‘There would be an urgent push to get any people reporting symptoms to seek medical advice immediately, with things like hotlines set up. I would also expect immediate veterinary/biosecurity investigations to begin into the birds in the national park and if that showed some kind of disease present’ Precautionary strategy . Wait and see . Act immediately . Singapore ‘This is a massive over-reaction. Birds may die of many things and the scenario above has not shown a link between bird deaths and the human respiratory disease’ ‘Quarantine all patients who have had exposure to Sungei Buroh within the last 10 days, monitor all people these patients who have had contact with, prevent people from entering Sungei Buroh until scientific proof has been obtained that the patients did not pick up an agent from this area that caused their pneumonia’ Australia ‘This sort of scenario probably occurs reasonably frequently and the relevant parties are not aware. (so simply goes away)’ ‘There would be an urgent push to get any people reporting symptoms to seek medical advice immediately, with things like hotlines set up. I would also expect immediate veterinary/biosecurity investigations to begin into the birds in the national park and if that showed some kind of disease present’ Open in new tab In a subsequent question in round one, Delphi participants in Singapore and Australia were asked to: ‘Indicate when, if ever, the general public should be informed of a significant escalation of the risk of animal-to-human disease transmission?’ The results suggest that both positions 1 and 2 have significant levels of support within each group of panellists (Figure 1). If we collapse the analytic categories down to a choice between deciding to act immediately to protect the public or wait for scientific proof that a spill-over event has taken place, then the majority of participants in both settings prefer to wait until the epidemiological link is clear. However, a third of participants in Singapore and less than a quarter of Australian participants were in favour of informing the public before evidence of the link between the disease in birds and humans was certain. Figure 1. Open in new tabDownload slide Participant responses to multiple choice question as to when should the general public be informed about a significant escalation in zoonotic disease risks? Figure 1. Open in new tabDownload slide Participant responses to multiple choice question as to when should the general public be informed about a significant escalation in zoonotic disease risks? Deciding What Actions to Take In the second round of both Delphi surveys, panellists were asked to rank their key priorities in developing a plan of action when faced with a major zoonotic risk. Table 2 describes the final rankings produced by each Delphi survey. Arguably, the rationale for prioritizing one of these dimensions over others is because they are considered, on balance, to be the ‘worst’ things that could happen in the given circumstances, or are key factors in attenuating or preventing these unwanted outcomes (Finkel, 1995; Duckett et al., 2015). Therefore, items on these lists can be understood as objectives, constraints or hazards that the panellists in each of these settings believe should be central in policy considerations about response planning and implementation. Table 2. The Priorities of Delphi Participants in Emerging Infectious Disease (EID) Response Planning . Singapore . Australia . 1. Impacts on human health Impacts on human health 2. Impacts on animal health or welfare Availability of human and healthcare resources 3. Availability of human and healthcare resources Continuity of food supply/essential services 4. Economic impacts Public education about the risks faced 5. Ease of tracking exposed persons Economic impacts 6. Potential public reaction The financial cost of implementing the plan 7. Impact on public transport Potential public reaction 8. Emotional/psychological stress on individuals Ease of tracking exposed persons 9. Impacts on tourism and travel Impacts on health and welfare of animals 10. Reputation of Singapore Emotional/psychological stress on individuals . Singapore . Australia . 1. Impacts on human health Impacts on human health 2. Impacts on animal health or welfare Availability of human and healthcare resources 3. Availability of human and healthcare resources Continuity of food supply/essential services 4. Economic impacts Public education about the risks faced 5. Ease of tracking exposed persons Economic impacts 6. Potential public reaction The financial cost of implementing the plan 7. Impact on public transport Potential public reaction 8. Emotional/psychological stress on individuals Ease of tracking exposed persons 9. Impacts on tourism and travel Impacts on health and welfare of animals 10. Reputation of Singapore Emotional/psychological stress on individuals The lists of priorities presented to each Delphi panel were slightly different in content and length because each was drawn from the qualitative analyses of panelist’s responses to round one scenario in each setting. Delphi participants in Singapore and Australia were asked to choose from lists of 15 and 19 items, respectively and slightly different scoring systems were used in each setting to generate the final rankings. For further details please see the parent papers (Lysaght et al., 2017; Degeling et al., 2017). Open in new tab Table 2. The Priorities of Delphi Participants in Emerging Infectious Disease (EID) Response Planning . Singapore . Australia . 1. Impacts on human health Impacts on human health 2. Impacts on animal health or welfare Availability of human and healthcare resources 3. Availability of human and healthcare resources Continuity of food supply/essential services 4. Economic impacts Public education about the risks faced 5. Ease of tracking exposed persons Economic impacts 6. Potential public reaction The financial cost of implementing the plan 7. Impact on public transport Potential public reaction 8. Emotional/psychological stress on individuals Ease of tracking exposed persons 9. Impacts on tourism and travel Impacts on health and welfare of animals 10. Reputation of Singapore Emotional/psychological stress on individuals . Singapore . Australia . 1. Impacts on human health Impacts on human health 2. Impacts on animal health or welfare Availability of human and healthcare resources 3. Availability of human and healthcare resources Continuity of food supply/essential services 4. Economic impacts Public education about the risks faced 5. Ease of tracking exposed persons Economic impacts 6. Potential public reaction The financial cost of implementing the plan 7. Impact on public transport Potential public reaction 8. Emotional/psychological stress on individuals Ease of tracking exposed persons 9. Impacts on tourism and travel Impacts on health and welfare of animals 10. Reputation of Singapore Emotional/psychological stress on individuals The lists of priorities presented to each Delphi panel were slightly different in content and length because each was drawn from the qualitative analyses of panelist’s responses to round one scenario in each setting. Delphi participants in Singapore and Australia were asked to choose from lists of 15 and 19 items, respectively and slightly different scoring systems were used in each setting to generate the final rankings. For further details please see the parent papers (Lysaght et al., 2017; Degeling et al., 2017). Open in new tab Unsurprisingly, protecting human health and ensuring that there was sufficient capacity to respond were top priorities in both Delphi surveys. Both parent studies also found significant within-group differences between priorities of participants from human and animal health sectors, which would complicate outbreak decision-making (Lysaght et al., 2017; Degeling et al., 2017). There are, however, some marked differences between the groups, especially in the rankings given to animal health and welfare, and for factors pertaining to tourism, trade and travel, which were given much greater emphasis by participants in Singapore than in Australia. Differences between the groups from Singapore and Australia are likely because policy decisions are not made in a vacuum. Current priorities and heuristics for decision-making are embedded in social, economic, geo-strategic and historical contingencies such as the level of trust political actors have in scientific expertise, their tolerance for risk, and their personal and institutional experiences of outbreak responses and communicable disease policymaking. Institutional and personal factors can colour the advice given and the choices made by decision-makers. With respect to these contingencies, Singapore and Australia have different social, economic and geo-political characteristics, different exposures to global trade, and different histories of pandemic and infectious disease outbreaks. For example, in Singapore it is likely that previous EID outbreaks have prompted a higher prioritization of factors affecting tourism, trade and travel, than in Australia. As noted above, Singapore was an epicentre of the SARS outbreak in 2003. As a global travel hub, the outbreak had enormous and sustained effects on central elements of the Singaporean economy (Wilder-Smith, 2006). Providing support for tourism services and trade became a key government priority during both the SARS response and post-outbreak recovery (Henderson, 2004). Close analysis of post-SARS political discourse in Singapore indicates that balancing the risks and benefits of global connectivity has become a significant concern among decision-makers (Heng, 2013). This is to be expected given Singapore has the world’s highest gross domestic product (GDP) to trade ratio (consistently greater 300 per cent) which means the income from imports and exports is of vital importance to state revenues and, ultimately, the provision of essential social and public services. Another difference was in the prioritization of non-human animal health and welfare. During the height of the SARS crisis, authorities in Singapore began culling the stray feline populations to mitigate the risks of disease transmission. Uncertainty surrounded the natural host of SARS when civets (referred to as ‘cats’) were identified as intermediate hosts, but domestic cats (which are unrelated to civets) were mistakenly believed to be the source (Davis, 2011). Despite the rationale of protecting human health, this response provoked community outrage and a resistance to animal culling. This objection occasionally re-emerges; for example, when native Macaque monkeys are culled to reduce their interactions with human populations (Lysaght et al., 2017). The majority of the Singaporean population are from Asian religious traditions, which tend to view animals differently from the secular or other religious traditions prevalent in Australia. These factors are likely to have influenced the responses of Singaporean panellists, who will have had experiences of being on the frontlines of regional Nipah virus and global Zika virus outbreaks (Ho et al., 2017). In Australia, the focus of the panellists was much more clearly on preparing the public and ensuring that vital systems such as food supply, essential services and the costs of intervention were given sufficient prominence in decision-making. Unlike Singapore, Australia has a large agricultural sector and the location of the fictional outbreak (the Hunter Valley in NSW) is also home to several very large intensive poultry farms. With a wealth of primary industries and large domestic market, the economy in Australia is not as dependent as Singapore on global trade for national income and food security. The valuation of animals in Australian biosecurity policy is oriented around protecting trade concessions and herd productivity, not the health welfare of individual animals (Gray, 2015). Yet in their responses, many panellists emphasized the importance of protecting these industries, while also expressing some surprise at the relatively low ranking of animal health and welfare. Finally, the policy approach to communicable disease in Australia, historically, has been strongly oriented towards maintaining border integrity (Bashford, 2007). Australia’s isolation relative to the population and trading hubs of South East Asia, and the relatively large distances between major metropolitan centres, have protected its population (and decision-makers) from most of the impacts of recent epidemics of global importance. The isolation and geographic scale may have also delayed the onset and slowed the spread, respectively, of pandemic influenza in Australia during each of the three influenza pandemics of the 20th century (Viboud et al., 2004; Bishop et al., 2009). Comparing Two Regimes for Managing EID Risks and Uncertainty Disparities in the responses of the Delphi panels in Singapore and Australia suggest that socio-economic, legal and political contexts influence decision-making during EID outbreaks. Yet, despite these differences, both expert groups also shared a similar range of attitudes and responses to the uncertainty of evidence and ambiguity of risk. As noted earlier, it is rare to have certainty in managing complex situations (such as the incidence and risks of zoonotic disease outbreaks) such that most decisions are made in conditions of greater or lesser certainty. Exposure to higher levels of uncertainty in policy decisions about EIDs is often unavoidable and the political stakes can be high. Post hoc judgement that the response was too early, too late or disproportionate are easy to make, but rarely reflect the ‘fog’ of the conditions under which these decisions have to be made. Therefore, it is hardly surprising that some decision-makers remain cautious about acting precipitously against emergent risks, while also being aware that the worst possible outcome in a political sense is the charge that ‘they did nothing’ (Silverstein, 1981). Acting and not acting both carry significant risks and decisions can be hard to reverse even when the level of threat posed begins to diminish (Tay et al., 2010; Waller et al., 2016). Experience has shown that the social and political impacts of EID uncertainty can be mitigated somewhat by transparency in public communications—but many decision-makers remain more concerned about causing alarm than loosing public trust (Smith, 2006; Hooker et al., 2017). A burgeoning cross-disciplinary field of sociological and philosophical inquiry has examined how institutions, social systems and individuals conceptualize and seek to manage risk and uncertainty (Douglas, 1985; Beck, 1992; Dean, 2010). Scholars from a range of disciplines have drawn on this corpus to examine the logics surrounding communicable disease policy, resulting policy apparatuses and their consequences, in great depth. Synthesizing the work of Lakoff and Collier (2008), Lakoff (2007), Scoones (2010), Baekkeskov (2016) and others, we believe that the need for precaution in EID planning and response can usefully be framed through two idealized and sometimes overlapping approaches to understanding and managing risk and uncertainty (Table 3). We call these two models or systems, for EID control and prevention, the regimes of ‘risk management’ and ‘organizing uncertainty’. Crucially both regimes hinge on different approaches to precaution, and prioritizations and interpretations of knowledge about risk and uncertainty. Table 3. Two Precautionary Regimes in Emerging Infectious Diseases (EID) Decision-Making . Regime of risk management . Regime for organizing uncertainty . Precaution framed as: Sustaining the present by the least restrictive means Securing the future against potential catastrophe Problem definition: The potential link between human and animal cases The potential for a high impact pandemic Overarching goal: Containing EID outbreaks in humans Containing EID emergence Focus is on: What is known What is not known Absence of evidence is: Unquantified or potential risk Uncertainty Decisional orientation: Ideational trajectories Epistemic deliberation Prioritized knowledge: Expert led (exclusionary) Participatory (democratic) Field of surveillance: Statistical and microbiological data Everywhere Implied sentinels: Humans/viruses (genomics) Non-human animals Tipping point for action: Epidemiological /Phylogenetic triggers Syndromic triggers Systems have preference for: False negatives False positives Preferred intervention point: Human-human transmission Animal-environment-human interface Object of protection: The general populations Those at immediate risk from emergence including the marginalized Intended outcome: Maintenance of vital infrastructures System resilience Price of precaution: Unnecessary human/animal morbidity and mortality Unnecessary social/economic upheaval . Regime of risk management . Regime for organizing uncertainty . Precaution framed as: Sustaining the present by the least restrictive means Securing the future against potential catastrophe Problem definition: The potential link between human and animal cases The potential for a high impact pandemic Overarching goal: Containing EID outbreaks in humans Containing EID emergence Focus is on: What is known What is not known Absence of evidence is: Unquantified or potential risk Uncertainty Decisional orientation: Ideational trajectories Epistemic deliberation Prioritized knowledge: Expert led (exclusionary) Participatory (democratic) Field of surveillance: Statistical and microbiological data Everywhere Implied sentinels: Humans/viruses (genomics) Non-human animals Tipping point for action: Epidemiological /Phylogenetic triggers Syndromic triggers Systems have preference for: False negatives False positives Preferred intervention point: Human-human transmission Animal-environment-human interface Object of protection: The general populations Those at immediate risk from emergence including the marginalized Intended outcome: Maintenance of vital infrastructures System resilience Price of precaution: Unnecessary human/animal morbidity and mortality Unnecessary social/economic upheaval Open in new tab Table 3. Two Precautionary Regimes in Emerging Infectious Diseases (EID) Decision-Making . Regime of risk management . Regime for organizing uncertainty . Precaution framed as: Sustaining the present by the least restrictive means Securing the future against potential catastrophe Problem definition: The potential link between human and animal cases The potential for a high impact pandemic Overarching goal: Containing EID outbreaks in humans Containing EID emergence Focus is on: What is known What is not known Absence of evidence is: Unquantified or potential risk Uncertainty Decisional orientation: Ideational trajectories Epistemic deliberation Prioritized knowledge: Expert led (exclusionary) Participatory (democratic) Field of surveillance: Statistical and microbiological data Everywhere Implied sentinels: Humans/viruses (genomics) Non-human animals Tipping point for action: Epidemiological /Phylogenetic triggers Syndromic triggers Systems have preference for: False negatives False positives Preferred intervention point: Human-human transmission Animal-environment-human interface Object of protection: The general populations Those at immediate risk from emergence including the marginalized Intended outcome: Maintenance of vital infrastructures System resilience Price of precaution: Unnecessary human/animal morbidity and mortality Unnecessary social/economic upheaval . Regime of risk management . Regime for organizing uncertainty . Precaution framed as: Sustaining the present by the least restrictive means Securing the future against potential catastrophe Problem definition: The potential link between human and animal cases The potential for a high impact pandemic Overarching goal: Containing EID outbreaks in humans Containing EID emergence Focus is on: What is known What is not known Absence of evidence is: Unquantified or potential risk Uncertainty Decisional orientation: Ideational trajectories Epistemic deliberation Prioritized knowledge: Expert led (exclusionary) Participatory (democratic) Field of surveillance: Statistical and microbiological data Everywhere Implied sentinels: Humans/viruses (genomics) Non-human animals Tipping point for action: Epidemiological /Phylogenetic triggers Syndromic triggers Systems have preference for: False negatives False positives Preferred intervention point: Human-human transmission Animal-environment-human interface Object of protection: The general populations Those at immediate risk from emergence including the marginalized Intended outcome: Maintenance of vital infrastructures System resilience Price of precaution: Unnecessary human/animal morbidity and mortality Unnecessary social/economic upheaval Open in new tab The Regime of Risk Management Arguably, decision-making surrounding EID emergence in most parts of the world, and especially in high-income countries like Singapore and Australia, is currently dominated by a regime of risk management and securitization (Davies, 2008; Wraith and Stephenson, 2009). The focus is on a global and aggregate view of the known and knowable risks and impacts of EID emergence to prepare for, and respond proportionally to mitigate, the impacts of epidemics. Policy responses are pre-determined and based on hard scientific evidence produced by expert systems such as laboratories, databases and population models. Under these conditions precaution acts as an epistemic rule that guides what types of inferences are acceptable in light of the risks of errors. Consequently, when faced with uncertainty there is a preference for false negatives in that it is better not to over-react to a potential outbreak until verifiable evidence is available to support a course of action. Since certainty is rewarded in both science and politics in a way that uncertainty is not, the focus of this regime is on known facts, and not unverified or unknown risks, because they protect decision-makers against over-anticipating the course and impacts of the potential outbreak. The structure and underlying utilitarian ethos of this regime draws the focus of experts and decision-makers to proximally directed technological solutions to mitigate known risks. Because the regime concentrates on and centralizes pathogen transmission between humans and animals, the preferred tools of intervention are culling or vaccinating of animal populations and vaccine and therapeutic development for humans (Capps and Lederman, 2015). Those charged with making decisions about EID risks have a strong interest in using this regime to convert the complexity of responding to outbreaks into a precisely defined and tractable set of problems based on solid scientific principles and data. When the levels of uncertainty are high, the most compelling consideration shaping decision-making is the risk of over-reacting to the threat, such that precaution acts as an epistemic rule. Because actions are only taken once, the outbreak is confirmed and the link between human and animal infection is clear, the price for sustaining this precautionary regime is paid by people who suffer the otherwise preventable morbidity and mortality caused by missing the first few cases. Conversely, delays in acting can also take a greater toll on animal populations through mass culling exercises that could have been avoided or limited if responses were made earlier when the possibility of an outbreak was more confined. The Regime for Organizing Uncertainty Decision-making within the regime for organizing uncertainty, in contrast, is much more responsive to what is not known than in the regime of risk management. Instead of trying to manage risks and reduce hazards by focusing on what is calculable, precaution is a decision rule which provides general guidance on which policy response is most appropriate when decisions must be made in conditions of uncertainty. This regime acknowledges alternative forms of knowledge (such as experienced stewardship and practiced observation) and seeks to include them in policy considerations and decision-making. A set of criteria is still applied to evaluate these alternative sources of evidence, but other ways of knowing the potential impacts and causal pathways are included in considerations, such that there is a greater emphasis on lay and local participation. The sources of uncertainty are made clear such that ‘best practice’ might be acting when the evidence is ‘good enough’, and the decision to limit acceptable evidence to the ‘right kind’ of information is treated as a risk in itself (Japp and Kusche, 2008). Because informal and unverified information from different sources is treated as a form of evidence, decision-making tends to converge through epistemic deliberation as more actionable data emerges. The net effect is that resources are iteratively directed towards an appropriate and proportionate policy response as the seriousness, scope and scale of the outbreak become clearer. The goal of these interventions is to promote system resilience while protecting those at immediate risk from disease emergence, including the most marginalized (Lysaght et al. 2016). Because surveillance and ‘triggers for action’ are both syndromic rather than epidemiological or phylogenetic, as well as initiating earlier responses to uncertain risks, this regime also prompts ‘upstream’ interventions that seek to prevent, rather than just react to, disease emergence at the human–animal–environment interface. Under this regime, there is a strong preference for false positives in that it is believed to be better to over-react to a potential outbreak than to run the risk of missing opportunities for early prevention. The price for this regime of precaution is the dislocations and expenditures related to an unnecessary outbreak response, and the direct and opportunity costs of disrupting social and business activity because of a false positive. Yet, the costs to animal and human health need not be as drastic compared with a delayed response of the risk management regime. Vaccination and culling of animal populations will still be deployed when the risk to human health and systems is very high. The social and economic costs of more frequent responses, as well as the broader scope for anticipatory actions, could also provide justifications for larger structural reforms; measures which can help, for example, to mitigate zoonotic risks by reconfiguring sub-optimal agricultural systems such as the intensive poultry industry (Wallace and Wallace, 2015; Degeling et al., 2016). Discussion Public health structures and systems for managing EID risks can be usefully construed as a civic practice—in that the activities are systematic, rule-bound and aim to achieve specific sets of social and common goods, while also seeking to manage conflicting ethical imperatives and public values (Jennings and Arras, 2016). Historical studies and policy analyses indicate that current practices and systems for the control and prevention of EIDs focus on the worst consequences and are mostly highly utilitarian in orientation (Tay et al., 2010; Davis et al., 2014; Baekkeskov, 2016). But as Table 2 shows, differences between Australian and Singaporean decision-makers priorities, when faced with EID uncertainty indicate that after protecting human health, what is considered to be the greatest good and worst outcomes varies significantly depending on social and political contexts. Against background conditions that valorize technical evidence and expert opinions (Silva et al., 2015), decision-makers tend to see social justice concerns as a constraint on achieving population health outcomes, such that the focus, at times of acute crisis, is on protecting the mean and not the margins (Smith et al., 2019). Nevertheless, drawing on relevant social and anthropological theories we can discern two idealized types of precautionary approaches to EID decision-making apparent in both settings studied. The imperative to act with precaution can perform as either an epistemic or a decision rule, which has implications for how EID surveillance and response systems are operationalized. If precaution is an epistemic rule that constrains decision-makers until there is clear evidence of a link between disease in humans and animal populations, then the overarching imperative becomes to prevent the adverse consequences of a well-intentioned but disproportionate public health response. If precaution is construed as a decision rule that prompts responses that seek to avoid the worst possible outcomes, then the overarching imperative becomes to respond immediately by taking anticipatory actions in order to minimize the possibility of a significant zoonotic outbreak and its consequences. Notably, neither of these approaches to precaution denies that sound factual information and risk assessment can be a foundation for effective and ethical decision-making (Steele, 2006). Indeed, a key finding of both studies was the high value that those charged with responding to EID risks place on accurate microbiological and epidemiological information. However, it is worth noting that the consequences of waiting until there is a conclusive epidemiological or phylogenetic link between disease in animal and human populations does not necessarily prevent a systematic over-reaction to the threat posed. Because epidemiological and microbiological response triggers are typically embedded in technocratic protocols, a disproportionate response to an EID outbreak can also be built into the system regardless of the degree of supporting evidence for causation. The supporting policy architecture for preparedness can preclude rapid and responsive changes in policy direction. With regard to this, policymakers in both Australia and Singapore have expressed their frustration with how they were locked in to ‘over-reacting’ during the 2009 H1N1 ‘swine flu’ pandemic (Tay et al., 2010; Davis et al., 2014; Waller et al., 2016). Rather than waiting for evidence to make the ‘right’ decision, the thresholds for action that operate in this sphere can also be framed around conditions of ‘reasonableness’. Because the stakes are so high, some decision-makers can also take an approach to uncertainty that prioritizes the iterative and careful deliberative assessment of the available options (Rosella et al., 2013; Silva et al., 2018). Differences in approaches to uncertainty among decision-makers also can cause disagreement and controversy, in that the need to exercise precaution focuses attention on and ascribes value to different entities, uses different techniques and tools to generate and evaluate evidence and ultimately directs decision-makers towards different action trigger points and responses. What this means is that, in practice, both modes of precaution can be operationalized simultaneously—either as a deliberate strategy or as an outcome of differences in the priorities of responding agencies; the scope and resources accorded to each ultimately determine the overall nature of the response. Whether precaution acts as a epistemic or decision rule distributes the risks and burdens of infectious disease outbreaks differently such that the ‘price of precaution’ is cashed out in different ways and at different points in social and economic systems (Wildavsky, 1988; Munthe, 2011; Steel, 2015). In the context of pursuing a One Health approach to EIDs, choices about the types of system that are maintained and what counts as evidence have implications for social and global justice, and who bears the burden of risk (Scoones, 2010; Wallace et al. 2015; Lysaght et al., 2017). The normative underpinnings of One Health are yet to be coherently articulated (Johnson and Degeling, 2019; van Herten et al., 2019). Nevertheless, how precaution frames One Health decisions is not just a matter of epistemic conventions and technical expertise, it goes to the core question as to exactly whose health a One Health approach to EID risks meant to protect (Scoones and Forster, 2009). Who should pay the price when society is faced with an imminent threat—the people who might go out of business because of economic disruption, animal populations that could harbour and transmit the disease or the people whose deaths could have been avoided by an earlier intervention? Should decision-making be directed towards avoiding economic losses or protecting the lives of those most at risk? In seeking to monitor and control the emergence and incidence of zoonotic diseases, any strategy of anticipation and precaution should be accompanied by a recognition and justification of all of its costs (Wildavsky, 1988). Furthermore, rather than simply being about human health and well-being, these decisions also have more-than-human dimensions in that the benefits and burdens of interventions will be distributed across species barriers (Capps and Lederman, 2015; Degeling et al., 2016). Here, the concept of justice may be useful in resolving conflicts between the interests and values of human vs non-human health, rural vs urban communities and local vs global responsibilities (Lysaght et al., 2017).2 Because uncertainty and ignorance around EIDs are unavoidable, an emerging focus in One Health programs and polices is the idea of ‘upstream’ prevention. One Health increasingly recognizes that zoonotic disease control programs are most effective when the broader socio-economic and ecological determinants of health are included (Charron, 2012; Zinsstag, 2012). This preventive approach to infectious disease emergence in humans, but also in non-human animals, aligns much more closely with the regime of organizing uncertainty than the regime for managing risks, since it encourages transdisciplinary approaches to expertise and recognizes the value of different epistemologies and knowledge generation outside of the techno-scientific paradigm. It is more inductive and adaptive to exploratory research designs and reflective policymaking that acknowledges what is unknown and makes policy adjustments as new information emerges (Rosella et al., 2013). Rather than rely on precaution as an epistemic rule that draws attention to proximal technological solutions, such as vaccination, the regime for organizing uncertainty lends itself to more structural approaches to EIDs, which shift the focus beyond simply containing, towards attenuating, EID emergence and protecting human life at the margins. Put bluntly, if we wait for certainty, then we are certainly allowing outbreaks to emerge. But by trying to attenuate them, by reacting to the drivers of disease emergence, the focus is not just protecting health in the present—but also the political economy and long-term dynamics of keeping lives safe and maintaining livelihoods within resilient socio-ecological systems (Degeling et al., 2016; van Herten et al., 2019). In this article, we have described two idealized and overlapping models or regimes of precaution in outbreak decision-making. As an organized and evidence-based response to zoonotic risks, the adoption of a One Health approach can transcend scales and systems and redistribute the risks and burdens of interventions both within and across human and non-human animal populations (Buse et al., 2019). Therefore, decision-making in this arena of health protection is complex; and a universally accepted approach to EID uncertainty is not a precondition of a just and effective response to outbreaks. Our studies indicate that these are not constrained by culture or geography but the underlying values behind each approach have yet to be identified. However, neither does this mean that the values that underpin how precaution is operationalized within decision-making should be an institutional convention or a matter of private conviction. Because the evidence that emerges during an EID outbreak does not always conform to plans or prior expectations, the values that underpin EID responses should be a matter for broad, transparent negotiation involving all stakeholders across the globe. Footnotes 1 Examples of circumstances in which the accuracy of scientific predictions have decreased (at least for a time) as experience of the new pathogen accumulates include the 2009 H1N1 swine origin influenza pandemic and the BSE/variant Jakob Creutzfelt (vCJD) event in the 1980s and 1990s in the UK. In the case of swine ‘flu’ pandemic, early predictions from pathogenomic and modelling studies of a high morbidity and mortality event proved to be unfounded, leading to a global overcommitment of resources. In the case of BSE/vCJD, uncertainty as to the public health implications of the zoonotic outbreak increased radically as more information about the prion emerged, causing major precautionary shifts in agricultural, food preparation and blood and organ donation practices. For further details on BSE/vCJD, see: Miller (1999), Phillips et al. (2000) and Forbes (2004). For further details on the 2009 swine ‘flu’ pandemic, see: Rosella et al. (2013) and Ong et al. (2010), for example. 2 One Health challenges conventional paradigms to take into account both human and animal health and re-orientate responses to zoonotic risks around wider community values. As members of our authorship team and others have argued elsewhere, this requires policy reforms that include the explicit consideration of justice to address environmentally linked health disparities both within and across human and relevant non-human animal populations (Rock and Degeling 2015; Lysaght et al., 2017; Degeling et al., 2016). Data availability statement There is no data available for sharing because of restrictions imposed by research ethics approvals in both Australia and Singapore. Acknowledgements We are indebted to the collaborators on both research projects for their contributions to the development of the case scenario, survey design and intellectual insights. Specifically, on the Singapore project, we thank (in alphabetical order) Drs Michele Bailey, David Bickford, Benjamin Capps, Richard Coker and Zohar Lederman; and on the Australian project Profs Michael Ward and Andrew Wilson. Funding This work was supported by the Australian National Health and Medical Research Council under Grants 1083079 and 1102962; and Communicable Diseases-Public Health Research Grant from the Ministry of Health, Singapore under Grant MOH/CDPHRG/0011/2014. Conflict of interest statement The authors have no conflicts of interest to declare. References Adler M. , Ziglio E. ( 1996 ). Gazing into the Oracle: The Delphi Method and Its Application to Social Policy and Public Health . London : Jessica Kingsley Publishers . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Baekkeskov E. ( 2016 ). Explaining Science-Led Policy-Making: Pandemic Deaths, Epistemic Deliberation and Ideational Trajectories . Policy Sciences , 49 , 395 – 419 . Google Scholar Crossref Search ADS WorldCat Bashford A. ( 2007 ). The Age of Universal Contagion’: History, Disease and Globalization. In Medicine at the Border . New York : Springer , pp. 1 – 17 . 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From “One Medicine” to “One Health” and Systemic Approaches to Health and Well-Being . Preventive Veterinary Medicine , 101 , 148 – 156 . Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2019. Published by Oxford University Press. Available online at www.phe.oxfordjournals.org This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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