Ebola

In keeping with global pandemic response strategies, underwritten in WHO guidance, responses to Ebola outbreaks have been rooted in strategies of ‘control at source’ through ‘containment’.30 Mathematical models have accentuated containment through isolation, quarantine and safe burial.17 18 For instance, models projected the isolation of at least 70% of people with Ebola within 3 days of their becoming symptomatic as a critical threshold in reducing R₀ to 131 (the point at which the basic reproduction number, R₀, indicates the force of transmission potential is neither increasing or decreasing). Yet, attempts to implement containment have revealed the basis of such assumptions as detached from local cultural logics and concerns.32 33 Far from smooth, the implementation of containment efforts entangled as sites of miscommunication, mistrust, disrespect and symbolic violence, fuelling resistance in towns and villages.32–35 Efforts to separate caregivers from their dead and to conduct burials through cremation met with particular resistance.32 36 Household quarantine, and isolating people at risk to holding centres, inflicted profound social and economic harms in some places, including deaths linked to reduced access to food and medicines.37

Pandemic models tend to assume an undifferentiated smooth implementation of containment interventions, making little or no incorporation of ‘setting’.7 19 30 Yet, Ebola responses vary from place to place as an effect of how public authority in relation to care and burials emerge through negotiations locally.36 Few models have incorporated the transmission impacts of contacts with the deceased,20 a practice embedded in cultural logics of care and dignity.37 38 Projections of transmission have overlooked local variation in the composition and dynamics of households.39 Case projections have been overestimated because community-based protective actions have been underestimated.36 Projections of containment based on hospital isolations, for example, led to large investments in upscaling hospital beds when these were not required by the time they were built given community responses.40 At the same time, global pandemic preparedness models emphasised investing in vaccines which only materialised at the tail end of epidemics, in some cases diverting investment from hospital care.41 Taken together, there is ‘social science intelligence’42 that is ‘rarely integrated into supremely important epidemiological projections’,37 including because it is ‘declared too difficult to model’.37

Yet, attuning models to their contexts is not simply a matter of better incorporating local data capturing sociopolitical dynamics into the calculations of models. It also requires critically reflecting on how models perform as evidence-making interventions.4 Efforts to quantify elements of ‘social context’ into models, for instance, can enact an ‘injustice’ when reproducing overly simplified and fixed interpretations of ‘context’, ‘culture’, ‘beliefs’ and ‘causality’.43 Agusto et al attempted to incorporate the effects of ‘traditional beliefs’ into modelled projections.44 Here, a composite parameter modifies transmission projections by accounting for beliefs that ‘there is actually no such thing as Ebola’, that ‘Ebola is Government propaganda’, that there is a ‘fear of being quarantined’, and that there is resistance to ‘allowing their loved ones, who have died of Ebola, to be cremated’. The model alters the ‘strength’ of the composite measure of ‘belief system’ from 1.5 to 1.2 to 1 (unity) in low, moderate and high effectiveness scenarios of infection control in the 2014 Guinea outbreak to conclude that R₀ enters the range of 1.15–2.05 in the presence of ‘detrimental traditional belief systems and customs’.44 A conclusion is ‘educating the populace to abandon’ such traditions. The model seeks to calculate, into a single measure, practices which are entangled in complex relations of trust, risk, respect, affect and care. A globalising narrative of ‘blame’ is reproduced through the calculation of transmission as a ‘problem’ of ‘traditional beliefs’ in need of correction, while simultaneously overlooking how local authorities might come together under the radar of official authorities (such as a paramount chief, staff from a Ministry of Health) to care for loved ones in creative and effective ways.36

We see then, how the political life and governing potential of models are afforded by their attachments in social relations. The material effects generated by US Centers for Disease Control (CDC) projections for the West Africa epidemic offer an example. In September 2014, the CDC estimated that 1.4 million people would die of Ebola in Liberia and Sierra Leone by January 2015.45 While the observed fatalities were much lower, at around 11 310, the projections served to mobilise over US$3.5 billion in humanitarian assistance, 150% more than the combined value of annual government budgets of the affected countries.46 The projections helped enact United Nations Security Council resolutions that declared the epidemics as threats to national peace and security, which in turn fostered a militarised approach to infection control, including through enforced containments and lockdowns.36 Ethnographic research has traced how local responses to Ebola in Sierra Leone adapted to the atmosphere of enforcement, revealing an alternative emergent ‘people’s science’ in the Ebola response.36 Models materialise pandemic narratives, generating political effects which are navigated locally.

This reminds us that scientific calculus is never separate to, but a constitutive element in the making of, context.47 Richardson, for instance, has traced how epidemiological measures in the Ebola epidemic of the Democratic Republic of Congo (DRC) perpetuated structural inequalities by making absent their global power and colonial relations.43 48 The globalising narrative that people mistrusted and avoided internationally supported interventions because ‘they did not believe that Ebola was real’ is traced to epidemiological research published in the Lancet Infectious Diseases,49 which was then translated locally, including through media stories, among people with Ebola to reinforce the sense that transmission resulted from their ‘false beliefs’. The circulating narrative of ‘evidence-based’ mistrust, with its particular causal logic of individual responsibility, silenced alternative narratives of the historical and political hinterland of DRC which materialised mistrust as a matter of postcolonial concern. We have then, different ‘models’ of emergent causation, with different cultural and epidemic logics, which a critical social science helps to notice. The implication is that models can be done differently, so that they attune as matters of becoming. In the case of DRC, for instance, models can adapt emergent causations of mistrust to incorporate measures of structural effect linked to global health, corporate and institutional actors and not only affected individuals.43 In an adaptive science approach, the model becomes a speculative intervention of ‘epistemic reconstitution’43; that is, an evidence-making event which generates an ontological change.4

H5N1 influenza

Influenza outbreaks further illustrate that mathematical models are not mere devices of correspondence in relation to contexts, be these local or global, but are modes of enactment which perform a pandemic in context. Anthropological studies of the mathematical models14 which shaped WHO responses to the H5N1 avian influenza outbreak, which emerged in Thailand in 2003, have noted various ‘questionable assumptions’ due to ‘limited data’ which led to massive overestimates of the antiviral pharmaceuticals needed for global stockpiling.30 The heterogeneous data inputs combined in these models included, for instance, age-specific attack rates ‘from 1957 data in Sheffield, UK’ and ‘incubation times from a study of infection on an aeroplane’.30 The models assumed that households were ‘randomly distributed’, thereby ‘ignoring any social dynamics in rural village settings’, and introduced ‘no changes in behaviour as the pandemic accelerated’, while also assuming that population and quarantine restrictions ‘would work smoothly’.30 Although detached from local implementation contexts, the model was presented as ‘valid’ for ‘Southeast Asia’, with ‘the potential to prevent millions of deaths’.14 The model attaches in a network of practices to enact a global narrative of pandemic threat of great magnitude, where the ‘costs of failure’ are projected as ‘so catastrophic’ that it is ‘mperative to ensure that containment is given the best possible chance of success’.14

While the model parameter inputs were relative unknowns at the time, alternative models in Thailand produced very different case projections with much smaller estimates of antiviral pharmaceuticals needed for stockpiling.15 These alternative models, however, had less traction with global agencies coordinating the pandemic response.30 Focused instead on the local particularities of farming systems and trade markets, as well as differentiating risk in rural populations, these models emerged more slowly on the basis of empirical observations, generated different logics and assumptions, and produced more modest recommendations of lesser global policy impact.30 We have, as in the Ebola case above, different models being enacted, each located differently, and each projecting infection control in relation to their emergent situated matters of concern. Our point here is that models attach in their moments and contexts of production—be this in relation to global policy narratives, scientific method, causal logics or local concerns—and this materialises how their outputs translate (indeed, attach or detach) when implemented locally.

As it turned out, in the case of H5N1, there was to be no ‘pandemic’ as feared, and massive stockpiles of antiviral pharmaceuticals went largely unused. Yet, our point is that models enact pandemics even before their arrival is ‘known’. Our example here draws on an ethnographic analysis of pandemic preparedness in Israel.50 Working with different pandemic outcomes projected by the higher (45 000 deaths predicted) and lower (6000 deaths predicted) bounds of 1918 and 1957 past pandemics, the Ministry of Health of Israel ordered, in 2007, a policy of ‘full preparedness’ based on an intermediate ‘attribution scenario’ (assuming 30 000 deaths).50 Importantly, such modelled scenarios are ‘treated as real’,50 and become materialised in the present through policy and other actions. Projections actualise pandemics in the here-and-now, even if the pandemic event is yet-to-come.7 Pandemic models thus work as performative devices to ‘make-up’ pandemic possibility and context.

H1N1 influenza

Projections of H1N1 influenza outbreak in 2009 drew largely on ‘prepandemic’ models.16 29 The models driving policy, for instance, in the UK, assumed a basic reproductive number, R₀, in keeping with the influenza pandemic of 1918. These estimates, drawn from a time and society markedly different from the UK or Europe in 2009, were higher than those of the influenza pandemics of 1957 or 1968. Models elsewhere, for instance, in Australia, also based their key parameters on past outbreaks situated in different world contexts (in Australia’s case, from past epidemics in the USA51). National and global projections shifted frequently, as well as dramatically, in the course of the outbreak, altering estimations of critical care need.52 At the same time, modelled containment strategies (involving social distancing, isolation, quarantine and population movement restrictions) theorised an unhindered implementation, allowing no possibility for breakthrough infections or differentiated uptake.29 The modelled H1N1 pandemic is at once abstracted from local implementation contexts and made uncertain.

The uncertainty of the unfolding H1N1 pandemic reproduces the global policy response narrative of WHO, which theorises a threat of unknown massive potential, justifying a precautionary response.53 A ‘plausible uncertainty’54 of a threat of ‘disastrous proportions’55 are key elements in the performance of pandemic preparedness. In the making of H1N1 pandemic, the WHO enacted the pathogen as an unpredictable danger (‘The virus writes the rules on this one’56; ‘History has told us that these viruses are very, very, very unpredictable’57). Crucially, the uncertainty of scientific projections performed the basis for global policies of precaution. As has been noted: ‘The WHO’s narration of risk in respect to the H1N1 pandemic accommodated scientific uncertainty by resting on an appeal to (rather than an erasure of) uncertainty as the basis of risk’.53 Rather than present the science as if certain, WHO accentuated its contingency as a feature of the unknowability of pandemic threat. We see here, a pattern across viral outbreaks—from Ebola to influenza—where the pandemic model performs a qualitative scenario of generalised threat rather than predicts with local precision.7 In the end, the projections driving H1N1 pandemic preparedness, which accentuated the urgent need for the stockpiling of (partially effective) antiviral pharmaceuticals, overestimated the ‘pandemic’ and its harms, even to the extent that the WHO was accused of ‘faking’ these to boost industry profits.58 Models, as with any evidence-making intervention, are calculative machines of invention; a means to know and to act in uncertainty.

COVID-19

As with Ebola and influenza, models of COVID-19 reproduce pandemic preparedness narratives of ‘control at source’ through ‘containment’. They also draw on past pandemic models, of different viruses, in different contexts, to enact a COVID-19 pandemic in-the-now. There are shared input traces, for instance, in the assumptions driving simulations of SARS coronavirus 2 (SARS-CoV-2) today and influenza dating back to 1918.22 Even the most basic yet highly consequential parameters shaping COVID-19 projections are emergent, including estimates of infectivity, transmissibility, asymptomatic infection and incubation time, with models adapting in relation to heterogeneous data triangulated from multiple sources and contexts. An enquiry on models informing policies of COVID-19 infection control in the UK, for instance, emphasised how assumptions drawn from past pandemic models of influenza have led to underestimates of cases and deaths linked to hospitals and care homes, given how influenza virus is differentially transmitted to SARS-Cov-2, with different clinical effects, in relation to age and in response to infection controls in different settings such as schools, hospitals and care homes.59 Estimates of R₀ in relation to SARS-CoV-2 have not only shifted from original assumptions based on those of influenza and SARS, but evolve as empirical data emerges (with earlier models underestimating R₀), and crucially, are heterogeneous and multiple on account of their contexts of production.60 In the UK, for instance, estimated R₀ in care homes is quite different to that estimated in community or other settings, and yet this singular calculation is mobilised in policy, as well as captured in the popular imagination, as a population-wide metric of modelled infection control. The pandemic model, as we have seen above, performs a generalised threat of unknown yet great potential to enact a precautionary response, and models of COVID-19 are no exception. Such models are not without controversy.61 They are recognised, including by modellers, to have generated ‘very alarmist values’ as a ‘worst case scenario’ because this is ‘what policy-makers needed at that time’.59

Unlike previous pandemics, the performativity of COVID-19 models is expanded by the intensity and scale of public and media engagement with projections and metrics.62 Controversy linked to shifting national policies based on emergent modelled scenarios of ‘herd immunity’ (viral mitigation) and ‘lockdown’ (viral suppression) have enacted the mathematical model, its adaptations, and the logics and science of these, as sites of public trouble.61 There is ongoing public deliberation in some settings about trusting generalised projections made in the absence of empirical data, especially that derived from community testing initiatives.61 Sociological studies have sought to ‘follow the numbers’ of COVID-19 models as they travel in policy and public communication to investigate how projections ‘go public’ as sites of social concern.61 Here, the model is not treated as if separate from society, as if facts can be isolated from concerns, but is understood as made-to-matter in the local of the everyday. The ‘domestication’ of the mathematical model and citizen engagement in modelling science precipitated by COVID-19 potentiates the model as a site of deliberative intervention in evidence-making incorporating multiple forms of expertise.62 63

COVID-19 also indicates the urgent need to model ‘beyond the virus’. Far beyond attenuating for differentiations between multiple forms of virus (ie, moving beyond the prepandemic influenza model), the pandemic model requires local differentiation and adaptation the moment it is released. Applied as a tool for navigating emergency and novel situations, the pandemic model is always a matter of becoming, as it transforms into multiple, dynamic and iterative versions of localised adaptation. While acknowledging that ‘no public health intervention with such disruptive effects on society has been previously attempted’,22 pandemic models tend to simplify. Social distancing is obviously not a single dose or one-off ‘intervention’ but a complex of practices, made-up of multiple combination effects (closures, population mixing, isolation, quarantine) in particular settings (urban, rural, household, community, hospitals, care homes) at particular times (immediate, short and longer term, in cycles of viral peaks, troughs and rebound). The ‘social distancing complex’ is in urgent need of ethnographic unpacking. Pandemic models tend to assume smooth intervention translations as rational cost–benefit actions,14 16 29 yet risk and intervention engagements emerge as matters of negotiation, structuration and habit. While COVID-19 models tend to assume relatively undifferentiated publics engaging in a smooth roll-out of viral mitigation or suppression, empirical evidence suggests that different social distancing measures affect populations differently, including their capacity to isolate or shield, and shaped by social and material inequalities.64 The intensity and sustainability of community engagements in infection control are highly variable in population, time and space, and differ from influenza-informed assumptions.59 64 COVID-19 pandemic models have yet to treat SARS-CoV-2 and its control as a matter of dynamic adaptation located in complex evolving systems.65 The time to better attune COVID-19 models to their local contexts is now.