C.2 Generic or ‘Agent X’ pandemic scenarios
How a future pandemic will play out in Aotearoa New Zealand is a function of three factors:
- Pathogen and host: Specifically, the virulence and infectiousness of the pandemic agent (likely a virus), and the immunological and general susceptibility (such as age and co-morbidities) of the people it infects.
- Response: That is, the actions we take collectively and individually to respond, selecting from the ‘tools’ we have in the ‘toolbox’. (This in turn is influenced by what pandemic preparation has occurred in the past.) The response options are wide-ranging, including: public health and social measures (PHSMs), ranging from voluntary physical distancing to lockdowns; vaccines – both the quality of what is in the vial, and when and how we deploy or roll it out in society; treatments that might be generic for any serious viral illness (such as ICU care) through to bespoke pharmaceuticals developed in response to the new pandemic agent; testing including the actual test itself through to how it is deployed and used; contact tracing; isolation and quarantine; and border controls.
- Contextual factors: Social cohesion and trust (in government, science, each other) are important preconditions for a coordinated response that requires solidarity or kotahitanga to execute (such as an elimination strategy that occasionally requires working from home or even lockdowns).
The range of possibilities under each of these three domains is large. It is not possible to conceptualise and work through all possible scenarios. However, the backbone of future pandemic preparedness will involve developing scenarios that in turn inform preparedness activities. We also recommend that modelling – including economic and social inputs and impacts – of many scenarios is performed to help guide that process going forward. A combined WHO, OECD and World Bank report has eloquently made the case for integrated epidemiologic and economic modelling capacity to be built before the next pandemic.2
But for this appendix, we flesh out a handful of scenarios – the objectives being:
- To highlight that the next pandemic will likely be different from COVID-19.
- To highlight that the impact of the next pandemic will likely depend on what preparation is done in advance.
- To demonstrate how outlining scenarios can assist prioritisation of preparedness activities.
For our scenario thinking, we consider component scenarios as follows:
Figure 1: Scenario Components
| 1. Pathogen characteristics: |
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a) four combinations of infectiousness (measured by R0i) and visibility for case detection, contact tracing, isolation and quarantineii (i) low infectiousness (R0 = 2.0) and high visibility (ii) moderate infectiousness (R0 = 3.0) and low visibilityiii (iii) high infectiousness (R0 = 6.0) and high visibility (iv) high infectiousness (R0 = 6.0) and low visibility b) virulence of infection fatality risk (IFRiv; low=0.5 percent/high=7.5 percentv) |
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= 8 scenarios |
| 2. Societal preparation: good versus poorvi |
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= 2 scenarios |
| 3. Strategy: |
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a) Immediate: There will be an initial and urgent decision required as to whether to use an exclusion strategy and minimise the possibility of the pathogen entering the country at all (or at least delaying its arrival). The exclusion strategy would be used with a pathogen with obvious major potential health and social impact due to its combination of infectiousness, virulence, visibility – and the societal preparedness and response capacity in place. It would also be used when there was a high degree of uncertainty requiring time to rule out the likelihood of the pandemic pathogen being ‘bad’ (in other words, applying the precautionary principle). b) If the pathogen is within New Zealand: If an exclusion strategy is not taken, or it is taken then a pivot to more open borders is pursued with the inevitability of onshore transmission occurring, or exclusion fails, despite rigorous international border quarantine, with incursion of the pathogen into New Zealand, the strategy choices are broadly two-fold: (i) Elimination or aggressive suppression (keep stamping it out, aiming for zero within-country transmission most of the time; may even revert to exclusion strategy, emphasising that strategies sit on a spectrum); versus (ii) Loose suppression of mitigation (that is, let the pathogen wash through until something like herd immunity is achieved, using ‘flattening the curve’ activities if peak healthcare demand exceeds system capacity). |
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= 2 scenarios |
| 4. Vaccine: |
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a) Good scenario of vaccines with high vaccine effectiveness (including against transmission), rapid development and rollout (for example, starting within six months of the pathogen being identified and completed within another six months), and high vaccine uptake (for example, 90 percent or more of the eligible population); versus b) Bad scenario of vaccines with only moderate vaccine effectiveness (protection) against death (for example, a 90 percent reduction in risk) and hospitalisation (for example, an 80 percent reduction in risk) and poor vaccine effectiveness against transmission (for example, a 30 percent reduction in risk of vaccinated person being infected, and a 50 percent reduction in risk of a vaccinated person with infection passing it on to others – meaning a hypothetical 1 – ((1–30 percent) × (1–50 percent)) = 65 percent reduction in transmission in society if everyone was vaccinated), and only 60 percent vaccine uptake in the eligible population.vii |
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= 2 scenarios |
Another key consideration is uncertainty – which will likely be high initially. Uncertainty may be explicitly included in frameworks for deciding on the optimal pandemic strategy (see, for example, Kvalsvig and Baker, 20213). There is also likely to be uncertainly in relation to the initial decision about whether to immediately impose strict national border restrictions and keep the pathogen out (exclusion strategy above). We refer to these dimensions occasionally, but do not explicitly include them in our framework.
These component scenarios come together as 64 different combinations (8×2×2×2). This is far too many to expound in depth, but we will select from them to demonstrate possible futures.
Next, the pathogen characteristics are considered in more detail. Table 1 shows the expected deaths in an unmitigated pandemic in a population of 6 million. The lower bound is given, assuming the proportion infected is determined by the herd immunity threshold (R0 – 1)/R0), which will require strong controls as the pandemic progresses to ensure infection rates are kept low as the herd immunity threshold is approached. The upper bound is that for a completely unmitigated epidemic, whereby there are many people infected when the population reaches the level of infection required to achieve herd immunity (meaning there is still some way to go before wave of infection fades away). Note that these numbers are theoretical, assuming homogenous mixing and no societal or individual measures to reduce the risk of transmission. In reality this is an unlikely situation since – even in the absence of a coordinated government response – people are likely to take voluntary measures to ‘shield’ the vulnerable (such as elderly people avoiding social gatherings and people wearing masks), meaning death counts would likely be lower than those projected here.
Table 1 also shows in parentheses the likely time-specific occurrence (return period) for such a pandemic, using the work of Madhav et al (2023)4 and assumptions as per the footnotes to Table 1.
Table 1: Excess deaths by pandemic scenario in an unmitigated pandemic with no societal or behavioural change † for a country of 6 million (return period in backets derived from Madhav et al, 2023 ‡)
| Infectiousness | Virulence | |
|---|---|---|
| Low, IFR = 0.5% | High, IFR = 7.5% | |
| Low, R0 = 2 | 15,000 to 27,000£ (1 in 25 yr) | 225,000 to 400,000£ (1 in 100 yr to 1 in 200 yr) |
| Moderate, R0 = 3 | 20,000 to 28,000£ (1 in 25 yr) | 300,000 to 425,000£ (1 in 100 yr to 1 in 200 yr) |
| High, R0 = 6 | 25,000 to 30,000£ (1 in 25 yr) | 375,000 to 450,000£ (1 in 200 yr) |
† For illustrative purposes (in reality there will be behavioural changes, although the extent is unclear)
‡ Madhav et al compiled a historical record of pandemics. They created an approximate excess death rate corresponding to how often a pandemic of that severity occurred. Those excess death rates are ‘observed’ and therefore mitigated to some extent. Further, and assuming the IFR is higher among older ages, then the ‘completely unmitigated’ excess death rate in contemporary society would be more than in historical records due to older populations. These factors are allowed for in the (very) approximate assigning of 1 in 25-year and 1 in 100-year to 1 in 200-year pandemics.
£ Lower bound is for the herd immunity threshold (HIT) of infection ((1 – R0)/R0) multiplied by the IFR by 6 million. For the proportion of the population to be infected to equal the HIT requires homogenous mixing and (critically) that the epidemic is controlled so that it approaches the HIT with low infection rates – that is, there would need to be considerable flattening of the curve and mitigation activities. The upper bound is that for an unmitigated epidemic (‘let it rip’, no dampening of transmission whatsoever) that means the epidemic still has many people infected at the HIT, and whilst each infected will pass it on (on average) to less than one other person, there is still much more momentum to run out. Using formulas derived for a SIR model5 (Susceptible, Infectious and Removed individuals) model, an unmitigated epidemic for a pathogen with R0 of 2.0 will see 79.7 percent of the population infected (c.f. HIT = 50 percent), and R0 6.0 will see 99.7 percent infected (c.f. HIT = 83.3 percent).
Rather soberingly, the current H5N1 strain of avian influenza (‘bird ‘flu’) has a recorded case fatality rate (CSR) of about 50 percent among people infected via animal-to-human transmission. (The virus has not yet mutated to allow human-to-human transmission, which could potentially precipitate another pandemic.) Based on experience with previous influenza viruses, we assume that – should human-to-human transmission occur, the infection fatality risk (IFR) for H5N1 will be much less than the case fatality rate (CFR).viii This is likely for two reasons: firstly, many cases of H5N1 influenza infection from animal-to-human transmission are likely to have remained undetected due to mild or absent symptoms (that is, IFR < CFR); and secondly, if mutations occur to allow human-to-human transmission, the virus is likely to simultaneously become less virulent (although this second assumption is not certain). Thus, our worst case of an IFR of 7.5 percent should not be discounted as impossible.
i The R0 is the basic reproductive number – or the number of people each infected person infects on average, early in the outbreak when there is no immunity among the population. It is also a social construct, in that the R0 depends on contact patterns and facilitation of transmission in the society. For the purposes of these scenarios, we assume this R0 applies to a pathogen ‘dropped into New Zealand in 2019’ before it was detected. If in the future people congregate in buildings with much improved ventilation (and possibly filtration), the R0 of a given pathogen will be reduced. Likewise, the R0 will be less in the future if people work and study more from home.
ii High visibility for contact tracing would be a long incubation period (allowing more time for people to quarantine effectively and be contact traced); little if any pre-symptomatic infectious period (meaning people do not circulate for long in the community before self-isolating when they become symptomatic – assuming they comply); and few if any people getting asymptomatic infection (yet still being infectious to others). Low visibility is the converse. For additional discussion and consideration of ‘visibility’ of a pandemic pathogen, including social factors that influence visibility and detectability, see J.M. McCaw, K. Glass, G.N. Mercer, and J. McVernon, ‘Pandemic controllability: a concept to guide a proportionate and flexible operational response to future influenza pandemics’, Journal of Public Health 36, no. 1 (3 June 2013), 5-12, https://doi.org/10.1093/pubmed/fdt058, https://academic.oup.com/jpubhealth/article/36/1/5/1572791.
iii It seems unlikely for a low infectiousness virus (R0 = 2.0) to also be low visibility, so we set a moderate infectiousness (R0 = 3.0) as the ‘best’ scenario with a low visibility pathogen.
iv The infection fatality risk (IFR) is the proportion of people infected who die (in the absence of more than supportive care, before any specific treatments for the pandemic pathogen are available). It is less than the case fatality rate (CFR), which has symptomatic and detected people as the denominator. Thus, if two thirds of people are symptomatic and classified as a case (for example, because they are captured by surveillance systems), then a 10 percent IFR equates to a 15 percent CFR. For these scenarios, we assume the IFR and CFR vary by age, being greater among older age groups. The 1918 influenza epidemic had a notably high CFR among young adults, probably due to some immune memory from an influenza virus that circulated in the late 1800s and secondary bacterial infection on top of the 1918 influenza virus that actually resulted in most of the deaths. A high CFR among young adults relative to older adults in a future pandemic is possible but seems unlikely.
v The IFR will almost certainly vary by age, perhaps greater than 100 fold. But here we just consider the ‘crude’ IFR across all ages combined.
vi A well-prepared society might have these features: improved ventilation and filtration of public buildings (especially healthcare settings), leading to a 5 to 10 percent reduction in the R0 of any respiratory-borne pathogen; be digitally enabled allowing easy work and study from home; deploy effective digitally enhanced contact tracing and surveillance; have a strong public health workforce that is able to surge for contact tracing and supporting cases and contacts, with excellent connections into and collaborations with diverse communities; have strong health systems that are able to surge to meet community, secondary and tertiary care demands in a pandemic; have strong testing strategy and capacity that can be surged rapidly; have strong IT systems in health, to provide for situational awareness surveillance, and prioritisation of activities; maintain large well-managed stockpiles of personal protective equipment (PPE) and medicines; have onshore manufacturing capacity for PPE and masks that can be surged; have a comprehensive quarantine system that can surge to provide a mix of strict facilities through to supported home quarantine; have predetermined governance and decision-making structures, supported by strong legislation, policy workforce capacity and with engagement and liaison arrangements with Māori and other community groups, that can all be surged in a coordinated manner; have strong wage and business support systems that can be turned on and off rapidly, and targeted as required; maintain strong border systems and workforce that can rapidly move up levels of stringency for international arrivals; and have strong social support and welfare sector that can reprioritise and surge to support people.
vii A 60 percent uptake, with the vaccine effectiveness against transmission as stated in this paragraph, would lead to a 60 percent × 65 percent = 39 percent ≈ 40 percent (assuming homogeneous population mixing, and 60 percent uptake is for all ages as the denominator). Assuming no waning, and no immune escape from new variants, the effect of this vaccine scenario alone (with no other changes in society) would be enough to achieve an effective reproductive rate (Reff) of 1.0 for a pathogen with an R0 of 1.67. It would still be helpful in combination with other measures to reduce transmission for pathogens with an R0 > 1.67.
viii See footnote iv for an explanation of IFR and CFR.