23 Sep 2021
Modelling definitions and Frequently Asked Questions
Acronyms
- TTIQ – Test, trace, isolate, quarantine
- PHSM – Public health and social measures
- TP – Transmission potential
Definitions
- Baseline PHSM – No stay-at-home-orders, low density requirements, no retail restrictions, schools open.
- Low PHSM – As per baseline PHSM, but with capacity limits on recreational activities, limits on retail group sizes and restrictions on workplace capacity.
- Medium PHSM – Stay-at-home except for work, study and essential purposes, retail and cafes/restaurants open subject to density restrictions, working from home if possible with density restrictions in workplaces. Indoor recreational venues closed, small numbers of household visitors allowed. Closed or graduated return to schools.
- High PHSM – As per medium PHSM but with no recreational gatherings, movement distance limits, no household visitors, two person rule for exercise and curfew.
- Partial TTIQ – The observed reduction in transmission (43%) resulting from test-trace-isolate-quarantine responses at the height of the Victorian ‘second wave’ when case numbers were in the hundreds per day and the system was under strain resulting in delays.
- Optimal TTIQ – The observed average reduction in transmission (54%) resulting from test-trace-isolate-quarantine responses implemented in NSW over a period of several months including the Christmas/New Year outbreak. Responses remained timely over variable case loads.
- Seeding – The initial number of daily cases present in the population at a given vaccination threshold.
- Low seeding – ranging from 10-100 cases per day
- Medium seeding – ranging from 300-1000 cases per day
- High seeding – ranging from 1,000-4,500 cases per day
- Small area effects – The change in transmission caused by relatively small groups, such as geographic or cultural differences, that are not explicitly modelled in this work.
Frequently Asked Questions
Q. Are the PHSMs in the table those recommended by the Doherty Institute?
A. The PHSMs described in the paper were implemented in NSW and Victoria at different times during the COVID-19 epidemic. We were able to estimate the impact of these measures on transmission potential in the population at those times. They are broadly reflective of different degrees of measures that have variously implemented around the country. Treasury was able to estimate the costs of these ‘bundles’ of these interventions on the economy. The Doherty Institute-led modelling consortium has no position on what specific components of PHSMs should be implemented based on this work.
Q. The estimates of TTIQ impact come from a time before the Delta variant. Are they still relevant?
A. TTIQ works by ‘removing’ infected people from the population so that they can’t physically come into contact with others to spread infection. Isolating people who already have symptoms is only partially effective because people with COVID-19 are infectious for a couple of days before they become unwell. Quarantine is the most effective part of this measure because it ‘removes’ people who’ve been exposed to a case before they even know whether they’re infected. At best, it can stop people being infectious in the community at all. Our model considers the impact of TTIQ as a proportional reduction in people’s infectiousness related to the timeliness of case and contact finding. We can apply this proportional ‘discount’ to the infectiousness of any of the COVID strains we’ve seen in Australia so far.
Q. Does the modelling make any assumptions about people's willingness to comply with isolation rules once they are fully vaccinated? Is this issue relevant to the projections of partial/optimal effectiveness of TTIQ? If so, how?
A. Compliance with isolation rules is assumed to be the same for vaccinated and unvaccinated individuals. If compliance for vaccinated individuals were less, then TTIQ will also be less effective, and transmission potential will be higher than modelled. However, because vaccinated individuals are less likely to spread infection, the impact will not be as great as compliance levels among those unvaccinated.
Q. Do the models assume vaccination will continue past 80 per cent? If so, what rate do they assume it will rise to over the 180-day time frame?
A. Yes, vaccination continues past 80 per cent in these models. The final vaccination coverage was determined by Quantium, and is approximately 89.8% in the eligible population.
Q. In this tweet here, the Doherty Institute states: “In the COVID-19 modelling, opening up at 70% vaccine coverage of the adult population with partial public health measures, we predict 385,983 symptomatic cases and 1,457 deaths over six months.” Are these numbers a midpoint of the 20-30 simulations run for the scenario?
A. Each of the 20-30 simulation runs are input into the clinical pathways model, and the clinical pathways model is run 200 times for each scenario (page 15, main technical report). This results in 400-600 clinical outcomes. The reported numbers in the tables are the median estimates from the clinical pathways model.
Q. Does the model for these numbers assume that baseline PHSM continues for the duration of the 180 days without changing?
A. The model assumes that baseline PHSM (or whatever level of PHSM is used) continues for the duration of the simulation. No other intervention, proactive or reactive, is applied.
Q. How likely is it that the outcomes on the fringes of the graphs (the line edges) would come to pass? Should they be considered as likely as the shaded middle, or less likely?
A. In all figures, dark banding represents the central 50% credible interval (i.e. from the 25th to 75th centile) for simulations. The light banding represents the central 90% credible interval (i.e. from the 5th to 95th centile) for simulations.
There is a 5% chance that the outcome is above the top band and a 5% chance that it is below the bottom band on any given day.
The bands do not represent a single trajectory: some might peak earlier or later, slightly higher or lower. They are designed to give an idea of the possible variability in outcomes, rather than an exact prediction of the future.
Q. What about basal level of immunity i.e. prior infection? Do the modellers in the UK think this is important for their setting? Is Australia very different because we have so few infected?
A. The only immunity in this model is vaccine or infection-induced. Given the relatively little amount of community transmission, no prior infection-induced immunity is assumed. This is clearly different to a setting like the UK, where infection has been spreading for some time.
To accurately assess the level of prior infection-induced immunity in Australia, we would need studies such as serosurveys, which can give a snapshot of the proportion of individuals protected at any point in time.
Q. Why do the models only look at 180 days?
A. There are two reasons for confining reporting of scenarios to the 180 day timeframe.
- Future uncertainty about variants, waning immunity etc. mean that even a six month window is ‘long’ in COVID-time so we are uncomfortable projecting any longer.
- Treasury analyses considered economic impacts over comparable time windows.
Q. Have you included in your models newer approaches to testing e.g. antigen tests?
A. No, these models considered only current testing approaches, i.e. PCR tests. The impact of newer testing approaches will depend heavily on their sensitivity, specificity and policy around their usage.
Q. Do your models consider the impact of new treatments for COVID-19?
A. No, our models do not consider the potential impacts of current or emerging therapies for COVID-19 on health outcomes. Such treatments may further reduce severe disease in the future.
Q. Does the modelling take into account the emerging evidence regarding the severity of the Delta variant?
A. As evidence continues to emerge regarding the Delta variant, it has become clear that our initial assumptions on transmissibility and vaccine effectiveness remain robust, however it also suggests Delta is more severe than was assumed in our modelling. This new evidence further endorses our recommendations for a multi-pronged approach to disease control to keep case numbers as low as possible so the health system is able to manage anticipated clinical loads. It therefore does not change our conclusions in any way.
As stated in our original report, ongoing situational assessment of measured transmission potential and circulating SARS-CoV-2 variants in the Australian population over the coming months will allow benchmarking of these hypothetical scenarios to guide real time policy decision making about the transition to Phase B of the National Plan.
We provide such assessments to the Australian Health Protection Principal Committee every week, ensuring continuous updating of advice based on emerging local and international evidence to inform health responses.