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  2022 will be better: Modelling Report

COVID-19 Pandemic Trade-offs: Version 3, September 2021

A report summarising Version 3 COVID-19 Pandemic Tradeoffs modelling, ‘2022 will be better’, accompanies this website.

This tool allows you to explore how COVID-19 policy responses (restrictions, vaccination roll-out, and border opening) impact future SARS-CoV-2 infection, hospitalisation and mortality rates, and the future probability of lockdowns. This Version 3 updates Version 2 in the following important ways:

• Including options for a higher virus reproduction rate (5, 6.5 and 8) given recent concerns about greater infectivity of Delta (e.g. CDC estimates range 5 to 9)
• Shorter duration from initially becoming infected to being infectious
• Removing previously lower chance of children being infected as seen pre-Delta; we now assume, consistent with global experience, that children as just as likely to be infected as adults
• Moving from vaccinated but infected people being 50% less likely to transmit the virus on, to a 25% reduction (range 0% to 50%) given recent evidence that the viral load in vaccinated but Delta-infected individuals is little different from unvaccinated Delta-infecteds.
• Mortality rates from Delta infections are now assumed to be two-fold higher (hospitalisation and ICU rates two-fold higher on an odds-scale) than for previous variants based on early UK and Candian results. Whilst we believe these are the best current working estimates, they are very uncertain. Therefore, users not convinced that Delta has two-fold higher virulence than previous variants may consider halving the number of deaths, hospitalisations and ICU admissions shown below. We will obviously update this important parameter as more data emerges.

These changes all mean that for any policy setting (e.g. vaccination coverage, triggers to lockdown, mask wearing) the expected number of infections (and hence morbidity and mortality), and the expected time in lockdown, is – unfortunately – worse than we estimated in Version 2 of this tool.

A co-published modelling report (PDF 9.0 MB) gives our interpretation and policy recommendations based on this Version 3.

We use the State of Victoria as the case study, but the findings can be broadly applied to other states and territories in Australia, New Zealand and other South Eastern Asian and Oceanic countries that are trying to use an elimination strategy before opening their open the borders. The findings may also be generalised further to countries that have not used an elimination strategy, but are considering how best to manage borders at varying levels of within-country immunity (be that through vaccination and / or natural infection).

This tool will allow the user to explore possible outcomes for the remainder of the current year, with increasing vaccination coverage, and then, in the year subsequent to border restrictions being lifted. By “borders open”, we mean when a substantial proportion of vaccinated in-bound travelers are allowed quarantine-free arrival – but there will always be a need for arrivals from some countries that have high infection rates, or new variants of concern, to go to formal managed quarantine (presumably at purpose-built facilities). Indeed, this Trade-offs tool can assist with deciding what that threshold might be, in directing high-risk arrivals to formal quarantine. The model allows for incursions of the virus through both leakages out of formal quarantine (4.5% per day up to opening the borders, 2.25% probability per day after opening borders), and from vaccinated but infected quarantine arrivals ‘slipping through’ at rates of 0.2, 1, or 5 per day.

You will see 216 scenarios presented in interactive tools below, for combinations of 3 in-country policy settings for when to tighten restrictions, 4 levels of vaccination coverage achieved before borders are opened, whether children (aged 5 to 17) are also vaccinated, expected vaccinated but infected arrivals after the borders open (0.2, 1 and 5 per day), and the reproductive rate of the virus (R0 of 5, 6.5 and 8).

For more information on these input variables, view our model details page.

Read the full report

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Infections

A basic measure of COVID-19 policy success is the extent to which it keeps the number of new infections manageable.

To model this, we ran 100 simulations of how the SARS-CoV-2 virus might spread through a population of 6 million people, for each scenario. The graph below shows the median (or average) daily infections across the 100 simulations of each scenario, along with a 90% uncertainty interval (the 5^th and 95^th percentiles).

Use the options to the left of (or below) the graph to explore how different scenarios affect the spread of the virus.

Lockdown restrictions

In each of the 100 simulations, the model also tracks which stage of restrictions is in place each day (Stage 1 being near-normal, up to Stage 4 with strong stay at home orders, i.e. a hard lockdown). The graph below shows the percentage prevalence of each stage of restrictions, by week, across 100 simulations of the chosen scenario.

Use the options to the left of (or below) the graph to explore how different scenarios affect the enforcement of lockdown restrictions over time.

For more detail, click below to view all 100 simulations of each scenario, coloured by restriction stage in use depending on the policy setting (elimination, tight suppression, loose suppression, and extra loose suppression).

Explore all 100 simulations

If you are interested in the details of the model, these are included on a separate page.

View modelling details

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Heatmaps of infections, hospitalisations, ICU admissions, deaths and time in lockdown

Below are two heatmaps. The first shows (depending on which option you select) the average number of daily infections, the yearly SARS-CoV-2 deaths and the yearly SARS-CoV-2 hospitalisation (for a population the size and make-up of Victoria). Deaths are estimated using pre-Delta variant fatality risk ratios from the Institute for Health Metrics and Evaluation infection, which are doubled for increased virulence of Delta. Hospitalisation and ICU admission rates are estimated using the respective rates observed throughout the Victorian second wave, doubled (on odds scale) again for increased virulence of Delta.

The second heatmap shows the proportion of time in Stage 3 (soft lockdown) and Stage 4 (hard lockdown) for all scenarios.

For both heatmaps, you can also select whether you want to see:

• the period before borders open: when an infection escaping from quarantine has a probability of 4.5% per day); or
• the period after borders open: when infections are still escaping from quarantine at a rate of 2.25% per day, and vaccinated infected arrivals occurring at the rate indicated for each scenario.

For both heatmaps, if you hover over a cell you will be shown the 90% simulation interval, due to both stochastic uncertainty and input parameter uncertainty (e.g. about vaccination effectiveness).