Last updated: 2021-12-02 14:04 NZDT
Updates daily near 1pm NZDT

Progression of the COVID outbreak in New Zealand

←Back to per-country plots

New: Progression of the COVID outbreak in Victoria

New: Progression of the COVID outbreak in the Australian Capital Territory

See also: Progression of the COVID outbreak in New South Wales

See also: Australian vaccination rollout

See also: The road to a COVID-free Victoria (old 2020 second wave plots)

Contents

  • Reff estimate from all cases
  • Projected effect of vaccine rollout
  • Animated projections over time
  • Reff in Auckland vs rest of NZ
  • Disclaimer on trends
  • Methodology
  • Data sources and contact
  • Reff estimate from all cases

    New Zealand is currently experiencing an outbreak of COVID, due to the delta variant. On August 17th, Auckland entered lockdown restrictions under Alert Level 4. Since then restrictions have been loosened to Alert Level 3, and further loosened under "Phase 1" of a reopening plan.

    How have restrictions in New Zealand affected the spread of the virus? The below plot shows how the effective reproduction number of the virus, Reff has changed over time in New Zealand, as well as how the daily cases have changed over time. A trendline shows the approximate trajectory daily case numbers would follow, were the reproduction number of the virus to remain at its current level.

    The same plot is shown twice, the first with cases on a linear scale, and the second on a log scale—the latter showing how consistently the caseload has followed exponential growth (which forms a straight line on a log scale).

    Projected effect of vaccine rollout

    The trendlines in the above plots simply project forward case numbers assuming that Reff remains at its current estimated value. What about the effect of vaccines? Below is a plot with the projected trend taking into account an estimated reduction of spread due to vaccination levels increasing in New Zealand through the remainder of the year. This projection is a stochastic SIR model, and thus also takes into account immunity due to infections, which would decrease transmission once a non-neglibible fraction of the population has been infected.

    The rate of vaccination assumed in the model 0.25 doses per 100 population per day, up to a maximum of 80% of the population. See below for plots of how these assumed rates compare to the vaccination rollout in New Zealand so far. The vaccines are assumed to reduce Reff by 40% per dose per capita—that is, overall spread is reduced by 40% for a partly-vaccinated individual and 80% for a fully vaccinated individual.

    This projection shows what might be possible if all other factors affecting Reff—such as restrictions—are held constant. If New Zealand eases restrictions, however, then Reff will likely increase and the effect of vaccines in reducing spread will be delayed compared to this projection. On the other hand, if contact tracing is successful in decreasing Reff, case numbers may decline faster than this projection.

    The same plot is shown twice, the first with cases on a linear scale, and the second on a log scale.

    Below are plots of the vaccination rollout in New Zealand to date, in terms of daily and cumulative doses per 100 population, with the assumed future rate used for the above projections also shown.

    Animated projections over time

    How have the above projections changed over time? Below are animated versions of the above projection, one with cases on a linear scale, and one with cases on a log scale, run on old data to show how the projections have changed over time. Note that these are not 100% identical to the projections actually made on previous days, as there have been some slight methodology changes - but they should be very close.

    Reff in Auckland vs rest of NZ

    Below are two plots showing case numbers and an estimated Reff value in Auckland (the Auckland, Counties Manukau, and Waitematā DHBs), and the rest of New Zealand excluding Auckland.

    The Reff estimate outside of Auckland should be interpreted with caution. Because case growth outside of Auckland is a due to a mix of local transmission and an increase in cases from Auckland, attributing it all to local transmission (as this Reff estimate does) may result in an overestimate. This will likely become less true as time goes on and local transmission becomes the dominant source of case growth.

    Note: case numbers on these plots are shown on a log scale.

    Disclaimer on trends

    The plotted trendlines are simple extrapolations of what will happen if Reff remains at its current value. This does not take into account that things are in a state of flux. As restrictions take effect, the virus should have fewer opportunities for spread, and Reff will decrease. If restrictions are eased, it may increase. Contact tracing may suppress spread to a greater or lesser degree over time. The above plots specifically showing the effect of vaccines do take into account a reduction in Reff as vaccination coverage increases, but ignore any other possible future changes in Reff.

    Furthermore, when case numbers are small, the random chance of how many people each infected person subsequently infects can cause estimates of Reff to vary randomly in time. As such the projection should be taken with a grain of salt—it is merely an indication of the trend as it is right now.

    Methodology

    Padding, smoothing, and calculating Reff

    Before calculating Reff, the daily case numbers are padded on the right and smoothed. A symmetric smoothing method is used, which is why padding on the right is necessary before smoothing.

    The padding is an extrapolation based on a fit to the most recent 14 days of the 5-day moving average of daily case numbers n(t), with the following fit function:

    n(t) = A exp[ k( t-ttoday) + 1/2 (t-ttoday )2 ]

    where A and k are the fit parameters for the current daily caseload and exponential growth rate, and is the current expected rate of change of the exponential growth rate due to vaccines:

    = 1 / 𝜏 (svax(ttoday) / svax(tyesterday) - 1)

    where 𝜏 = 5 days is the approximate mean generation time of the virus, and svax(ttoday) and svax(tyesterday) are the fraction of the population not protected by vaccination from infection today and yesterday. See vaccination assumptions below for how svax(t) is calculated from vaccination numbers.

    After padding, daily case numbers are smoothed with 4-day Gaussian smoothing:

    nsmoothed(t) = n(t) ∗ (2𝜋Ts2)-1/2 exp(-t2 / 2Ts2)

    where Ts = 4 days and ∗ is the convolution operation.

    Reff is then calculated for each day as:

    Reff(ti) = (nsmoothed(ti) / nsmoothed(ti-1))𝜏.

    The uncertainty in Reff has contributions from the uncertainty in the above-mentioned fit, as well as uncertainty in daily case numbers. The latter is considered to be Poisson noise scaled by a constant, chosen so as to make the reduced chi squared between raw and smoothed daily case numbers equal to 1.0.

    Trend based on Reff only

    The plot(s) with a simple extrapolation of daily case numbers, without taking into account the expected effects of vaccines, is exponential growth/decay using the most recent value of Reff and its uncertainty range:

    nextrap (ti) = nsmoothed(ttoday) Reff(ttoday) (ti - ttoday) / 𝜏

    Projected effect of vaccines

    To project the effect of vaccines on the progression of daily cases in the future, I use a stochastic SIR model. The model is run 1000 times to project daily case numbers, and the median result and 68% range of outcomes shown in the projection.

    In each run of the model, a random value for each of Reff(ttoday) and nsmoothed(ttoday) is drawn from a multivariate normal distribution, using the mean values and covariance of the estimates of the two parameters.

    The initial infectious population is taken to be:

    i(ttoday) = 𝜏 nsmoothed(ttoday)

    The initial recovered population r(ttoday) is taken to be the cumulative number of cases so far in the outbreak, minus the currently infectious population.

    Each day, the fraction of the population susceptible to infection s(t) is taken to be

    s(t) = (1 - svax(t)) [1 - (r(t) + i(t)) / N]

    where svax(t) is the fraction of the population not protected from infection by vaccines (see See below for how this is calculated), and N is the total population.

    Assuming that Reff is proportional to the susceptible fraction of the population, we can estimate what the reproduction number of the virus would be in the absence of any vaccines or immunity from infection:

    Rnaive = Reff(ttoday) / s(ttoday)

    And compute an Reff for each future day based on the susceptible fraction of the population that day:

    Reff(t) = s(t) Rnaive

    Each day of the model, the infectious population is increased by the number of new infections, and decreased by the number of new recoveries. The recovered population is increased by the number of new recoveries.

    The number of new infections each day is drawn from a Poission distribution with mean equal to i(t) Reff(t) / 𝜏.

    The number of new recoveries each days is drawn from a binomial distribution with number of trials equal to the infectious population i(t), and with probability 1 / 𝜏.

    Vaccination assumptions

    To model vaccines taking effect after a delay, daily vaccine dose numbers v(t) are convolved with a Gaussian function offset 1.5 weeks in the future, with standard deviation 0.5 weeks:

    veffective(t) = v(t) ∗ (2𝜋σ2)-1/2 exp(-(t - μ)2 / 2σ2)

    where μ = 10.5 days, σ = 3.5 days, and ∗ is the convolution operation. This causes the effect of a vaccine dose to over the course of the second week after it is adminsitered, reaching nearly full effectiveness approximately two weeks after administration.

    I assume that one dose of any vaccine reduces spread by 40%, and two doses reduce spread by 80%. The proportion of the population not protected by the vaccine against infection is then:

    svax(ti) = 1 - 0.4 × d(ti)

    where d(ti) is the cumulative number of doses per capita on each day.

    Data sources and contact

    Source for case numbers: New Zealand Ministry of Health

    Plots/analysis by Chris Billington. Contact: chrisjbillington [at] gmail [dot] com

    Python script for producing the plots can be found at GitHub.