The Covid-19 outbreak in Victoria
The Victorian estimates are not currently being updated
The last update for Victoria was estimated on 26 October 2020, with the data available at that moment. There are too few cases (yay!) to make it worthwhile updating these estimates for now. This decision will be revisited if community transmission in Victoria increases beyond 5 cases per day.The charts below are out of date.
The chart above is based on an "if things keep on this way..." forecast of the Covid-19 outbreak in Victoria, comparing probable outcomes in two key fortnights with the targets for Melbourne proceeding to Step 2 and Step 3 of the Victorian Government's Coronavirus Reopening Roadmap. The forecast simplifies the actual targets by disregarding the Melbourne/regional distinction, and the requirement before moving to Step 3 for there to be less than 5 cases with unknown source in the past 14 days. So it should be treated as only a partial indicator, and purely indicative.
This site is not affiliated with the Victorian Government in any way, and any contradiction between what I have on this page and official advice should be assumed (until there's evidence to the contrary) to be an error on my part.
The model on which this forecast is based is shown below, focusing for this chart on just a two-week forecast period:
For each of the three time series shown, the green segment on the left shows a period of time that is substantively settled. The brown/orange segment is a "nowcast" - still heavily impacted by delays in incubation and reporting, but with at least some partial data. The final blue segment is a forecast.
The darker part of each ribbon reflects the estimated 50% credibility interval - the range within which a particular variable (infections, cases, or effective reproduction number) is believed to have a 50% chance of occurring, were the truth to be known. The wider, paler part of each ribbon is a 90% credibility interval.
The vertical pale grey bars represent confirmed cases, but with a small adjustment for test positivity (ie the proportion of tests for Covid-19 that return positive on any one day). Victoria's test positivity over the last few months is shown in this chart, which makes clear that the proportion of positive tests is still low (ie good) by international standards:
This next chart shows the impact of that positivity correction:
The modelled values for positivity-adjusted cases, infections, and effective reproduction number can be downloaded as a CSV.
The Covid-19 outbreak in New South Wales
The NSW estimates are not currently being updated
The last update for NSW was estimated on 23 September 2020, with the data available at that moment. There are too few cases (yay!) to make it worthwhile updating these estimates for now. This decision will be revisited if community transmission in NSW increases beyond 5 cases per day.The charts below are out of date.
For each of the three time series shown, the green segment on the left shows a period of time that is substantively settled. The brown/orange segment is a "nowcast" - still heavily impacted by delays in incubation and reporting, but with at least some partial data. The final blue segment is a forecast.
The darker part of each ribbon reflects the estimated 50% credibility interval - the range within which a particular variable (infections, cases, or effective reproduction number) is believed to have a 50% chance of occurring, were the truth to be known. The wider, paler part of each ribbon is a 90% credibility interval.
The vertical pale grey bars represent confirmed cases, but with a small adjustment for test positivity (ie the proportion of tests for Covid-19 that return positive on any one day). New South Wales' test positivity over the last few months is shown in this chart, which makes clear that the proportion of positive tests is very low (ie good) by international standards:
The Covid-19 outbreak in Australia as a whole
The Australia estimates are not currently being updated
The last update for Australia was estimated on 23 September 2020, with the data available at that moment. There are too few cases outside of Victoria to make it worthwhile updating these estimates for now. This decision will be revisited if community transmission increases beyond 5 cases per day in at least two states.The charts below are out of date.
Treat the estimates for Australia as a whole with particular caution. After all, the average reproduction number will be an average over a large, diverse area. Generally, the state-specific estimates will be more useful.
For each of the three time series shown, the green segment on the left shows a period of time that is substantively settled. The brown/orange segment is a "nowcast" - still heavily impacted by delays in incubation and reporting, but with at least some partial data. The final blue segment is a forecast.
The darker part of each ribbon reflects the estimated 50% credibility interval - the range within which a particular variable (infections, cases, or effective reproduction number) is believed to have a 50% chance of occurring, were the truth to be known. The wider, paler part of each ribbon is a 90% credibility interval.
The vertical pale grey bars represent confirmed cases, but with a small adjustment for test positivity (ie the proportion of tests for Covid-19 that return positive on any one day). Australia's test positivity over the last few months is shown in this chart, which makes clear that the proportion of positive tests is still low (ie good) by international standards:
The positivity adjustment could be improved by doing it separately for each state. More generally, the effective reproduction number for Australia as a whole should be treated as even more indicative than for individual states; it is more useful to consider the states as separate outbreaks for this purpose.
What is this?
The main content on this page is a set of estimates of time-varying effective reproduction number in various states and for Australia as a whole. In essence, this is an estimate of the average number of people in an outbreak that each infected person infects in turn. If it is more than one, the epidemic expands. If it is less than one, it is under control and soon (if it stays under one) the case incidence numbers will begin to shrink.
This is a standard and important measure widely used in epidemiology. Unfortunately, it is difficult to estimate in real time, with incomplete data and significant delays between infection and a case showing up in the official confirmed count. Hence the emphasis on this site on a credibility interval showing the range within which we think the number is, not just a point estimate. Focus on the range of possibilities rather than expecting the precise number to be right.
Source data
The case numbers for Victoria are from the Victorian Department of Health and Human Services dashboard, with the latest day's data updated from their regular morning tweet. The date referred to by each number is the day before the announcement (so the number announced on 30 August 2020 is allocated to 29 August 2020 in my charts and estimates), following DHHS practice.
The case numbers for New South Wales come directly from NSW Health. There is about a 24 hour lag between the latest media announcements of "today's confirmed cases" and appearing in this data source.
The data on number of tests are collated by The Guardian from various government sources.
Estimation methods
This analysis is done with the EpiNow2 R package, whose primary authors are at the London School of Tropical Hygiene and Medicine. It takes into account estimated delays from infection, incubation and reporting. The approach is described in this blog post from July 2020.
The NSW data are published by likely source of infection, but currently the EpiNow2 method cannot take this into account in estimating effective reproduction number. I am sticking with the EpiNow2 approach because it has the best available adjustments for uncertain delay and right-truncation of case numbers, despite this limitation for now.
The approach to adjusting for positivity is as described in this blog post, with a power parameter of 0.1.
Code
The code for this analysis is available on GitHub.