Postcode characteristics of Melbourne's COVID19 hotspots

Monika Sarder

This analysis can be reproduced in R in full from the relevent GitHub Repository

Melbourne’s second wave, which commenced in June and peaked in the first week of August, was driven primarily by community transmission. New outbreaks throughout this period have tended to arise in workplaces, flowing through to families and communities (Source: DHHS).

The highly localised nature of the second wave led to experimentation with a Postcode-based lockdown in July. This was quickly abandoned in favour of a city-wide lockdown. However, it is clear that geography, and geospatial characteristics, have an effect on the rate of virus transmission.

This social and economic reality was notably absent from the modelling that informed Melbourne’s roadmap out of lockdown. The model assumed identical features across postcodes, that all cases of COVID19 in the community were randomly distributed, and that all cases are mixing in the community in the same way.

In order to achieve a successful COVID19 endgame, we need to identify the actual community characteristics that are correlated with high rates of transmission. A detailed understanding of the reality of the city, has informational value relevant to both the modelling, and the public health response.

The final dataset comprises 222 Melbourne postcodes.1

Data in this study

The ABS 2016 Census of Population and Housing contains rich information on a range of community characteristics. Statistical analysis was undertaken of the relationship between key features and the level of COVID19 infection in various postcodes (ie number of confirmed cases per 100K) at the peak of the wave on 6 August.

The following characteristics showed positive and statistically significant relationships between COVID19 infection levels and the proportion of residents:

  • working in the transport industry; and
  • who are below the median age
  • living below the poverty line
  • living in rental properties
  • living in a flat or semi-detached home (cf a detached house)
  • living in a home without an internet connection
  • speaking a language other than English in the home

A note on interpretation

A positive statistical correlation indicates that when we see an increase in one characteristic (eg number of rental households), we can expect the other characteristic (ie COVID19 cases) to increase as well. An important caveat to readers, is that such a relationship does not constitute ‘proof’ that one thing causes another, merely that something connects them.

For example, the number of ice creams sold per day may be positively correlated with the number of beach lifesaver rescues. This does not mean however, that ice-creams are causing people to get into trouble in the water.

Statisticians deal with the relationships, or patterns, between observable and measurable phenomena. When you are dealing with the statistics of human behaviour (ie in this case in relation to a public health crisis and highly transmissible virus), there is always more to the story, more questions to ask, more to uncover. Statistics cannot provide you with a perfect answer, but it can show you where to look to uncover a better answer than the one you already have.

Geographical concentration of COVID19

At the peak of the second wave the number of confirmed cases per 100K of population was significantly higher in Melbourne’s North and West than in other parts of the city.

Figure 1 Confirmed cases per 100K by Postcode on 6 August