Planning for recovery

The current COVID-19 Pandemic has everyone scrambling to understand and deal with its immediate implications. The effort to contain the spread and limit the health risks, especially for those who have conditions which increase mortality, is paramount. There is also the requirement to maintain the economic conditions as much as possible such that essential infrastructures and eco-systems remain valid. Without downplaying these immediate priorities, there also needs to be some strategic thinking around how we deal with the inevitable potential recovery.

It would seem that, like the spread of COVID-19 and the economic impacts, there will be sectors and geographies which will open up and recover faster than others. The ability to identify and prioritise what needs to be done, based on available data, will be key to ramping economies back up. This includes getting unemployed people back to work, and for those still in work, back to maximum productivity and effectiveness.

Attributes that are useful to facilitate this process include, in no particular order:

· Local and regional social, cultural, and economic conditions,

o Unemployment or partial employment rates,

o Merchant locations and profile eg retail, commercial office, manufacturing, employee numbers, etc

o Age and health demographics,

· Government support programs by industry and geography,

· Current restrictions on social distancing and isolation

· Infection “hotspots”

· Transport routes and modes

Processing and visualising the above data, and other data sets, will provide location intelligence that would, for example, provide insight as to whether and where the restrictions could be relaxed for non-essential retailers and other businesses. It can also provide insight for businesses looking to kick-start their operations once restrictions are relaxed. In the case of greater Sydney and the Central Coast of NSW, the below images show high-risk COVID-19 mortality areas based on age and existing health conditions.

In this case, an algorithmic “risk index” ranging from 0–25 has been constructed by Mapcite with 25 being the highest possible mortality risk in any region and 0 being the lowest. Across Sydney the risk index ranges up to 22 but on the Central Coast of NSW, the risk ranges up to 24, primarily as a result of high concentrations of elderly people. Until such time that there is very little overall risk to COVID-19, it would seem obvious that relaxing social distancing in areas with a high-risk index is inadvisable.

Using this type of data analysis, it could be determined which areas could be “revitalised” first, with the least risk to the community. This does not necessarily mean a mandatory action by government which favours some regions over others; it could be more a matter of businesses making their own assessments of where to re-start their operations. Having said that, there will be many business locations, especially in the retail sector, which may never open again.

Whilst this may appear to be helping those who may not necessarily be in greatest need, we are dealing here with economic, rather than personal health, recovery. Such revitalising would therefore have flow-on affects well beyond the location being prioritised. An example of this might be re-opening a school which would then allow parents who can’t work from home, to return to work.

The objective here is to be able to determine:

· What are the regions of highest demand for economic growth,

· Where and what businesses can be re-ignited,

· From where to recruit employees most able to meet the demand,

· Impacts on limited transport modes and routes,

· Maximising supply logistics, and

· Minimising risks of COVID-19 resurgence.

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Steve Walker

Steve Walker

Geospatial data analytics and AI Advocate / Strategist / PropTech / HealthTech / Supporter — Indigenous Opportunity / Food and Wine Critic (Not professionally)