Homelessness As An Epidemic With A Computer-Predictable Course

A show jumper in classic riding gear guides his horse over a jump supported by little rooftops
The homeless who live in the shadows of Toronto's stockbroker belt keep a low profile in Caledon, where anything horsey is a far more common sight.

Virtually all nations are still struggling with attempting to chart the rise and fall of the COVID-19 pandemic. It has produced an unprecedented display of graphic peaks and valleys. Experts, who are working with still uncertain knowledge, attempt to predict the future course of the disease and inform (convince?) the public of the appropriate actions that should, or must, be taken to mitigate the consequences.

Homelessness can be viewed as a separate epidemic, albeit one influenced by the coronavirus and in turn one that locally influences the course of COVID-19. Not surprisingly, given the free flow of populations in many nations, there is important information to be interpreted and shared about the about the rise and fall of homelessness, as well as value in predicting its location and impact.

Which brings us to a federally-funded Canadian project that is attempting to develop computer algorithms to do that. It is clearly not a development field for the faint-hearted. Consider the serious teething pains of a recently-implemented project in the United Kingdom to predict the “disease” of fraud among applicants for financial support. Try: Poor Grades For UK Machine “Intelligence” Serving Council Needs

The Canadian project soldiers on, however, gaining some astonished attention with a prediction that homelessness is due to rise significantly in York Region, which includes Toronto’s stockbroker belt with its monster mansions, infinity lawns and well-kept horse fencing. Homelessness in that neighbourhood? It’s apparently already there. Who knew?

Read more in NEWMARKETTODAY: Where will homelessness rise or fall? Federally funded AI predicts spike in York Region