How big data is analysing COVID-19 numbers

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All those insights come from crunching numbers.

Big-data-COVID-19

Big data COVID-19

Big data COVID-19-The COVID-19 pandemic is many things to many people, but for data scientists it has become an unprecedented opportunity to apply complex mathematical models to describe the spread of a pandemic that continues to defy history.

Where sites like Worldometer glibly feed a news-hungry public macbre leaderboards about the number of infections and deaths, data-analytics specialists are helping epidemiologists leverage much larger data sets – fuelling predictions about the potential spread of the disease, casualty estimates, the impact of various preventative measures, and potential forecasts about when life might begin returning to normal.

Data from a “complex model of how COVID-19 could spread in Australia”, one recent University of Sydney analysis proclaimed, had confirmed that we could control the outbreak if 8 out of 10 Australians stay home – but if the rate dropped to 7 out of 10 people, cases would continue growing out of control.

Such conclusions directly inform public-health measures and shape the nature and extent of the response – and data firms are working continuously to develop, test, and refine the models that produce the numbers.

It has been a fierce and immediate call to arms for firms like Smash Delta, a Sydney-based data-strategy firm whose normal engagements revolve around using data to answer questions around customer churn, operational efficiency, staff development, product development, profit maximisation, and the like.

The inconsistencies of COVID-19 data and forecasts had come to annoy the principals of the firm, who set out to develop an authoritative source of data about the pandemic.

“We were really frustrated with what we were hearing and seeing, whether from the public generally or people around us, not understanding the severity of where we are heading with this virus,” managing director Ben Morley-John told Information Age.

The delays between infection with the virus and presentation of its symptoms had made this pandemic particularly difficult to model, he explained, since infected individuals can spread the virus for many days before they even realise they have it.

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Article Credit: ACS

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