It may take until the end of the year to get case numbers close to zero, unless more stringent measures are introduced.
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Articles on Modelling
Displaying 21 - 40 of 68 articles
Two museum exhibits, one in the Smithsonian and one in Australia, have opened public displays featuring the spectacular meg. But while both models are mega impressive, they’re not the same. Why?
With the Australian Open about to start this year’s grand slam series, a crunch of the data from past performances gives a hint at who is the current best male player, possibly.
Cybersecurity risks are evolving rapidly. How can they be more effectively assessed and managed ?
Studying how SARS-CoV-2, the virus that causes COVID-19, travels through indoor air spaces can help reduce transmission risk.
Here’s what we still need to find out before we can know when we’ll be able to return to our pre-coronavirus ways.
To better understand how cancer tumors grow, mathematicians use diffusion models.
We’re on the road again. Getting enough COVID-19 vaccine to where it’s needed in a given time frame is the next logistical hurdle.
The R number fluctuates more as case numbers fall.
Many of the more formal models for predicting the pandemic try to understand why changes happen – but often it can be more accurate to ignore the reasons and simply look at the data.
The UK locked down too late and has been in catch-up mode ever since. But with contact tracing, it can turn things around.
The South African government and some of its advisors want to have the best of both worlds. They want to use incorrect predictions by early models about the COVID-19 pandemic to claim success.
Neuroscientist Karl Friston claims generative modelling techniques produce more valid predictions than conventional models, but the evidence so far is limited.
An easy question, but a difficult answer.
National models on the spread of COVID-19 have helped us through this crisis. But we’ll need local models to get us through the next stage.
The science to policy process that was developed to guide climate mitigation decisions can be applied to the response to the COVID-19 pandemic, without having to be reinvented.
One practical exit strategy from lockdown would be identifying green zones, and progressively joining them together once it is safe to do so.
Policy-makers at various levels of government rely on models and simulations to make predictions about controlling the spread of COVID-19.
Scientific models can help us understand the important features of complex systems, but they need good data.
Study raises hopes that we may be closer to herd immunity than previously thought.
Top contributors
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Lecturer in Probability and Statistics and Senior Research Fellow in Infectious Disease Modelling, University of Oxford
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Professor, Systems and Computer Engineering, Carleton University
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Deputy Director (Programs), Burnet Institute
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Professor, epidemiological modelling of infectious diseases, UNSW Sydney
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Honorary Enterprise Professor, School of Population and Global Health, and Department of General Practice and Primary Care, The University of Melbourne
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Professor of Infectious Disease and Epidemiology, James Cook University
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Professor of Biostatistics and Epidemiology, University of South Australia
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Econometrician, Burnet Institute
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Professor of Civil and Environmental Engineering, University of Adelaide
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Associate Professor of Human Dimensions of Natural Resources, Colorado State University
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Research Fellow, School of Civil, Environmental and Mining Engineering, University of Adelaide
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Chief Executive Officer, Monash University
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Senior Lecturer in Applied Mathematics, Queensland University of Technology
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Professor of Mathematics, Heriot-Watt University
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Adjunct professor, UNSW Sydney
