A statistically designed random sampling scheme, based on as few as 100 people, would give a very high probability of detecting if there are any COVID-19 cases and highlight at-risk hotspots.
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Articles on surveillance testing
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As we start to test people without symptoms for COVID-19, the likelihood of generating false positive tests goes up. Here’s why that’s a problem.
Testing and isolating cases only after they develop symptoms might not be enough to prevent further outbreaks of COVID-19. Sentinel surveillance, by testing randomly in the community, can go further.
Top contributors
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Professor and Director of Doherty Epidemiology, The University of Melbourne
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Associate Professor, Computing & Information Systems; Director, Melbourne Data Analytics Platform, The University of Melbourne
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Director, WHO Collaborating Centre for Viral Hepatitis, The Peter Doherty Institute for Infection and Immunity
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Senior research fellow, The Peter Doherty Institute for Infection and Immunity
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Epidemiologist, WHO Collaborating Centre for Viral Hepatitis, The Peter Doherty Institute for Infection and Immunity
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Emeritus Professor of Statistics, Massey University
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Professor of Statistics and Data Science, Te Herenga Waka — Victoria University of Wellington
