Covid-19 Modelling Aotearoa
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Effective reproduction number for Covid-19 in Aotearoa New Zealand

by Admin | 22 May 2020 | Branching process modelling

Abstract The effective reproduction number, Reff, is the average number of secondary cases infected by a primary case, a key measure of the transmission potential for a disease. Compared to many countries, Aotearoa New Zealand has had relatively few Covid-19 cases,...

A structured model for Covid-19 spread: Modelling age and healthcare inequities

by Admin | 15 May 2020 | Branching process modelling, Equity and at-risk communities

Abstract We use a stochastic branching process model, structured by age and level of healthcare access, to look at the heterogeneous spread of Covid-19 within a population. We examine the effect of control scenarios targeted at particular groups, such as school...

New interactive app simulates Covid-19 spread

by Admin | 24 Apr 2020 | Branching process modelling

An Aotearoa New Zealand-specific interactive epidemic simulation app developed by Dr Audrey Lustig, Associate Investigator at Te Pūnaha Matatini, and hosted by the University of Auckland’s Centre for eResearch, has just been released. Called the Covid-19 Take Control...

Effect of Alert Level 4 on Reff: Review of international Covid-19 cases

by Admin | 22 Apr 2020 | Branching process modelling

Abstract The effective reproduction number, Reff, is an important measure of transmission potential in the modelling of epidemics. It measures the average number of people that will be infected by a single contagious individual. A value of Reff > 1 suggests that an...

Estimated inequities in Covid-19 infection fatality rates by ethnicity for Aotearoa New Zealand

by Admin | 17 Apr 2020 | Branching process modelling, Equity and at-risk communities

Abstract There is limited evidence as to how clinical outcomes of Covid-19 including fatality rates may vary by ethnicity. We aim to estimate inequities in infection fatality rates (IFR) in New Zealand by ethnicity. We combine existing demographic and health data for...
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