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Modelling the nation’s COVID-19 response

Recognising the need for robust ways to measure the effectiveness of contact tracing in reducing the spread of COVID-19, a team of researchers, including Manaaki Whenua’s Dr Rachelle Binny and Dr Audrey Lustig, applied their wildlife disease modelling knowledge to develop a model to investigate the importance of contact tracing, quarantine, and isolation in reducing transmission. “We used an agestructured branching process model for COVID-19 transmission in AoNZ, in the presence of contact tracing and case isolation,” says mathematical modeller Dr Binny.

“Our results show that a highquality, rapid contact tracing system, combined with strong support for people in quarantine or isolation, can be highly effective in reducing the spread of COVID-19.

“If case isolation or quarantine are imperfect, or some contacts aren’t traced or are traced more slowly, then the reduction is only around 40%, meaning that stronger social distancing measures would be needed to control an outbreak.” Predicting the elimination of evolving COVID-19 variants.

A second research paper involving Dr Binny, ‘Predicting elimination of evolving virus variants’, models the recent emergence of multiple SARSCoV-2 variants and the risk these pose to global efforts to control the COVID-19 pandemic. In the study, researchers created a simple model of disease spread, which includes the evolution of new variants and varying vaccine effectiveness for these new strains.

They found that viruses that mutate into multiple new variants need fast vaccine delivery in order to be contained. The researchers concluded that rapid vaccine updates to target new strains are more effective than slow updates, and that containing spread through non-pharmaceutical interventions is vital while these vaccines are delivered.

The study also suggested that a continuous vaccination roll-out programme, where updated vaccines are given to unvaccinated individuals, rather than revaccinating high-priority individuals, may slightly increase the probability of elimination. However, the researchers note that this prediction warrants further investigation using population-structured models to assess the risk this would pose to vulnerable individuals such as frontline workers or those at higher risk of severe disease.