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Likelihood of infecting or getting infected with COVID-19 as a function of vaccination status, as investigated with a stochastic model for New Zealand (Aotearoa)
Leighton M Watson.
  • Leighton M Watson; University of Oregon
Preprint in English | medRxiv | ID: ppmedrxiv-21266967
ABSTRACT
AimThe New Zealand government is transitioning from the Alert Level framework, which relies on government action and population level controls, to the COVID-19 Protection Framework, which relies on vaccination rates and allows for greater freedoms (for the vaccinated). As restrictions are eased, there is significant interest in understanding the relative risk of spreading COVID-19 posed by unvaccinated and vaccinated individuals. MethodsA stochastic branching process model is used to simulate the spread of COVID-19 for outbreaks seeded by unvaccinated or vaccinated individuals. The likelihood of infecting or getting infected with COVID-19 is calculated based on vaccination status. ResultsA vaccinated traveler infected with COVID-19 is 9x less likely to seed an outbreak than an unvaccinated traveler infected with COVID-19. For a vaccination rate of 50%, unvaccinated individuals are responsible for 87% of all infections whereas 3% of infections are from vaccinated to vaccinated. When normalized by population, a vaccinated individual is 6.8x more likely to be infected by an unvaccinated individual than by a vaccinated individual. For a total population vaccination rate of 78.7%, which is equivalent to the 90% vaccination target for the eligible population (over 12 years old), this means that vaccinated individuals are 1.9x more likely to be infected by an unvaccinated individual than by a vaccinated, even though there are 3.7x more vaccinated individuals in the population. ConclusionsThis work demonstrates that most new infections are caused by unvaccinated individuals. These simulations illustrate the importance of vaccination in stopping individuals from becoming infected with COVID-19 and in preventing onward transmission.
Full text: Available Collection: Preprints Database: medRxiv Document Type: Preprint Language: English Year: 2021

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Full text: Available Collection: Preprints Database: medRxiv Document Type: Preprint Language: English Year: 2021
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