An assessment of the potential impact of the Omicron variant of SARS-CoV-2 in Aotearoa New Zealand

Giorgia Vattiato; Oliver Maclaren; Audrey Lustig; Rachelle N Binny; Shaun C Hendy; Michael J Plank

doi:10.1016/j.idm.2022.04.002

An assessment of the potential impact of the Omicron variant of SARS-CoV-2 in Aotearoa New Zealand

Infect Dis Model. 2022 Jun;7(2):94-105. doi: 10.1016/j.idm.2022.04.002. Epub 2022 Apr 9.

Authors

Giorgia Vattiato^{1

2}, Oliver Maclaren³, Audrey Lustig⁴, Rachelle N Binny⁴, Shaun C Hendy², Michael J Plank¹

Affiliations

¹ School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand.
² Department of Physics, University of Auckland, Auckland, New Zealand.
³ Department of Engineering Science, University of Auckland, Auckland, New Zealand.
⁴ Manaaki Whenua, Lincoln, New Zealand.

Abstract

New Zealand delayed the introduction of the Omicron variant of SARS-CoV-2 into the community by the continued use of strict border controls through to January 2022. This allowed time for vaccination rates to increase and the roll out of third doses of the vaccine (boosters) to begin. It also meant more data on the characteristics of Omicron became available prior to the first cases of community transmission. Here we present a mathematical model of an Omicron epidemic, incorporating the effects of the booster roll out and waning of vaccine-induced immunity, and based on estimates of vaccine effectiveness and disease severity from international data. The model considers differing levels of immunity against infection, severe illness and death, and ignores waning of infection-induced immunity. This model was used to provide an assessment of the potential impact of an Omicron wave in the New Zealand population, which helped inform government preparedness and response. At the time the modelling was carried out, the date of introduction of Omicron into the New Zealand community was unknown. We therefore simulated outbreaks with different start dates, as well as investigating different levels of booster uptake. We found that an outbreak starting on 1 February or 1 March led to a lower health burden than an outbreak starting on 1 January because of increased booster coverage, particularly in older age groups. We also found that outbreaks starting later in the year led to worse health outcomes than an outbreak starting on 1 March. This is because waning immunity in older groups started to outweigh the increased protection from higher booster coverage in younger groups. For an outbreak starting on 1 February and with high booster uptake, the number of occupied hospital beds in the model peaked between 800 and 3,300 depending on assumed transmission rates. We conclude that combining an accelerated booster programme with public health measures to flatten the curve are key to avoid overwhelming the healthcare system.

Keywords: COVID-19; Epidemiological modelling; Immunity; Infectious diseases; Public health; Vaccination.