Mathematical modelling to inform New Zealand’s COVID-19 response

Abstract

Between February and May 2020, New Zealand recorded 1504 cases of COVID-19 before eliminating community transmission of the virus in June 2020. During this period, a series of control measures were used including population-wide interventions implemented via a four-level alert system, border restrictions, and a test, trace, and isolate system. Mathematical modelling played a key role in informing the government response and guiding policy development. In this paper, we describe the development of a stochastic mathematical model for the transmission and control of COVID-19 in New Zealand. This includes features such as superspreading, case under-ascertainment, testing and reporting delays, and population-wide and case-targeted control measures. We show how the model was calibrated to New Zealand and international data. We describe how the model was used to compare the effects of various interventions in reducing spread of the virus and to estimate the probability of elimination. We conclude with a discussion of the policy-modelling interface and preparedness for future epidemic outbreaks.