Abstract
New Zealand responded to the COVID-19 pandemic with a combination of border restrictions and an Alert Level (AL) system that included strict stay-at-home orders. These interventions were successful in containing an outbreak and ultimately eliminating community transmission of COVID-19 in June 2020. The timing of interventions is crucial to their success. Delaying interventions may reduce their effectiveness and mean that they need to be maintained for a longer period. We use a stochastic branching process model of COVID-19 transmission and control to simulate the epidemic trajectory in New Zealand's March–April 2020 outbreak and the effect of its interventions. We calculate key measures, including the number of reported cases and deaths, and the probability of elimination within a specified time frame. By comparing these measures under alternative timings of interventions, we show that changing the timing of AL4 (the strictest level of restrictions) has a far greater impact than the timing of border measures. Delaying AL4 restrictions results in considerably worse outcomes. Implementing border measures alone, without AL4 restrictions, is insufficient to control the outbreak. We conclude that the early introduction of stay-at-home orders was crucial in reducing the number of cases and deaths, enabling elimination.
Highlights
New Zealand responded to the COVID-19 pandemic with a combination of border restrictions and an Alert Level (AL) system that included strict stay-at-home orders
We explore whether introducing border restrictions earlier in New Zealand might have been sufficient to eliminate or reduce transmission from international arrivals to the extent where stringent AL4 restrictions could have been avoided or less restrictive measures been sufficient
Case data were obtained from Ministry of Health (MoH), containing arrival dates, symptom onset dates, isolation dates and reporting dates for all international cases arriving in New Zealand between February and June 2020
Summary
We simulated a stochastic model of COVID-19 spread in New Zealand [4,14] under a factual scenario using actual timings for border restrictions, border closure and AL4, and for counterfactual scenarios in which implementation of these interventions were either delayed or started earlier. Hendy et al [4] used the stochastic branching process under the same assumptions applied here, except for a shorter scale parameter of 3.48 days for the isolation-to-report delay distribution, which has little impact on Reff estimates [21] They compared the average simulated numbers of reported cases per day with observed reported cases and estimated best-fit Reff values by minimizing the root-mean-square error of square roottransformed data, over a time window from 10 March to 27 April. This value is lower than that of AL1, reflecting a likelihood of substantial behaviour change and heightened public awareness of the risks of community transmission, even in the absence of any alert level restrictions
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