Early intervention is the key to success in COVID-19 control

Author:

Binny Rachelle N.12ORCID,Baker Michael G.34,Hendy Shaun C.52ORCID,James Alex62ORCID,Lustig Audrey12,Plank Michael J.62ORCID,Ridings Kannan M.52,Steyn Nicholas652ORCID

Affiliation:

1. Manaaki Whenua, Lincoln, New Zealand

2. Te Pūnaha Matatini: the Centre for Complex Systems and Networks, Auckland, New Zealand

3. Department of Public Health, University of Otago, Wellington, New Zealand

4. Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand

5. Department of Physics, University of Auckland, Auckland, New Zealand

6. School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand

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.

Funder

Te Pūnaha Matatini, New Zealand

Ministry of Business, Innovation and Employment

Publisher

The Royal Society

Subject

Multidisciplinary

Reference34 articles.

1. Desvars-Larrive A et al. 2020 CCCSL: Complexity Science Hub Covid-19 Control Strategies List. Version 2.0. See https://github.com/amel-github/covid19-interventionmeasures (accessed 20 August 2020).

2. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

3. The effect of large-scale anti-contagion policies on the COVID-19 pandemic

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

5. Inferring the effectiveness of government interventions against COVID-19

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