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

Abstract

New Zealand responded to the COVID-19 pandemic with a combination of border restrictions and an Alert Level system that included strict stay-at-home orders. These interventions were successful in containing the outbreak and ultimately eliminating community transmission of COVID-19. The timing of interventions is crucial to their success. Delaying interventions for too long may both reduce their effectiveness and mean that they need to be maintained for a longer period of time. Here, we use a stochastic branching process model of COVID-19 transmission and control to simulate the epidemic trajectory in New Zealand and the effect of its interventions during its COVID-19 outbreak in March-April 2020. We use the model to calculate key outcomes, including the peak load on the contact tracing system, the total number of reported COVID-19 cases and deaths, and the probability of elimination within a specified time frame. We investigate the sensitivity of these outcomes to variations in the timing of the interventions. We find that a delay to the introduction of Alert Level 4 controls results in considerably worse outcomes. Changes in the timing of border measures have a smaller effect. We conclude that the rapid response in introducing stay-at-home orders was crucial in reducing the number of cases and deaths and increasing the probability of elimination.

Exploring the impact of COVID‐19 on mobile dating: Critical avenues for research

“…Considering how this occurs must be further located within the surrounding sociomaterial conditions of different lives, in diverse locations. Take the case of Aotearoa New Zealand; whilst we have benefited from a successful and strong public health response to COVID‐19, this ‘success’ will always be contingent on how the pandemic continues to unfold and what strategies are taken to contain it (Binny et al., 2021 ). Moreover, like other infectious diseases, COVID‐19 has affected communities differently across Aotearoa, along existing lines of ethnic and socio‐economic inequity (McLeod et al., 2020 ).…”

The Costs and Benefits of Covid-19 Lockdowns in New Zealand

“…These in turn are based upon an epidemiological model in which each infected individual is estimated to infect R others (the reproduction rate) and this process extrapolated until so many people are infected that the virus dies out for lack of new targets. The estimates of Binny et al (2020) share this crucial feature.…”

“…In the first of these models, the estimated deaths under lockdown (500) are far in excess of the actual deaths incurred to date under lockdown, and this implies that their estimates of the additional deaths under mitigation will also be far too high. In addition, the death rate estimated by Binny et al (2020) under a mitigation policy (at least 32,000 deaths, which is 6,400 per 1m of New Zealand's population of 5m) is vastly in excess of the death rate per 1m to date in any country pursuing a mitigation policy or any alternative. 1 In addition, both of these models do not allow for the fact that, as the number of deaths rises, people will react by engaging in more and more protective actions that will reduce the future death rate, such as hand washing, mask wearing, reducing social interactions, working from home, etc.…”

Border closure and travel restrictions to control the spread of COVID-19: an update to a Cochrane review

“…No new studies were identified in the updated search. The Cochrane review 3 identified two modelling studies; 10,17 one study 17 reported benefits with travel restrictions (0.3% to 8% at peak) and the other study 10 reported that an early implementation of border restrictions or a delayed border closure would lead to 79 (95% CI 67 to 97) and 91 (95% CI 77 to 110) daily cases at the epidemic peak, respectively (low-certainty evidence).…”

“…Similar to the Cochrane review, 3 we did not attempt to differentiate between a complete border closure versus travel restrictions (leading to varying degrees of difficulty in crossing borders), and instead report these in a combined intervention category. Studies reported on (1) 'cases avoided due to the measure' (n = 30), [6][7][8][9][10][12][13][14]16,[18][19][20][21][23][24][25]28,29,[32][33][34][35][36][37][38][39]41,42,46,47 (2) the shift in epidemic development (n = 19), 8,10,11,14,15,17,18,21,22,26,27,30,31,36,40,42,44,45,48…”