Effective reproduction number for COVID-19 in Aotearoa New Zealand

Abstract

The effective reproduction number, Reff, is the average number of secondary cases infected by a primary case, a key measure of the transmission potential for a disease. Compared to many countries, New Zealand has had relatively few COVID-19 cases, many of which were caused by infections acquired overseas. This makes it difficult to use standard methods to estimate Reff. In this work, we use a stochastic model to simulate COVID-19 spread in New Zealand and report the values of Reff from simulations that gave best fit to case data. We estimate that New Zealand had an effective reproduction number Reff = 1.8 for COVID-19 transmission prior to moving into Alert Level 4 on March 25 2020 and that after moving into Alert level 4 this was reduced to Reff = 0.35. Our estimate Reff = 1.8 for reproduction number before Alert Level 4, is relatively low compared to other countries. This could be due, in part, to measures put in place in early- to mid-March, including: the cancellation of mass gatherings, the isolation of international arrivals, and employees being encouraged to work from home.

Evolutionary Dynamics and Dissemination Pattern of the SARS-CoV-2 Lineage B.1.1.33 During the Early Pandemic Phase in Brazil

“…Phylodynamic modeling indicates a significant reduction of 66% in the median R e (from 3.8 to 1.3) of lineage B.1.1.33 following non-pharmaceutical interventions in Rio de Janeiro, but also points that such interventions probably failed to bring R e below 1.0 during the early phase. These findings are fully consistent with the pattern inferred from epidemiological modeling in Rio de Janeiro ( Candido et al, 2020 ; de Souza et al, 2020 ; Mellan et al, 2020 ) and contrast with data from Europe and Oceania that points to stronger reductions in R e (71–82%) after public health interventions, sufficient to bring the epidemic under control ( Binny et al, 2020 ; Flaxman et al, 2020 ; Jarvis et al, 2020 ; Seemann et al, 2020 ).…”

Phylodynamics reveals the role of human travel and contact tracing in controlling the first wave of COVID-19 in four island nations

“…3). Estimates of R e based only on epidemiological data are also consistently below 1 during these time periods (Binny et al, 2020b,a; Price et al, 2020; Karnakov et al, 2020).…”

Probability of elimination for COVID-19 in Aotearoa New Zealand

“…Estimates were obtained by simulating 1000 realisations of the model and calculating probability of elimation, P(elim) , as the proportion of model realisations that achieve elimination (under our definition above) within a given timeframe. We used the parameter values given in Plank et al (2020) and James et al (2020), with a longer reporting delay of 6 days (distribution from isolation to reporting, Γ(shape = 1, scale = 6)) and the best-fit estimates for reproduction number R eff prior to and during Alert Level 4 reported in Binny et al (2020). For the optimistic scenario, we assumed 75% of clinical cases are detected and reported ( p R =75%) and the transmission rate relative to no population-wide control, C(t) , was set to C(t) = 1 , 0.75, 0.4 and 0.15 (corresponding to R eff = 2.37, 1.78, 0.95 and 0.35) for Alert Levels 1-4, respectively.…”

“…Four weeks of Alert Level 4 restrictions were effective at reducing New Zealand’s effective reproduction number to 0.35 (Binny et al, 2020) and numbers of reported cases declined to less than 10 new cases per day. From 27th April (11.59pm), there was a slight easing of certain lockdown restrictions (Alert Level 3) (for example, schools – years 1 to 10 – and Early Childhood Education centres were permitted to open with limited capacity) which remained in place for a further three weeks.…”