key: cord-0754060-hzpbujye authors: Varghese, Chris; Xu, William title: Quantifying what could have been – the impact of the Australian and New Zealand governments’ response to COVID-19 date: 2020-05-27 journal: Infect Dis Health DOI: 10.1016/j.idh.2020.05.003 sha: a0c0c2e32481f84717620149442b6c1d33e7a4a6 doc_id: 754060 cord_uid: hzpbujye The Australian and New Zealand governments both initiated strict social distancing measures in response to the COVID-19 pandemic in late March. It remains difficult to quantify the impact this had in reducing the spread of the virus. Bayesian structural time series model provide a model to quantify the scenario in which these government-level interventions were not placed. Our models predict these strict social distancing measures caused a 79% and 61% reduction in the daily cases of COVID-19 across Australia and New Zealand respectively. This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics. The COVID-19 pandemic has been characterised by a heterogenous response from governments around the world. Some have initiated early social distancing measures and mandatory shut down of non-essential services, while others have relied heavily on thorough test and trace strategies [1] . There have been variations in the severity of government measures between countries. Among this heterogeneity, it is difficult to quantify the effect of these actions and what may have occurred in the absence of such efforts. On the 25 th of March 2020, at 11.59pm, New Zealand entered Alert Level 4 in an attempt to eliminate viral transmission [2] . Alert Level 4 assumes sustained and intensive transmission of COVID-19, making widespread outbreaks likely. The government has implemented a range of urgent measures including: instructing people to stay at home, closure of educational facilities and non-essential facilities (excluding supermarkets, pharmacies, clinics and lifeline utilities), rationing of supplies, requisition of facilities, suspension of international travel, limitation of domestic travel, and major reprioritisation of healthcare services. Australia mounted a similar but slightly less stringent nationwide response on the 29 th of March [3] . Widespread social distancing measures were implemented with varying levels of non-essential facility shutdown between country states. We aimed to investigate the effect of government driven social distancing measures in Australia and New Zealand and quantify the potential magnitude of COVID-19 case reduction. In the absence of randomized controlled data investigating the impact of quarantine measures at the country-level, causal inferences via Bayesian structural time series models may provide a suitable alternative. Our model was trained on both real-world data and simulated statistics to generate a counterfactual model. The counterfactual was defined as the predicted number of daily cases if strict social distancing measures were not implemented by the New Zealand or Australian government. A modified SEIR calculator, utilised in other COVID-19 modelling studies [5] , was used to estimate the rate of COVID-19 dissemination through the New Zealand and Australian populations [6] . SEIR predictive parameters inputted into the model were: Countries test tactics in "war" against COVID-19 Science (80-) Current COVID-19 Alert Level The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study Epidemic Calculator The reproductive number of COVID-19 is higher compared to SARS coronavirus CV conceived the idea of the letter. CV conducted statistical analysis. All authors contributed with the first draft. All authors contributed with subsequent revisions. All authors approved the final submitted version None. CV conceived the idea of the letter. CV conducted statistical analysis. All authors contributed with the first draft. All authors contributed with subsequent revisions. All authors approved the final submitted version. None to report. No conflicts of interest to report.