{"title":"Quantifying what could have been – The impact of the Australian and New Zealand governments’ response to COVID-19","authors":"Chris Varghese, William Xu","doi":"10.1016/j.idh.2020.05.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Conclusion</h3><p>This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.</p></div>","PeriodicalId":45006,"journal":{"name":"Infection Disease & Health","volume":"25 4","pages":"Pages 242-244"},"PeriodicalIF":2.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.idh.2020.05.003","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infection Disease & Health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468045120300298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 21
Abstract
Background
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.
Methods
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.
Conclusion
This provides both evidence and impetus for governments considering similar measures in response to COVID-19 and other pandemics.
期刊介绍：
The journal aims to be a platform for the publication and dissemination of knowledge in the area of infection and disease causing infection in humans. The journal is quarterly and publishes research, reviews, concise communications, commentary and other articles concerned with infection and disease affecting the health of an individual, organisation or population. The original and important articles in the journal investigate, report or discuss infection prevention and control; clinical, social, epidemiological or public health aspects of infectious disease; policy and planning for the control of infections; zoonoses; and vaccination related to disease in human health. Infection, Disease & Health provides a platform for the publication and dissemination of original knowledge at the nexus of the areas infection, Disease and health in a One Health context. One Health recognizes that the health of people is connected to the health of animals and the environment. One Health encourages and advances the collaborative efforts of multiple disciplines-working locally, nationally, and globally-to achieve the best health for people, animals, and our environment. This approach is fundamental because 6 out of every 10 infectious diseases in humans are zoonotic, or spread from animals. We would be expected to report or discuss infection prevention and control; clinical, social, epidemiological or public health aspects of infectious disease; policy and planning for the control of infections; zoonosis; and vaccination related to disease in human health. The Journal seeks to bring together knowledge from all specialties involved in infection research and clinical practice, and present the best work in this ever-changing field. The audience of the journal includes researchers, clinicians, health workers and public policy professionals concerned with infection, disease and health.
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