数学科学在支持澳大利亚和新西兰新冠疫情应对中的作用
The role of the mathematical sciences in supporting the COVID-19 response in Australia and New Zealand
AI总结：
本文概述澳大利亚和新西兰新冠疫情应对中数学建模与数据分析的应用，涉及模型类型、建模-政策接口经验及沟通问题，总结该领域未来挑战与机遇。
摘要：
数学建模已被用于支持包括澳大利亚和新西兰在内的世界各国的新冠疫情应对工作。这两个国家采取了类似的疫情应对策略，结合严格的边境措施和社区干预措施，在实现高疫苗覆盖率之前将感染率降至最低。这需要一套与那些在整个疫情期间患病率高得多的国家所使用的不同的建模工具。在本文中，我们概述了一些为澳大利亚和新西兰疫情政策应对提供信息的数学建模和数据分析工作。这是对我们在建模-政策接口工作的经验以及这对应对疫情的影响的反思。我们概述了从短期预测到长期情景模型等在不同背景下使用的各类模型输出。我们讨论了数学建模者与卫生官员和政策制定者等利益相关者之间的沟通问题。最后，我们提出了该领域未来的一些挑战和机遇。
英文摘要：
Mathematical modelling has been used to support the response to the COVID-19 pandemic in countries around the world including Australia and New Zealand. Both these countries have followed similar pandemic response strategies, using a combination of strict border measures and community interventions to minimise infection rates until high vaccine coverage was achieved. This required a different set of modelling tools to those used in countries that experienced much higher levels of prevalence throughout the pandemic. In this article, we provide an overview of some of the mathematical modelling and data analytics work that has helped to inform the policy response to the pandemic in Australia and New Zealand. This is a reflection on our experiences working at the modelling-policy interface and the impact this has had on the pandemic response. We outline the various types of model outputs, from short-term forecasts to longer-term scenario models, that have been used in different contexts. We discuss issues relating to communication between mathematical modellers and stakeholders such as health officials and policymakers. We conclude with some future challenges and opportunities in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
