标题： 建模种族与传染病传播动态之间的相互作用 显示英文标题

标题： Modelling the interaction between ethnicity and infectious disease transmission dynamics

摘要： 在新冠疫情中，Aotearoa 采取了消除策略，随后采取了缓解策略，取得了巨大成功，并保持了较低的健康影响。 然而，健康结果存在不平等现象，特别是毛利人和太平洋族裔的疫苗覆盖率较低，并经历了较高的年龄标准化住院率和死亡率。 模型可以预测疾病传播和疾病负担，这可以有效地为政策提供信息，但通常不太擅长包括不平等/异质性。 尽管不同种族的健康结果存在差异，但大多数模型并未将种族异质性作为因素明确考虑。 我们开发了这样的模型，以研究 Aotearoa 的第一次奥密克戎浪潮，这是首次出现 SARS-CoV-2 的广泛社区传播。 我们分析了三种接触模式模型，即按比例混合、同质混合和经验推导的混合，并使用针对不同种族的报告病例数据和空间细分的人口数量进行拟合。 我们发现，毛利人、太平洋人和亚裔的接触率分别是在欧洲人接触率的 1.17-2.46 倍、1.60-3.89 倍和 0.83-0.92 倍。 然后我们发现，在模型中考虑的参数中，传播率的差异解释了观察到的攻击率差异的大部分，而同质性和疫苗接种率则解释得较少。 显示英文摘要

摘要： During the COVID-19 pandemic, Aotearoa followed an elimination strategy followed by a mitigation strategy, which saw high success and kept health impact low. However, there were inequities in health outcomes, notably that M\=aori and Pacific Peoples had lower vaccine coverage and experienced higher age-standardised rates of hospitalisation and death. Models provide predictions of disease spread and the burden of disease, which can effectively inform policy, but are often not good at including inequities/heterogeneity. Despite the differences in health outcomes by ethnicity, most models have not explicitly considered ethnic heterogeneities as factors. We developed such a model to investigate the first Omicron wave of the COVID-19 pandemic in Aotearoa, which was the first time there was widespread community transmission of SARS-CoV-2. We analysed three models for contact patterns within and between ethnicities -- proportionate mixing, assortative mixing, and empirically derived mixing -- and fit them using ethnicity-specific data on reported cases and spatially disaggregated population counts. We found that M\=aori, Pacific, and Asian contact rates were between 1.17-2.46, 1.60-3.89, and 0.83-0.92 times the European rates, respectively. We then found that from the parameters considered in the model, the disparity in transmission rates explained the majority of the observed disparity in attack rates, while assortativity and vaccine rates explained comparably less.