We improve upon the modelling of India’s pandemic vulnerability. Our model is multidisciplinary and recognises the nested levels of the epidemic. We create a model of the risk of severe COVID-19 and death, instead of a model of transmission. Our model allows for socio-demographic-group differentials in risk, obesity and underweight people, morbidity status and other conditioning regional and lifestyle factors. We build a hierarchical multilevel model of severe COVID-19 cases, using three different data sources: the National Family Health Survey for 2015/16, Census data for 2011 and data for COVID-19 deaths obtained cumulatively until June 2020. We provide results for 11 states of India, enabling best-yet targeting of policy actions. COVID-19 deaths in north and central India were higher in areas with older and overweight populations, and were more common among people with pre-existing health conditions, or who smoke, or who live in urban areas. Policy experts may both want to ‘follow World Health Organisation advice’ and yet also use disaggregated and spatially specific data to improve wellbeing outcomes during the pandemic. The future uses of our innovative data-combining model are numerous. Electronic supplementary material The online version of this article (10.1057/s41287-020-00333-5) contains supplementary material, which is available to authorized users.

中文翻译：

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印度大流行初期 COVID-19 死亡风险的分层建模

我们改进了印度流行病脆弱性的模型。我们的模型是多学科的，并承认流行病的嵌套层次。我们创建了一个严重的 COVID-19 和死亡风险模型，而不是传播模型。我们的模型允许在风险、肥胖和体重不足的人群、发病状况和其他条件性区域和生活方式因素方面存在社会人口群体差异。我们使用三个不同的数据源构建了严重 COVID-19 病例的分层多级模型：2015/16 年全国家庭健康调查、2011 年人口普查数据和截至 2020 年 6 月累积获得的 COVID-19 死亡数据。我们提供的结果为印度的 11 个州，实现了政策行动的最佳目标。在人口老龄化和超重的地区，印度北部和中部的 COVID-19 死亡人数较高，并且在已有健康状况、吸烟或居住在城市地区的人群中更为常见。政策专家可能既要“遵循世界卫生组织的建议”，又要使用分类和空间特定的数据来改善大流行期间的福祉结果。我们创新的数据组合模型的未来用途很多。电子补充材料 本文的在线版本 (10.1057/s41287-020-00333-5) 包含补充材料，授权用户可以使用。我们创新的数据组合模型的未来用途很多。电子补充材料 本文的在线版本 (10.1057/s41287-020-00333-5) 包含补充材料，授权用户可以使用。我们创新的数据组合模型的未来用途很多。电子补充材料 本文的在线版本 (10.1057/s41287-020-00333-5) 包含补充材料，授权用户可以使用。