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.

No abstract

We improve upon the modelling of India’s pandemic vulnerability. Our model is multi-disciplinary 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 2015/6, 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.

Modelling of pandemic vulnerability in a development context can be improved through combining disciplines, combining data, and recognising the many nested levels of the epidemic. Models of transmission have been constructed at national level or for multiple nations. We instead construct a model allowing for social-group differentials in risk, along with conditioning regional factors and lifestyle factors. Severe COVID-19 disease is our innovative key outcome. We use three data sources at once: National Family and Health Survey for India, Indian Census 2011, and COVID-19 deaths. We provide results for 11 states of India, enabling best-yet targeting of policy actions. The future uses of such models are many. COVID-19 deaths in north and central India were higher in areas with older populations and overweight populations, and was more common among those with pre-existing health conditions, or who smoke or 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.

No abstract