Hierarchical Modelling of COVID-19 Death Risk in India in the Early Phase of the Pandemic

Wendy Olsen, Manasi Bera, Amaresh Dubey, Jihye Kim, Arkadiusz Wiśniowski, Purva Yadav

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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.
Original languageEnglish
Pages (from-to)1476-1503
Number of pages28
JournalThe European Journal of Development Research
Issue number5
Publication statusPublished - 15 Dec 2020


  • Bayesian model
  • Data combining
  • Epidemic modelling
  • Hierarchical model
  • India
  • Latent variable
  • Pandemic
  • SARS-CoV2 virus pandemic
  • Severe COVID-19


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