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

doi:10.1057/s41287-020-00333-5

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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Abstract

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 language	English
Pages (from-to)	1476-1503
Number of pages	28
Journal	The European Journal of Development Research
Volume	32
Issue number	5
DOIs	https://doi.org/10.1057/s41287-020-00333-5
Publication status	Published - 15 Dec 2020

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1057/s41287-020-00333-5

Olsen2020_Article_HierarchicalModellingOfCOVID-1Final published version, 3.22 MBLicence: CC BY

Statistical Modelling
Onah, C. N., Shlomo, N., Chandola, T., Schoch, D., Cuitún Coronado, J., Aparicio-Castro, A., Thestrup, S., Olsen, W. K., Cernat, A., Shryane, N., Wisniowski, A., Smith, D., Aparicio-Castro, A., Shafie, T., Hannemann, T., Morales-Gómez, A., Mellon, J., Troncoso Ruiz, P., Taub, J., Murphy, J., Guest, E., Kim, J., Watson, J., Pashazadeh, F., Hoór, D., Jones, P. & Gosling, Z.
1/08/19 → 30/09/22
Project: Research

Cite this

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title = "Hierarchical Modelling of COVID-19 Death Risk in India in the Early Phase of the Pandemic",

abstract = "We improve upon the modelling of India{\textquoteright}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 {\textquoteleft}follow World Health Organisation advice{\textquoteright} 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.",

keywords = "Bayesian model, Data combining, Epidemic modelling, Hierarchical model, India, Latent variable, Pandemic, SARS-CoV2 virus pandemic, Severe COVID-19",

author = "Wendy Olsen and Manasi Bera and Amaresh Dubey and Jihye Kim and Arkadiusz Wi{\'s}niowski and Purva Yadav",

year = "2020",

month = dec,

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doi = "10.1057/s41287-020-00333-5",

language = "English",

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AU - Olsen, Wendy

AU - Bera, Manasi

AU - Dubey, Amaresh

AU - Kim, Jihye

AU - Wiśniowski, Arkadiusz

AU - Yadav, Purva

PY - 2020/12/15

Y1 - 2020/12/15

N2 - 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.

AB - 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.

KW - Bayesian model

KW - Data combining

KW - Epidemic modelling

KW - Hierarchical model

KW - India

KW - Latent variable

KW - Pandemic

KW - SARS-CoV2 virus pandemic

KW - Severe COVID-19

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ER -

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

Abstract

Keywords

UN SDGs

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Statistical Modelling

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