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.
Highlights
In India, all persons experiencing symptoms of COVID-19 are required to attend at government-managed COVID-19 testing sites
Social group differentials in the COVID-19 mortality rate are found after all other factors were controlled for, mean posterior slope β = 3.8 for SC people and β = 1.79 for Scheduled Tribes (ST) people, i.e. the effect is more apparent among Scheduled Castes (SCs) than Schedules Tribes (STs) compared to all other groups
We applied the method in the case of 11 states of India
Summary
In India, all persons experiencing symptoms of COVID-19 are required to attend at government-managed COVID-19 testing sites. The state governments have the task of managing COVID-19 evidence, including individual cases of infection, and they record the COVID-19-related deaths. The national Ministry of Health and Family Welfare handles the collation of state evidence. It releases both summary data and public-health advice via webpages. Data summaries were provided at District level via third-party organisations not benchmarked by government (e.g. How India Lives 2020). Data for deaths in India are modelled by using a hierarchical model within an information-optimising Bayesian inferential framework. Such models combining aggregate and individual-level data are rare in the COVID-19 literature. Our model can open up a wide range of variant models
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