Modelling COVID-19 With More Disaggregation and Less Nomothetic Parameterisation: UK and India Examples

Abstract 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.