The COVID-19 Pandemic in India: Data, Stories, and Myths

 

 The COVID-19 Pandemic in India: Data, Stories, and Myths

 by

Dr. Murad Banaji

(Mathematician, Middlesex University, London)

 

Date & Time : 22 nd July, 2021 Thursday at 3.00 PM (IST)

  

Recorded Video

 

 

About the speaker:

Dr. Murad Banaji is a mathematician at Middlesex University, London, who has been tracking India's COVID-19 epidemic using modelling and data analysis. Beginning with work on systems of coupled oscillators, he has worked on a variety of problems at the interface between dynamical systems, analysis and combinatorics, with applications to real-world systems with network structure, including in biology and chemistry. He has published extensively on the abstract (parameter free) theory of chemical reaction networks, on the theory of monotone dynamical systems, and on various themes in linear/multilinear algebra and combinatorics.

 

Abstract:

As the COVID-19 pandemic evolved in India, so did a multitude of narratives about it. Over time, scientific understanding and government claims diverged more and more dramatically. But it would too simple to pose a dichotomy between propaganda on the one hand, and science on the other. Scientists and others tracking India's epidemic often failed to acknowledge the limitations and context of available data, or to engage sufficiently with theory. 

In this talk, the speaker wants to explore how the epidemic itself evolved, and how stories about it evolved in parallel. Even as government discourse became more and more implausible, self-contradictory, and ultimately dangerous, the push-back against this discourse was muted, and largely confined to scientists, social scientists and journalists outside the mainstream. When the history of the Indian epidemic is written it will be important to recognise the role of poor data and weak transparency. But equally important will be to recognise the deeper weaknesses which allowed misleading stories about COVID-19 in India to become dominant, to such tragic effect.