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Social learning about Covid-19 vulnerability and social distancing in high density populations: the case of UK urban dwelling Bangladeshis

Early epidemiology indicates older members of Britain’s Bangladeshi communities are disproportionately affected by Covid-19 related morbidity and mortality. Bangladeshis are more likely to have comorbidities and live in poorer, overcrowded areas in the UK’s urban centres where viral contagion is more likely. This cross-section of socioeconomic, geographical and health related factors underlines the need for clear messaging about social distancing in a complex and shifting risk scenario – messages that this vulnerable group, who speak an oral language (Sylheti), may not be able to access directly due to low literacy and English language proficiency. This study will identify the practices adopted by Bangladeshis in a London borough in response to the pandemic, the attitudes and beliefs that underlie them and whether and how these have been influenced by messages about social distancing. Drawing on our earlier work, it will examine the role of social learning in how messages are accessed and interpreted and whether and how the health interactions of this older group are mediated by friends, family members and acquaintances. Remote interviews with older Bangladeshis and their social contacts who perform this mediating role will provide insights into how linguistically and culturally appropriate messaging can build on existing beliefs and practices to promote compliance, and on social mediation as a dissemination strategy. We will identify the role of choice of language (English or Sylheti), the differences between written and oral representations of Covid-19 risk, and the manifold ways in which linguistic choices give salience to aspects of a risk scenario.

Lead investigator:

Chris Tang


King's College London

Primary topic:

Health, physical & mental

Secondary topic:

Inequality & poverty

Region of data collection:


Country of data collection


Status of data collection


Type of data being collected:


Unit of real-time data collection



Periodic (other)