Search

When the COVID-19 pandemic struck and China reported the first case to the World Health Organization in December 2019, there was no evidence-based treatment to combat it. With the catastrophic situation that followed, materialised by a considerable number of deaths, researchers, doctors, traditional healers, and governments of all nations committed themselves to find therapeutic solutions, including preventive and curative. There are effective treatments offered both by modern medicine and traditional medicine for COVID-19 today. However, other therapeutic proposals have not been approved due to the lack of effectiveness and scientific rigour during their development process. Proponents of modern medicine prefer biomedical therapies while in some countries, traditional treatments are used regularly because of their availability, affordability and satisfaction they bring to the population. In this paper, we propose a transactional medicine approach where the interaction between traditional and modern medicine produces a change. With this approach, the promoters of traditional medicine and those of modern medicine will be able to acquire knowledge through the experience produced by their encounters. Transactional medicine aims to be a model for decolonising medicine and recognising the value of both traditional and modern medicine in the fight against COVID-19 and other global emerging pathogens. [ABSTRACT FROM AUTHOR]

In this paper, we trace how mathematical models are made 'evidence enough' and 'useful for policy'. Working with the interview accounts of mathematical modellers and other scientists engaged in the UK Covid-19 response, we focus on two weeks in March 2020 prior to the announcement of an unprecedented national lockdown. A key thread in our analysis is how pandemics are made 'big'. We follow the work of one particular device, that of modelled 'doubling-time'. By following how modelled doubling-time entangles in its assemblage of evidence-making, we draw attention to multiple actors, including beyond models and metrics, which affect how evidence is performed in relation to the scale of epidemic and its policy response. We draw attention to: policy; Government scientific advice infrastructure; time; uncertainty; and leaps of faith. The 'bigness' of the pandemic, and its evidencing, is situated in social and affective practices, in which uncertainty and dis-ease are inseparable from calculus. This materialises modelling in policy as an 'uncomfortable science'. We argue that situational fit in-the-moment is at least as important as empirical fit when attending to what models perform in policy. • Pandemics are performed as crises of uncertain yet catastrophic potential. • Mathematical models are forms of scalar narrative affording epidemics their scale. • Models and projections are made useful as evidence in their situated policy events. • Calculations are given agency as affects in the evidencing of pandemics. • Modelling pandemics is a site of 'uncomfortable science' and 'dis-ease'. [ABSTRACT FROM AUTHOR]