Mathematical models as public troubles in COVID-19 infection control: following the numbers

Abstract

Mathematical models are key actors in policy and public responses to the COVID-19 pandemic. The projections from COVID-19 models travel beyond science into policy decisions and social life. Treating models as 'boundary objects', and focusing on media and public communications, we 'follow the numbers' to trace the social life of key projections from prominent mathematical models of COVID-19. Public deliberations and controversies about models and their projections are illuminating. These help trace how projections are 'made multiple' in their enactments as 'public troubles'. We need an approach to evidence-making for policy which is emergent and adaptive, and which treats science as an entangled effect of public concern made in social practices. We offer a rapid sociological response on the social life of science in the emerging COVID-19 pandemic to speculate on how evidence-making might be done differently going forwards.

Intelligent computing with Levenberg–Marquardt artificial neural networks for nonlinear system of COVID-19 epidemic model for future generation disease control

“…Nowadays, the dynamics of COVID-19 models have been growing interest in the research community and may mathematical models are designed for the better interest of people around the world, such as the model of eight classes based on susceptible, infected, diagnosed, ailing, recognized, threatened, healed and extinct (SIDARTHE) [6], five classes based on SEIAR represented with 5 number of ordinary differential equations [7], a new θ -SEIHRD model represented with nine classes [8], modified SEIRS model system with five classes [9], four class modified SIR model [10], SAIR system based COVID-19 model for complex networks [11]. Beside, these variety of COVID-19 model are introduced by the researchers [8,[12][13][14][15][16][17][18][19][20][21][22]. However, in the current scenarios, we have taken a complex 8 classes model based on Susceptible (S), exposed (E), symptomatic and infectious (I), super propagation (P), infection but asymptomatic (A), hospitalized (H), recovery (R) and fatality (F) classes, i.e., SEIPAHRF for numerical investigations [23].…”

Segmenting communities as public health strategy: a view from the social sciences and humanities

“…This might be done in such a way that creates a stronger connection between the open letter and emerging social science writing about modelling and the public health and policy response to the COVID-19 pandemic. [2] How does their critical analysis of the segmenting and shielding approach also contribute to broader social science critiques of the centrality of modelling within the response to the COVID-19 pandemic?…”

“…Van Bunnik et al (2020) Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown https://www.wiki.ed.ac.uk/display/Epigroup/COVID-19+project?pre view=/442891806/447360858/van%20Bunnik%20et%20al.%20SS%20manuscr ipt%20050520.pdf [accessed 11 May 2020]2 Sample & Mason (2020) UK could relax lockdown for millions if over-70s are shielded, scientists say, The Guardian 5 May https://www.theguardian. com/society/2020/may/05/longer-lockdown-for-over-70s-would-allow-fewerrestrictions-for-rest-of-uk-scientists-suggest3 While we note that the Scottish Government's 'Coronavirus (COVID-19): Framework for decision making' acknowledges some of these harmful assumptions, it falls short of directly addressing how it will address these inequalities which place increased restrictions (and burden) on those who are shielding.4 Sacareau, I (2007) Himalayan Hill Stations from the British Raj to Indian Tourism.…”

Health policy and controlling Covid-19 in England: sociological insights

“…Yet the media daily news briefings consistently involved both politician and medical/public health scientists and the foreign secretary (07/04/2020) referred to an evidence-based approach to shaping policies. This linear or rational approach has also been challenged for treating the evidence base, such as the mathematical models ( Ferguson et al ., 2020 ), which led to the shift in government policy, as rigid and as ‘boundary objects’ ( Rhodes & Lancaster, 2020 ) when they should be seen as more fluid and the social process of the development of the evidence base as more emergent and adaptive. One particular problem in this context is the lack of evidence and uncertainties about the transmission of the virus and its control.…”

“…The media briefings particularly used graphs portraying statistical trends in social distancing, infection and deaths, which aim to illustrate the scientific approach being taken and encouraging, at least some members of the public, to become ‘armchair’ epidemiologists ( Rhodes & Lancaster, 2020 ). The format of the briefing was managed by the government through the public service medium of the BBC, even though the latter had an uneasy relationship with the government prior to the pandemic.…”