In efforts to control disease, mathematical models and numerical targets play a key role. We take the elimination of a viral infection as a case for exploring mathematical models as ‘evidence-making interventions’. Using interviews with mathematical modellers and implementation scientists, and focusing on the emergence of models of ‘treatment-as-prevention’ in hepatitis C control, we trace how projections detach from their calculative origins as social and policy practices. Drawing on the work of Michel Callon and others, we show that modelled projections of viral elimination circulate as ‘qualculations’, taking flight via their affects, including as anticipation. Modelled numerical targets do not need ‘actual numbers’ or precise measurements to perform their authority as evidence of viral elimination or as situated matters-of-concern. Modellers grapple with the ways that their models transform in policy and social practices, apparently beyond reasonable calculus. We highlight how practices of ‘holding-on’ to projections in relation to imaginaries of ‘evidence-based’ science entangle with the ‘letting-go’ of models beyond calculus. We conclude that the ‘virtual precision’ of models affords them fluid evidence-making potential. We imagine a different mode of modelling science in health, one more attuned to treating projections as qualculative, affective and relational, as excitable matter.

中文翻译：

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令人兴奋的模型：预测、目标和无疾病未来的创造

在控制疾病的努力中，数学模型和数值目标发挥着关键作用。我们以消除病毒感染为例，探索数学模型作为“证据干预措施”。通过对数学建模者和实施科学家的采访，并重点关注丙型肝炎控制中“治疗即预防”模型的出现，我们追踪了预测如何脱离其作为社会和政策实践的计算起源。借鉴米歇尔·卡伦（Michel Callon）等人的工作，我们表明，病毒消除的模型预测作为“限定”而传播，通过其影响（包括预期）而逃跑。建模的数字目标不需要“实际数字”或精确的测量来发挥其作为病毒消除证据或相关问题的权威性。建模者努力解决他们的模型在政策和社会实践中的转变方式，这显然超出了合理的计算范围。我们强调“坚持”与“基于证据”的科学想象相关的预测的实践如何与“放弃”微积分之外的模型纠缠在一起。我们的结论是，模型的“虚拟精度”为它们提供了流动的证据制作潜力。我们设想了一种不同的健康建模科学模式，一种更适合将预测视为定性的、情感的和关系性的、可兴奋的物质。