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Introduction: Testing for active SARS-CoV-2 infection is a fundamental tool in the public health measures taken to control the COVID-19 pandemic. Because of the overwhelming use of SARS-CoV-2 reverse transcription (RT)-PCR tests worldwide, the availability of test kits has become a major bottleneck and the need to increase testing throughput is rising. We aim to overcome these challenges by pooling samples together, and performing RNA extraction and RT-PCR in pools. Methods: We tested the efficiency and sensitivity of pooling strategies for RNA extraction and RT-PCR detection of SARS-CoV-2. We tested 184 samples both individually and in pools to estimate the effects of pooling. We further implemented Dorfman pooling with a pool size of eight samples in large-scale clinical tests. Results: We demonstrated pooling strategies that increase testing throughput while maintaining high sensitivity. A comparison of 184 samples tested individually and in pools of eight samples showed that test results were not significantly affected. Implementing the eight-sample Dorfman pooling to test 26 576 samples from asymptomatic individuals, we identified 31 (0.12%) SARS-CoV-2 positive samples, achieving a 7.3-fold increase in throughput. Discussion: Pooling approaches for SARS-CoV-2 testing allow a drastic increase in throughput while maintaining clinical sensitivity. We report the successful large-scale pooled screening of asymptomatic

Information systems (IS) research has been long concerned with improving task-related performance. The concept of fit is often used to explain how system design can improve performance and overall value. So far, the literature has focused mainly on performance evaluation criteria that are based on measures of task efficiency, accuracy, or productivity. However, nowadays, productivity gain is no longer the single evaluation criterion. In many instances, computer systems are expected to enhance our creativity, reveal opportunities and open new vistas of uncharted frontiers.To address this void, we introduce the concept of generativity in the context of IS design and develop two corresponding design considerations -'generative capacity' that refers to one's ability to produce something ingenious or at least new in a particular context, and 'generative fit' that refers to the extent to which an IT artefact is conducive to evoking and enhancing that generative capacity. We offer an extended view of the concept of fit and realign the prevailing approaches to human-computer interaction design with current leading-edge applications and users' expectations. Our findings guide systems designers who aim to enhance creative work, unstructured syntheses, serendipitous discoveries, and any other form of computer-aided tasks that involve unexplored outcomes or aim to enhance our ability to go boldly where no one has gone before.In this paper, we explore the underpinnings of 'generative capacity' and argue that it should be included in the evaluation of task-related performance. Then, we briefly explore the role of fit in IS research, position 'generative fit' in that context, explain its role and impact on performance, and provide key design considerations that enhance generative fit. Finally, we demonstrate our thesis with an illustrative vignette of good generative fit, and conclude with ideas for further research.In this theory development paper, we offer an extended view of the concept of fit in an attempt to realign the prevailing approaches to human-computer interaction design with current leading edge applications and users' expectations. Specifically, we identify an emerging dimension of task performance relating to creativity and innovativeness and explore how information systems can be fitted to enhance this dimension.It has been argued that fitting the human-computer interface and, more generally, fitting computing services to a user and an underlying computer-aided task enhances the desired outcome and has a positive effect on overall performance (Vessey & Galletta, 1991;Baecker et al., 1995;Goodhue & Thompson, 1995;Zigurs & Buckland, 1998). Although one's performance is theorized in a generalized fashion as related to any kind of a priori objectives associated with an underlying task (Daft, 1991), the literature so far focuses mainly on performance evaluation criteria that are based on measures of task efficiency, accuracy or productivity (Zhang & Na, 2004).This view of performance was sufficient in the early days ...